Abstract

Interactions between a training server and a plurality of environment controllers are used for updating the weights of a predictive model used by a neural network executed by the plurality of environment controllers. Each environment controller executes the neural network using a current version of the predictive model to generate outputs based on inputs, modifies the outputs, and generates metrics representative of the effectiveness of the modified outputs for controlling the environment. The training server collects the inputs, the corresponding modified outputs, and the corresponding metrics from the plurality of environment controllers. The collected inputs, modified outputs and metrics are used by the training server for updating the weights of the current predictive model through reinforcement learning. A new predictive model comprising the updated weights is transmitted to the environment controllers to be used in place of the current predictive model.

IPC Classes  ?

• G06N 3/047 - Probabilistic or stochastic networks
• G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
• G06N 3/08 - Learning methods

Abstract

Method and environment controller for inferring via a neural network one or more commands for controlling an appliance. A predictive model generated by a neural network training engine is stored by the environment controller. The environment controller determines at least one room characteristic. The environment controller receives at least one environmental characteristic value and at least one set point. The environment controller executes a neural network inference engine, which uses the predictive model for inferring the one or more commands for controlling the appliance. The inference is based on the at least one environmental characteristic value, the at least one set point and the at least one room characteristic. The environment controller transmits the one or more commands to the controlled appliance.

IPC Classes  ?

• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• F24F 11/54 - Control or safety arrangements characterised by user interfaces or communication using one central controller connected to several sub-controllers
• G05D 22/02 - Control of humidity characterised by the use of electric means
• G05D 23/19 - Control of temperature characterised by the use of electric means
• G06N 3/02 - Neural networks
• G06N 5/04 - Inference or reasoning models
• H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]

Goods & Services

Computer hardware modules for use in electronic sensing devices for collecting, using and communicating data in the internet of things, for use in connection with energy management, building infrastructure management, space utilization, wayfinding, real time location services, asset tracking, spatial data analytics; computer software platform for use in collecting, using and communicating data in the internet of things, for use in connection with facilities and operations management, building infrastructure management, space utilization, wayfinding, real time location services, asset tracking, and spatial data analytics; computer software platform for use in asset optimization and visualization, management and optimization of facilities and operations; computer software platform for use in management of facilities and operations data; software for information management, data collection and data analysis in the fields of asset optimization and visualization, management and optimization of facilities and operations; computer software for lighting based indoor positioning, wayfinding and real time location services; computer software for collecting and distributing data within computer networks, including the internet, and enabling data communication among application programs, mobile devices, and computer hardware devices or modules; computer application software for smart phones, mobile phones, and handheld electronic communication devices, which is used for commissioning and connecting lighting fixtures, and controlling lighting sensors, locating articles, objects and persons and indicating location, namely, indoor positioning, wayfinding and real time location services; computer programs for controlling lighting sensors; computer programs for communication between end-users and vendors; electronic devices and electronic control apparatus and control devices, namely, electronic sensors which detect the presence of occupants; LED lighting systems, comprised of computer software and related hardware designed to send digital content to the cloud via software and mobile applications for the purpose of data processing, data aggregation and data storage; microprocessor-based interface electronic controllers; electronic devices and electronic lighting control apparatus used to locate articles, objects and persons; electronic devices and electronic lighting control apparatus devices used to indicate location, namely, indoor positioning, wayfinding and real time location services; data processing equipment, namely, data processors; wireless data network products, namely, wireless communication devices for data or images transmission; wireless lighting control apparatus to remotely control lighting sensors and for use as communication apparatus; wireless communication system comprised of mobile device-based receivers for LED light fixtures using visible light communication for data transmission; user interfaces, namely, touch screen user interfaces; temperature controllers, namely thermostats; electric controllers for fans; sensors for electronic devices and electronic control apparatus and control devices for shade and sunblind for windows or skylights; electric controllers for lighting devices; electric controllers for internet of things (IOT) enabled devices; connected system controllers; adapters for enabling wireless connectivity between devices; noise level meters; air quality measurement apparatus, namely, particle counters and volatile organic compounds counters; downloadable software for commission IOT devices, building mechanicals, sensors, switches, and controllers; electronic devices for detecting and reporting environment impact, namely, co2 emissions; wireless remote temperature and humidity monitors for building maintenance; devices for connecting controllers with other devices; adaptors for enabling wireless connectivity between devices. Electric lighting fixtures; LED lighting systems, namely, led modules, power supplies, and wiring. Platform as a service (PAAS) featuring computer software platforms for use in asset optimization and visualization, management and optimization of facilities, and operations and operations data; software as a service (SaaS) services featuring software for use in asset optimization and visualization, management and optimization of facilities, operations, and operations data; platform as a service (PAAS) services featuring computer software platforms for use in connection with the connection, data collection, databasing, visualization, management, and devices related to buildings and structures; software as a service (SaaS) services featuring software for use in connection with the connection, data collection, databasing, visualization, management, and devices related to buildings and structures; providing temporary use of on-line non-downloadable software for use in asset optimization and visualization, management and optimization of facilities and operations; providing temporary use of on-line non-downloadable software for use in connecting, operating, and managing lighting control systems and for providing location knowledge for use in operating and managing networked HVAC systems; computer services, namely, providing a website featuring technology that enables users to monitor and control, automate and manage facilities, operations utilities, building systems and devices related to buildings through the internet; providing advice in the installation, maintenance and repair of internet of things software systems; providing temporary use of on-line non-downloadable software and applications using artificial intelligence for use in building automation systems; providing non-downloadable software for commissioning internet of things (IOT) devices, building mechanicals.

Goods & Services

(1) Noise level meters; wireless remote temperature and humidity monitors for building maintenance (2) Computer hardware modules for use in electronic sensing devices for collecting, using and communicating data in the internet of things, for use in connection with energy management, building infrastructure management, space utilization, wayfinding, real time location services, asset tracking, spatial data analytics; computer software platform for use in collecting, using and communicating data in the internet of things, for use in connection with facilities and operations management, building infrastructure management, space utilization, wayfinding, real time location services, asset tracking, and spatial data analytics; computer software platform for use in asset optimization and visualization, management and optimization of facilities and operations; computer software platform for use in management of facilities and operations data; software for information management, data collection and data analysis in the fields of asset optimization and visualization, management and optimization of facilities and operations; computer software for lighting based indoor positioning, wayfinding and real time location services; computer software for collecting and distributing data within computer networks, including the internet, and enabling data communication among application programs, mobile devices, and computer hardware devices or modules; computer application software for smart phones, mobile phones, and handheld electronic communication devices, which is used for commissioning and connecting lighting fixtures, and controlling lighting sensors, locating articles, objects and persons and indicating location, namely, indoor positioning, wayfinding and real time location services; computer programs for controlling lighting sensors; computer programs for communication between end-users and vendors; electronic devices and electronic control apparatus and control devices, namely, electronic sensors which detect the presence of occupants; LED lighting systems, comprised of computer software and related hardware designed to send digital content to the cloud via software and mobile applications for the purpose of data processing, data aggregation and data storage; microprocessor-based interface electronic controllers; electronic devices and electronic lighting control apparatus used to locate articles, objects and persons; electronic devices and electronic lighting control apparatus devices used to indicate location, namely, indoor positioning, wayfinding and real time location services; data processing equipment, namely, data processors; wireless data network products, namely, wireless communication devices for data or images transmission; wireless lighting control apparatus to remotely control lighting sensors and for use as communication apparatus; wireless communication system comprised of mobile device-based receivers for LED light fixtures using visible light communication for data transmission; user interfaces, namely, touch screen user interfaces; temperature controllers, namely thermostats; electric controllers for fans; sensors for electronic devices and electronic control apparatus and control devices for shade and sunblind for windows or skylights; electric controllers for lighting devices; electric controllers for internet of things (IOT) enabled devices; connected system controllers; adapters for enabling wireless connectivity between devices; air quality measurement apparatus, namely, particle counters and volatile organic compounds; downloadable software for commission IOT devices, building mechanicals, sensors, switches, and controllers; electronic devices for detecting and reporting environment impact, namely, co2 emissions; devices for connecting controllers with other devices; adaptors for enabling wireless connectivity between devices (3) Electric lighting fixtures (4) LED lighting systems, namely, led modules, power supplies, and wiring (1) Platform as a service (PAAS) featuring computer software platforms for use in asset optimization and visualization, management and optimization of facilities, and operations and operations data; software as a service (SaaS) services featuring software for use in asset optimization and visualization, management and optimization of facilities, operations, and operations data; platform as a service (PAAS) services featuring computer software platforms for use in connection with the connection, data collection, databasing, visualization, management, and devices related to buildings and structures; software as a service (SaaS) services featuring software for use in connection with the connection, data collection, databasing, visualization, management, and devices related to buildings and structures; providing temporary use of on-line non-downloadable software for use in asset optimization and visualization, management and optimization of facilities and operations; providing temporary use of on-line non-downloadable software for use in connecting, operating, and managing lighting control systems and for providing location knowledge for use in operating and managing networked HVAC systems; computer services, namely, providing a website featuring technology that enables users to monitor and control, automate and manage facilities, operations utilities, building systems and devices related to buildings through the internet; providing advice in the installation, maintenance and repair of internet of things software systems; providing temporary use of on-line non-downloadable software and applications using artificial intelligence for use in building automation systems; non-downloadable software for commissioning internet of things (IOT) devices, building mechanicals

Goods & Services

Computer hardware modules for use in electronic sensing devices for collecting, using and communicating data in the internet of things, for use in connection with energy management, building infrastructure management, space utilization, and spatial data analytics; downloadable computer software for collecting and distributing data within computer networks, including the internet, and for enabling data communication among application programs, mobile devices, and computer hardware devices and modules; downloadable computer programs for controlling lighting sensors; electronic devices and electronic control apparatus and control devices, namely, electronic sensors which detect the presence of occupants; microprocessor-based interface electronic controllers for sensing presence and occupancy levels, light intensity, air quality, sound levels, temperature, humidity, volatile organic compounds and carbon dioxide; data processing equipment, namely, data processors; wireless data network products, namely, wireless communication devices for data or images transmission; user interfaces, namely, touch screen computer user interfaces; temperature controllers, namely thermostats; electric controllers for fans; sensors for electronic devices and electronic control apparatus for sensing presence and occupancy levels, light intensity, temperature, air quality, sound levels, temperature, humidity, air quality, volatile organic compounds, and carbon dioxide; and electronic control devices for shade and sunblind for windows and or skylights; electric controllers for lighting devices; electric controllers for internet of things (IOT) enabled devices; computer network adapters for enabling wireless connectivity between devices; downloadable software for controlling commission IOT devices, building mechanical systems mechanicals, sensors, switches, and controllers; electronic devices for detecting and reporting environment impact, namely, electronic devices in the nature of co2 emissions detectors; wireless remote temperature and humidity monitors for building maintenance.

Abstract

Inference server and environment controller for inferring one or more commands for controlling an appliance. The environment controller receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy) and at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level); and forwards them to the inference server. The inference server executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring the one or more commands based on the received at least one environmental characteristic value and the received at least one set point; and transmits the one or more commands to the environment controller. The environment controller forwards the one or more commands to the controlled appliance.

IPC Classes  ?

• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
• G05B 17/02 - Systems involving the use of models or simulators of said systems electric
• G06N 3/084 - Backpropagation, e.g. using gradient descent
• F24F 11/76 - Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity by means responsive to temperature, e.g. bimetal springs
• F24F 11/30 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
• F24F 11/62 - Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• G06N 5/04 - Inference or reasoning models
• G06N 3/08 - Learning methods

Abstract

Method and environment controller for inferring via a neural network one or more commands for controlling an appliance. A predictive model generated by a neural network training engine is stored by the environment controller. The environment controller receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy). The environment controller receives at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level). The environment controller executes a neural network inference engine, which uses the predictive model for inferring the one or more commands for controlling the appliance based on the at least one environmental characteristic value and the at least one set point. The environment controller transmits the one or more commands to the controlled appliance.

IPC Classes  ?

• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
• G05B 17/02 - Systems involving the use of models or simulators of said systems electric
• G06N 5/04 - Inference or reasoning models
• G01N 33/00 - Investigating or analysing materials by specific methods not covered by groups
• F24F 11/30 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
• F24F 11/62 - Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• F24F 11/76 - Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity by means responsive to temperature, e.g. bimetal springs
• G06N 3/084 - Backpropagation, e.g. using gradient descent
• G06F 9/451 - Execution arrangements for user interfaces
• G01S 17/04 - Systems determining the presence of a target

Goods & Services

Computer hardware modules for facilities equipment and system control, for use in connection with energy management and building mechanical infrastructure management; computer software platform for use in asset optimization and visualization, management and optimization of facilities and operations; computer software platform for use in management of facilities and operations data; software for information management, data collection and data analysis in the fields of asset optimization and visualization, management and optimization of facilities and operations; computer software for lighting based indoor positioning, wayfinding and real time location services; microprocessorbased interface electronic controllers; electronic devices and electronic lighting control apparatus used to locate articles, objects and persons; electronic devices and electronic lighting control apparatus devices used to indicate location, namely, indoor positioning, wayfinding and real time location services; user interfaces, namely, touch screen user interfaces; temperature controllers, namely thermostats; electric controllers for fans; shade and sunblind controllers for windows or skylights; electric controllers for internet of things (IoT) enabled devices; connected system controllers; adapters for enabling wireless connectivity between devices; downloadable software for commission IoT devices, building mechanicals, sensors, switches, and controllers; electronic devices for detecting and reporting environment impact, namely, CO2 emissions; wireless remote temperature and humidity monitors for building maintenance; devices for connecting controllers with other devices; the preceding goods not in relation to lighting fixtures and not in relation to architectural illumination; none of the aforesaid for use in relation to oil and gas exploration and production. Platform as a service (PAAS) featuring computer software platforms for use in asset optimization and visualization, management and optimization of facilities, and operations and operations data; software as a service (SaaS) services featuring software for use in asset optimization and visualization, management and optimization of facilities, operations, and operations data; platform as a service (PaaS) services featuring computer software platforms for use in connection with the connection, data collection, databasing, visualization, management, and devices related to buildings and structures; software as a service (SAAS) services featuring software for use in connection with the connection, data collection, databasing, visualization, management, and devices related to buildings and structures; providing temporary use of on-line non-downloadable software for use in asset optimization and visualization, management and optimization of facilities and operations; providing temporary use of on-line non-downloadable software for use in connecting, operating, and managing lighting control systems and for providing location knowledge for use in operating and managing networked HVAC systems; computer services, namely, providing a website featuring technology that enables users to monitor and control, automate and manage facilities, operations utilities, building systems and devices related to buildings through the internet; providing advice in the installation, maintenance and repair of Internet of things software systems; providing temporary use of on-line non-downloadable software and applications using artificial intelligence for use in building automation systems; nondownloadable software for commissioning internet of things (IoT) devices, building mechanicals; the preceding services not in relation to lighting fixtures and not in relation to architectural illumination; none of the aforesaid for use in relation to oil and gas exploration and production.

Abstract

A computing device stores a predictive model generated by a neural network training engine. The computing device receives first and second two-dimensional (2D) thermal images comprising temperature measurements from respective first and second infrared (IR) sensors. The first and second images have the same size. An image capturing visual field of the second IR sensor partially overlaps with an image capturing visual field of the first IR sensor. The computing device executes a neural network using a predictive model for generating outputs based on inputs. The inputs comprise the temperature measurements of the first and second images. The outputs comprise horizontal and vertical shifts defining a translation of the second image with respect to the first image. An overlapping area in the first image, having a rectangular shape and overlapping with the second image, is determined using the horizontal and vertical shifts.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 5/04 - Inference or reasoning models
• G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
• H04N 5/33 - Transforming infrared radiation

Abstract

A computing device stores a predictive model generated by a neural network training engine. The computing device receives first and second two- dimensional (2D) thermal images comprising temperature measurements from respective first and second infrared (IR) sensors. The first and second images have the same size. An image capturing visual field of the second IR sensor partially overlaps with an image capturing visual field of the first IR sensor. The computing device executes a neural network using a predictive model for generating outputs based on inputs. The inputs comprise the temperature measurements of the first and second images. The outputs comprise horizontal and vertical shifts defining a translation of the second image with respect to the first image. An overlapping area in the first image, having a rectangular shape and overlapping with the second image, is determined using the horizontal and vertical shifts.

IPC Classes  ?

• G06T 7/00 - Image analysis

Abstract

Method and computing device using a neural network to determine whether or not to process images of an image flow. A predictive model of the neural network is generated and stored at a computing device. The computing device receives (b) an image of the image flow and executes (c) the neural network, using the predictive model for generating an indication of whether or not to process the image based on input(s) of the neural network, the input(s) comprising the image. The computing device determines (d) whether or not to process the image by an image processing module, based on the indication of whether or not to process the image. The image is processed by the image processing module if the determination is positive and not processed if the determination is negative. Steps (b), (c), (d) are repeated for consecutive images of the image flow.

IPC Classes  ?

• G06V 20/52 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects
• G06N 3/08 - Learning methods
• G06F 18/211 - Selection of the most significant subset of features
• G06T 7/20 - Analysis of motion
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06V 10/147 - Optical characteristics of the device performing the acquisition or on the illumination arrangements - Details of sensors, e.g. sensor lenses
• G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
• G06F 18/214 - Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• G06V 10/774 - Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Abstract

Method and computing device using a neural network to analyze temperature measurements of an infrared sensor. A predictive model of the neural network is stored by the computing device. The computing device receives a two-dimensional (2D) matrix of temperature measurements generated by the IR sensor, and executes the neural network using the predictive model for generating outputs based on inputs. The inputs comprise the 2D matrix of temperature measurements. The outputs comprise a 2D matrix of inferred temperatures. The computing device determines a subset of values of the 2D matrix of temperature measurements, and applies a comparison algorithm to the subset of values of the 2D matrix of temperature measurements and a corresponding subset of values of the 2D matrix of inferred temperatures, to detect an anomaly in the subset of values of the 2D matrix of temperature measurements. A method for training the neural network is also provided.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
• A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons

Abstract

Method and computing device using a neural network to analyze temperature measurements of an infrared sensor. A predictive model of the neural network is stored by the computing device. The computing device receives a two- dimensional (2D) matrix of temperature measurements generated by the IR sensor, and executes the neural network using the predictive model for generating outputs based on inputs. The inputs comprise the 2D matrix of temperature measurements. The outputs comprise a 2D matrix of inferred temperatures. The computing device determines a subset of values of the 2D matrix of temperature measurements, and applies a comparison algorithm to the subset of values of the 2D matrix of temperature measurements and a corresponding subset of values of the 2D matrix of inferred temperatures, to detect an anomaly in the subset of values of the 2D matrix of temperature measurements. A method for training the neural network is also provided.

IPC Classes  ?

• G01J 5/48 - Thermography; Techniques using wholly visual means
• G01J 5/80 - Calibration
• A61B 5/01 - Measuring temperature of body parts

Abstract

Remote control device and method for controlling interactions between the remote control device and a controlled appliance. The remote control device comprises a BLE interface and a battery for powering the BLE interface. Upon determination of a first condition being met, the remote control device sets the BLE interface in a standby mode where the power supplied by the battery to the BLE interface is limited to a minimal value. Upon determination of a second condition being met, the remote control device transmits one or more BLE advertising signal via the BLE interface. The remote control device receives a connection request from a controlled appliance via the BLE interface, establishes a connection between the remote control device and the controlled appliance through the BLE interface, and exchanges data with the controlled appliance via the BLE communication interface (e.g. transmission of a command for an actuator of the controlled appliance).

IPC Classes  ?

• G08C 17/02 - Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
• H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication

Abstract

Method and computing device using a neural network to bypass calibration data of an infrared sensor. A predictive model generated by a neural network training engine is stored by the computing device. The computing device determines a two-dimensional (2D) matrix of raw sensor data. Each raw sensor datum is representative of heat energy collected by the infrared sensor. The computing device executes a neural network inference engine. The neural network inference engine implements the neural network using the predictive model for generating outputs based on inputs. The inputs comprise the 2D matrix of raw sensor data. The outputs comprise a 2D matrix of inferred temperatures. A method for training a neural network to bypass calibration data of an infrared sensor is also provided.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 3/04 - Architecture, e.g. interconnection topology

Abstract

Method and computing device using a neural network to bypass calibration data of an infrared sensor. A predictive model generated by a neural network training engine is stored by the computing device. The computing device determines a two-dimensional (2D) matrix of raw sensor data. Each raw sensor datum is representative of heat energy collected by the infrared sensor. The computing device executes a neural network inference engine. The neural network inference engine implements the neural network using the predictive model for generating outputs based on inputs. The inputs comprise the 2D matrix of raw sensor data. The outputs comprise a 2D matrix of inferred temperatures. A method for training a neural network to bypass calibration data of an infrared sensor is also provided.

IPC Classes  ?

• G06N 3/02 - Neural networks

Abstract

Controlled appliance and method for controlling interactions between the controlled appliance and a remote control device. The controlled appliance comprises a BLE interface and an actuation module. The controlled appliance sets the BLE interface in a scanning mode where the BLE interface is capable of receiving BLE signals from other devices. The controlled appliance receives, via the BLE interface, a BLE advertising signal from the remote control device. The controlled appliance establishes a connection between the controlled appliance and the remote control device through the BLE interface. The controlled appliance exchanges data with the remote control device via the BLE interface. Upon reception from the remote control device via the BLE interface of a command for controlling operations of the actuation module, the controlled appliance applies the command to the actuation module.

IPC Classes  ?

• G05B 15/02 - Systems controlled by a computer electric
• F24F 11/30 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
• F24F 11/58 - Remote control using Internet communication
• H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
• H04W 8/00 - Network data management
• F24F 130/20 - Sunlight
• F24F 130/30 - Artificial light
• H04W 84/18 - Self-organising networks, e.g. ad hoc networks or sensor networks

Abstract

Device and method using a neural network to detect and compensate an air vacuum effect. The device stores a predictive model comprising weights of a neural network. The device receives an area temperature measurement (representative of a temperature of an area where the device is located) from a temperature sensing module of the device. The device determines at least one other measurement related to the device. The device executes a neural network inference engine implementing a neural network, using the predictive model for inferring output(s) based on inputs. The inputs comprise the area temperature measurement and the at least one other measurement related to the device. The output(s) comprises a metric representative of an air vacuum effect in the device. The device determines if an adjustment of the area temperature measurement needs to be performed based on the metric representative of the air vacuum effect in the device.

IPC Classes  ?

• F24F 11/50 - Control or safety arrangements characterised by user interfaces or communication
• G06N 3/02 - Neural networks

Abstract

Method and computing device for inferring a predicted state of a communication channel. The computing device stores a predictive model generated by a neural network training engine. The computing device collects a plurality of data samples representative of operating conditions of the communication channel. The communication channel is associated to a communication interface of the computing device. The communication interface allows an exchange of data between the computing device and at least one remote computing device over the communication channel. Each data sample comprises a measure of the amount of data respectively transmitted and received by the communication interface over the communication channel and a connection status of the communication channel, during a period of time. The computing device further executes a neural network inference engine using the predictive model for inferring the predicted state of the communication channel based on the plurality of data samples.

IPC Classes  ?

• H04W 24/10 - Scheduling measurement reports
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/08 - Learning methods
• G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
• H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]

Abstract

The present disclosure relates to an environment control system for controlling environmental conditions in a building. The environment control system comprises a plurality of sensors located in different areas of the building. Each sensor is used for determining a measured value for one of the environmental conditions in the area where the sensor is located. Further at least one of the plurality of sensors is configured for exchanging data with at least one mobile computing device for modifying a target value of one of the environmental conditions for the area where the sensor is located. The environment control system further comprises an environment controller for receiving the measured values and the modified target values from the plurality of sensors. The environment controller is further configured for comparing the measured values with the modified target values for each area of the building and generating commands for each area of the building based on a difference between the measured values and modified target values. The environment control system further comprises a plurality of room controllers, such that each room controller is installed in a room in one of the areas of the building. Each room controller is configured for exchanging data with the environment controller to obtain the measured values of the environmental conditions in the room.

IPC Classes  ?

• H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
• G05B 15/02 - Systems controlled by a computer electric
• H04W 4/33 - Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings

Abstract

Environment controller and method for controlling an environmental characteristic in an area of a building based on concurrent BLE interactions. The environment controller receives a plurality of concurrent environmental characteristic target values originating from a plurality of BLE enabled user devices. At least one of the plurality of concurrent environmental characteristic target values is forwarded from one of the BLE enabled user devices to the environment controller via a BLE enabled proxy device. At least one of the plurality of concurrent environmental characteristic target values is received by the environment controller directly from one of the BLE enabled user devices. The environment controller applies an algorithm to generate one or more command for controlling at least one controlled appliance based on the plurality of concurrent environmental characteristic target values. Examples of concurrent environmental characteristic target values include target temperatures, target humidity levels, target CO2 levels, target lightning levels, etc.

IPC Classes  ?

• H04W 84/18 - Self-organising networks, e.g. ad hoc networks or sensor networks
• H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
• G16Y 40/35 - Management of things, i.e. controlling in accordance with a policy or in order to achieve specified objectives
• G16Y 40/10 - Detection; Monitoring
• G16Y 30/00 - IoT infrastructure

Abstract

Computing device and method for inferring a predicted number of data chunks writable on a flash memory before the flash memory wears out. The computing device stores a predictive model generated by a neural network training engine. A processing unit of the computing device executes a neural network inference engine, using the predictive model for inferring the predicted number of data chunks writable on the flash memory before the flash memory wears out based on inputs. The inputs comprise a total number of physical blocks previously erased from the flash memory, a size of the data chunk, and optionally an operating temperature of the flash memory. In a particular aspect, the flash memory is comprised in the computing device, and an action may be taken for preserving a lifespan of the flash memory based at least on the predicted number of data chunks writable on the flash memory.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 7/00 - Computing arrangements based on specific mathematical models
• G11C 16/34 - Determination of programming status, e.g. threshold voltage, overprogramming or underprogramming, retention

Abstract

Computing device and method using a neural network to adjust temperature measurements. The computing device comprises a temperature sensing module, one or more processor and a display. The neural network receives as inputs a plurality of consecutive temperature measurements performed by the temperature sensing module, a plurality of consecutive utilization metrics of the one or more processor, and a plurality of consecutive utilization metrics of the display. The neural network outputs an inferred temperature, which is an adjustment of the temperature measured by the temperature sensing module to take into consideration heat dissipated by the one or more processor and the display when using the temperature sensing module for measuring the temperature in an area where the computing device is deployed. An example of computing device is a smart thermostat. A corresponding method for training a neural network to adjust temperature measurements is also disclosed.

IPC Classes  ?

• G05D 23/19 - Control of temperature characterised by the use of electric means
• G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators

Abstract

Method and environment controller for inferring via a neural network one or more commands for controlling an appliance. A predictive model generated by a neural network training engine is stored by the environment controller. The environment controller determines at least one room characteristic. The environment controller receives at least one environmental characteristic value and at least one set point. The environment controller executes a neural network inference engine, which uses the predictive model for inferring the one or more commands for controlling the appliance. The inference is based on the at least one environmental characteristic value, the at least one set point and the at least one room characteristic. The environment controller transmits the one or more commands to the controlled appliance.

IPC Classes  ?

• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
• F24F 11/54 - Control or safety arrangements characterised by user interfaces or communication using one central controller connected to several sub-controllers
• G06N 3/02 - Neural networks
• G05D 22/02 - Control of humidity characterised by the use of electric means
• G05D 23/19 - Control of temperature characterised by the use of electric means
• G06N 5/04 - Inference or reasoning models

Abstract

Inference server and environment controller for inferring via a neural network one or more commands for controlling an appliance. The environment controller determines at least one room characteristic. The environment controller receives at least one environmental characteristic value and at least one set point. The environment controller transmits the at least one environmental characteristic, set point and room characteristic to the inference server. The inference server executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring the one or more commands for controlling the appliance. The inference is based on the received at least one environmental characteristic value, at least one set point and at least one room characteristic. The inference server transmits the one or more commands to the environment controller, which forwards the one or more commands to the controlled appliance.

IPC Classes  ?

• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
• G06N 5/04 - Inference or reasoning models
• G05D 23/19 - Control of temperature characterised by the use of electric means
• G05B 17/02 - Systems involving the use of models or simulators of said systems electric
• G01N 33/00 - Investigating or analysing materials by specific methods not covered by groups
• F24F 11/30 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
• F24F 11/62 - Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• F24F 11/76 - Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity by means responsive to temperature, e.g. bimetal springs
• G06N 3/084 - Backpropagation, e.g. using gradient descent
• G06F 9/451 - Execution arrangements for user interfaces
• G06F 9/455 - Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
• G01S 17/04 - Systems determining the presence of a target
• C05B 17/02 - Other phosphatic fertilisers, e.g. soft rock phosphates, bone meal containing manganese
• F24F 120/10 - Occupancy

Abstract

Interactions between a training server and a plurality of environment controllers are used for updating the weights of a predictive model used by a neural network executed by the plurality of environment controllers. Each environment controller executes the neural network using a current version of the predictive model to generate outputs based on inputs, modifies the outputs, and generates metrics representative of the effectiveness of the modified outputs for controlling the environment. The training server collects the inputs, the corresponding modified outputs, and the corresponding metrics from the plurality of environment controllers. The collected inputs, modified outputs and metrics are used by the training server for updating the weights of the current predictive model through reinforcement learning. A new predictive model comprising the updated weights is transmitted to the environment controllers to be used in place of the current predictive model.

IPC Classes  ?

• G06N 3/08 - Learning methods
• F24F 11/63 - Electronic processing
• F24F 11/84 - Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers using valves
• F24F 11/86 - Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling compressors within refrigeration or heat pump circuits
• F24F 11/74 - Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
• G06N 5/04 - Inference or reasoning models
• F24F 110/20 - Humidity
• F24F 140/10 - Pressure
• F24F 110/10 - Temperature

Abstract

Interactions between a training server and a plurality of environment controllers are used for updating the weights of a predictive model used by a neural network executed by the plurality of environment controllers. Each environment controller executes the neural network using a current version of the predictive model to generate outputs based on inputs, modifies the outputs, and generates metrics representative of the effectiveness of the modified outputs for controlling the environment. The training server collects the inputs, the corresponding modified outputs, and the corresponding metrics from the plurality of environment controllers. The collected inputs, modified outputs and metrics are used by the training server for updating the weights of the current predictive model through reinforcement learning. A new predictive model comprising the updated weights is transmitted to the environment controllers to be used in place of the current predictive model.

IPC Classes  ?

• G06N 3/047 - Probabilistic or stochastic networks
• G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
• G06N 3/08 - Learning methods

Abstract

Interactions between a training server and a plurality of environment controllers are used for updating the weights of a predictive model used by a neural network executed by the plurality of environment controllers. Each environment controller executes the neural network using a current version of the predictive model to generate outputs based on inputs, modifies the outputs, and generates metrics representative of the effectiveness of the modified outputs for controlling the environment. The training server collects the inputs, the corresponding modified outputs, and the corresponding metrics from the plurality of environment controllers. The collected inputs, modified outputs and metrics are used by the training server for updating the weights of the current predictive model through reinforcement learning. A new predictive model comprising the updated weights is transmitted to the environment controllers to be used in place of the current predictive model.

IPC Classes  ?

• F24F 11/50 - Control or safety arrangements characterised by user interfaces or communication
• F24F 11/56 - Remote control
• F24F 11/62 - Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors

Abstract

Methods and environment controller for validating an estimated number of persons present in an area. The controller determines a temperature measurement in the area, a carbon dioxide (CO2) level measurement in the area, a humidity level measurement in the area, and the estimated number of persons present in the area based on data generated by an occupancy sensor. The controller executes a neural network inference engine for generating outputs based on inputs, using a predictive model comprising weights of a neural network. The inputs include the temperature, CO2 level and humidity level measurements, and the estimated number of persons. The outputs include an inferred temperature, an inferred CO2 level, an inferred humidity level, and an inferred number of persons. The controller applies a validation algorithm to the inputs and outputs of the neural network inference engine, to determine if the estimated number of persons is accurate or not.

IPC Classes  ?

• G01D 21/00 - Measuring or testing not otherwise provided for
• F24F 11/62 - Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• G01D 21/02 - Measuring two or more variables by means not covered by a single other subclass
• G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
• G06N 3/02 - Neural networks

Abstract

Inference server and environment controller for inferring one or more commands for controlling an appliance. The environment controller receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy) and at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level); and forwards them to the inference server. The inference server executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring the one or more commands based on the received at least one environmental characteristic value and the received at least one set point; and transmits the one or more commands to the environment controller. The environment controller forwards the one or more commands to the controlled appliance.

IPC Classes  ?

• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
• G05B 17/02 - Systems involving the use of models or simulators of said systems electric
• G06N 3/08 - Learning methods
• F24F 11/76 - Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity by means responsive to temperature, e.g. bimetal springs
• F24F 11/30 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
• F24F 11/62 - Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• G06N 5/04 - Inference or reasoning models
• F24F 120/10 - Occupancy
• F24F 110/20 - Humidity
• F24F 110/70 - Carbon dioxide
• F24F 110/10 - Temperature

Abstract

Method and environment controller for inferring via a neural network one or more commands for controlling an appliance. A predictive model generated by a neural network training engine is stored by the environment controller. The environment controller receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy). The environment controller receives at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level). The environment controller executes a neural network inference engine, which uses the predictive model for inferring the one or more commands for controlling the appliance based on the at least one environmental characteristic value and the at least one set point. The environment controller transmits the one or more commands to the controlled appliance.

IPC Classes  ?

• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
• G05B 17/02 - Systems involving the use of models or simulators of said systems electric
• G06N 5/04 - Inference or reasoning models
• G01N 33/00 - Investigating or analysing materials by specific methods not covered by groups
• F24F 11/30 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
• F24F 11/62 - Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• F24F 11/76 - Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity by means responsive to temperature, e.g. bimetal springs
• G06N 3/08 - Learning methods
• G06F 9/451 - Execution arrangements for user interfaces
• G01S 17/04 - Systems determining the presence of a target

Abstract

A service is stored in a non-volatile memory of a computing device and comprises instructions executable by a processor of the computing device. The processor generates an operational instance of the service, which comprises a reference to the service. The processor stores the operational instance of the service in the non-volatile memory with a read-write access right. The processor launches an executable instance of the service associated to the operational instance of the service. The launching comprises copying the instructions of the service from the non-volatile memory to a volatile memory of the computing device. The launching further comprises executing the instructions of the service copied into the volatile memory. The processor adds data generated by the execution of the instructions of the service to the operational instance of the service for permanent storage in the non-volatile memory.

IPC Classes  ?

• G06F 9/445 - Program loading or initiating
• G06F 11/07 - Responding to the occurrence of a fault, e.g. fault tolerance
• G05B 15/02 - Systems controlled by a computer electric

Abstract

Method providing resilient execution of a service on a computing device. The service is stored in a non-volatile memory of the computing device and comprises instructions executable by a processor of the computing device. The processor generates an operational instance of the service, which comprises a reference to the service. The processor stores the operational instance of the service in the non-volatile memory with a read-write access right. The processor launches an executable instance of the service associated to the operational instance of the service. The launching comprises copying the instructions of the service from the non-volatile memory to a volatile memory of the computing device. The launching further comprises executing the instructions of the service copied into the volatile memory. The processor adds data generated by the execution of the instructions of the service to the operational instance of the service for permanent storage in the non-volatile memory.

IPC Classes  ?

• G06F 3/06 - Digital input from, or digital output to, record carriers

Abstract

Controlled appliance and method for controlling interactions between the controlled appliance and a remote control device. The controlled appliance comprises a BLE interface and an actuation module. The controlled appliance sets the BLE interface in a scanning mode where the BLE interface is capable of receiving BLE signals from other devices. The controlled appliance receives, via the BLE interface, a BLE advertising signal from the remote control device. The controlled appliance establishes a connection between the controlled appliance and the remote control device through the BLE interface. The controlled appliance exchanges data with the remote control device via the BLE interface. Upon reception from the remote control device via the BLE interface of a command for controlling operations of the actuation module, the controlled appliance applies the command to the actuation module.

IPC Classes  ?

• G05B 15/02 - Systems controlled by a computer electric
• H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
• H04W 8/00 - Network data management
• F24F 11/30 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
• F24F 11/58 - Remote control using Internet communication
• F24F 130/20 - Sunlight
• F24F 130/30 - Artificial light
• H04W 84/18 - Self-organising networks, e.g. ad hoc networks or sensor networks

Abstract

Remote control device and method for controlling interactions between the remote control device and a controlled appliance. The remote control device comprises a BLE interface and a battery for powering the BLE interface. Upon determination of a first condition being met, the remote control device sets the BLE interface in a standby mode where the power supplied by the battery to the BLE interface is limited to a minimal value. Upon determination of a second condition being met, the remote control device transmits one or more BLE advertising signal via the BLE interface. The remote control device receives a connection request from a controlled appliance via the BLE interface, establishes a connection between the remote control device and the controlled appliance through the BLE interface, and exchanges data with the controlled appliance via the BLE communication interface (e.g. transmission of a command for an actuator of the controlled appliance).

IPC Classes  ?

• G08C 17/02 - Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
• H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication

Abstract

Computing device and method for inferring via a neural network a two-dimensional temperature mapping of an area. A predictive model is stored by the computing device. The computing device receives a plurality of temperature measurements transmitted by a corresponding plurality of temperature sensors located at a corresponding plurality of locations on a periphery of the area. The computing device executes a neural network inference engine, using the predictive model for inferring outputs based on inputs. The inputs comprise the plurality of temperature measurements. The outputs consist of a plurality of temperature values at a corresponding plurality of zones, the plurality of zones being comprised in a two-dimensional grid mapped on a plane within the area. For instance, the area is a room of a building, the periphery is an interface of a ceiling and walls of the room, and the plane is a horizontal plane within the room.

IPC Classes  ?

• F24F 11/63 - Electronic processing
• F24F 11/50 - Control or safety arrangements characterised by user interfaces or communication
• G06N 3/08 - Learning methods
• G06N 5/04 - Inference or reasoning models
• F24F 110/10 - Temperature

Abstract

Inference server and computing device for inferring an optimal wireless data transfer rate. The computing device determines parameters of a data transfer through a wireless communication interface of the computing device, and transmits the parameters of the data transfer to the inference server. The inference server receives the parameters of the data transfer, executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring an optimal data transfer rate based on the parameters of the data transfer, and transmits the optimal data transfer rate to the computing device. The computing device receives the optimal data transfer rate, and configures its wireless communication interface to operate at the optimal data transfer rate. For example, the computing device consists of an environment control device (e.g. an environment controller, a sensor, a controlled appliance, and a relay).

IPC Classes  ?

• H04L 12/825 - Adaptive control, at the source or intermediate nodes, upon congestion feedback, e.g. X-on X-off
• H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
• H04L 12/801 - Flow control or congestion control
• G05B 17/02 - Systems involving the use of models or simulators of said systems electric
• H04W 28/10 - Flow control
• G06N 3/00 - Computing arrangements based on biological models
• H04L 47/25 - Flow control; Congestion control with rate being modified by the source upon detecting a change of network conditions
• H04L 67/025 - Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
• H04W 28/02 - Traffic management, e.g. flow control or congestion control
• H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

Method and environment controller for improving predictive models used for controlling a temperature in an area. The environment controller executes a neural network inference engine using first and second predictive models for respectively inferring temperature increase and decrease values based on environmental inputs. The environment controller calculates a temperature adjustment value based on the temperature increase and decrease values, and the temperature in the area is adjusted based on the temperature adjustment value. The environment controller receives a vote related to the temperature in the area transmitted by a user device. The environment controller determines, based on the received vote, values of a first and second reinforcement signals. The environment controller executes a neural network training engine to update the first and second predictive models based on the inputs, respectively the temperature increase and decrease values, and respectively the values of the first and second reinforcement signals.

IPC Classes  ?

• F24F 11/63 - Electronic processing
• F24F 11/52 - Indication arrangements, e.g. displays

Abstract

A method and computing device for inferring an airflow of a controlled appliance operating in an area of a building. The computing device stores a predictive model. The computing device determines a measured airflow of the controlled appliance and a plurality of consecutive temperature measurements in the area. The computing device executes a neural network inference engine using the predictive model for inferring an inferred airflow based on inputs. The inputs comprise the measured airflow and the plurality of consecutive temperature measurements. The inputs may further include at least one of a plurality of consecutive humidity level measurements in the area and a plurality of consecutive carbon dioxide (CO2) level measurements in the area. For instance, the controlled appliance is a Variable Air Volume (VAV) appliance and a K factor of the VAV appliance is calculated based on the inferred airflow.

IPC Classes  ?

• F24F 11/64 - Electronic processing using pre-stored data
• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
• F24F 11/76 - Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity by means responsive to temperature, e.g. bimetal springs
• F24F 110/10 - Temperature
• F24F 110/20 - Humidity
• F24F 110/70 - Carbon dioxide

Abstract

A method and computing device for inferring an airflow of a controlled appliance operating in an area of a building. The computing device stores a predictive model. The computing device determines a measured airflow of the controlled appliance and a plurality of consecutive temperature measurements in the area. The computing device executes a neural network inference engine using the predictive model for inferring an inferred airflow based on inputs. The inputs comprise the measured airflow and the plurality of consecutive temperature measurements. The inputs may further include at least one of a plurality of consecutive humidity level measurements in the area and a plurality of consecutive carbon dioxide (CO2) level measurements in the area. For instance, the controlled appliance is a Variable Air Volume (VAV) appliance and a K factor of the VAV appliance is calculated based on the inferred airflow.

IPC Classes  ?

• F24F 11/63 - Electronic processing
• F24F 11/49 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring ensuring correct operation, e.g. by trial operation or configuration checks
• F04D 27/00 - Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
• F24F 7/007 - Ventilation with forced flow
• G01F 15/04 - Compensating or correcting for variations in pressure, density, or temperature of gases to be measured

Abstract

Environment controller and method for proportionally adjusting a respective light intensity of a plurality of lighting devices. The environment controller determines a current light intensity for each one of the plurality of lighting devices; and determines a current average light intensity by calculating the average of the current light intensities. The environment controller determines a target average light intensity; and determines a new light intensity for each one of the plurality of lighting devices, by proportionally adjusting the light intensity of each one of the plurality of lighting devices from its respective current light intensity to its respective new light intensity, so that the average of the new light intensities is equal to the target average light intensity. The current and new light intensities may be expressed as a percentage; and the target average light intensity received from a computing device or via a user interaction with the environment controller.

IPC Classes  ?

• H05B 45/10 - Controlling the intensity of the light
• H05B 39/04 - Controlling
• H05B 45/20 - Controlling the colour of the light
• H05B 47/10 - Controlling the light source

Abstract

Environment controller and method for proportionally adjusting a respective light intensity of a plurality of lighting devices. The environment controller determines a current light intensity for each one of the plurality of lighting devices; and determines a current average light intensity by calculating the average of the current light intensities. The environment controller determines a target average light intensity; and determines a new light intensity for each one of the plurality of lighting devices, by proportionally adjusting the light intensity of each one of the plurality of lighting devices from its respective current light intensity to its respective new light intensity, so that the average of the new light intensities is equal to the target average light intensity. The current and new light intensities may be expressed as a percentage; and the target average light intensity received from a computing device or via a user interaction with the environment controller.

IPC Classes  ?

• H05B 47/155 - Coordinated control of two or more light sources
• G05B 11/38 - Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a proportional characteristic

Abstract

The present method and electronic device are adapted for secured commissioning. A generic password is stored in memory of the electronic device, and a transmission power of the electronic device is set to a reduced transmission power. The electronic device receives a commissioning request including the generic password and a specific password. The generic password is replaced in the memory of the electronic device by the specific password, and the transmission power of the electronic device is increased to full transmission power.

IPC Classes  ?

• H04W 12/00 - Security arrangements; Authentication; Protecting privacy or anonymity
• H04W 52/14 - Separate analysis of uplink or downlink
• H04W 52/38 - TPC being performed in particular situations
• H04L 12/24 - Arrangements for maintenance or administration
• H04L 29/06 - Communication control; Communication processing characterised by a protocol

Abstract

Environment controller and method for controlling an environmental characteristic in an area of a building based on concurrent BLE requests. The environment controller receives a plurality of concurrent environmental characteristic target values originating from a plurality of BLE enabled user devices. The plurality of concurrent environmental characteristic target values is forwarded from the plurality of BLE enabled user devices to the environment controller via one or more BLE proxy device. The environment controller applies an algorithm to generate one or more command for controlling at least one controlled appliance based on the plurality of concurrent environmental characteristic target values. The environment controller further transmits the one or more command to the at least one controlled appliance. Examples of concurrent environmental characteristic target values include target temperatures, target humidity levels, target CO2 levels, target lightning levels, etc.

IPC Classes  ?

• G05B 15/02 - Systems controlled by a computer electric
• G05D 23/19 - Control of temperature characterised by the use of electric means
• F24F 11/30 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
• F24F 8/10 - Treatment, e.g. purification, of air supplied to human living or working spaces otherwise than by heating, cooling, humidifying or drying by separation, e.g. by filtering

Abstract

The present method and electronic device are adapted for secured commissioning. A generic password is stored in memory of the electronic device, and a transmission power of the electronic device is set to a reduced transmission power. The electronic device receives a commissioning request including the generic password and a specific password. The generic password is replaced in the memory of the electronic device by the specific password, and the transmission power of the electronic device is increased to full transmission power.

IPC Classes  ?

• H04W 60/00 - Affiliation to network, e.g. registration; Terminating affiliation with the network, e.g. de-registration
• H04W 12/04 - Key management, e.g. using generic bootstrapping architecture [GBA]
• H04W 12/08 - Access security
• H04W 52/50 - TPC being performed in particular situations at the moment of starting communication in a multiple access environment
• G06F 21/45 - Structures or tools for the administration of authentication
• H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
• H04W 12/71 - Hardware identity
• G06F 1/26 - Power supply means, e.g. regulation thereof

Abstract

Environment controller and method for controlling an environmental characteristic in an area of a building based on concurrent BLE requests. The environment controller receives a plurality of concurrent environmental characteristic target values originating from a plurality of BLE enabled user devices. The plurality of concurrent environmental characteristic target values is forwarded from the plurality of BLE enabled user devices to the environment controller via one or more BLE proxy device. The environment controller applies an algorithm to generate one or more command for controlling at least one controlled appliance based on the plurality of concurrent environmental characteristic target values. The environment controller further transmits the one or more command to the at least one controlled appliance. Examples of concurrent environmental characteristic target values include target temperatures, target humidity levels, target CO2 levels, target lightning levels, etc.

IPC Classes  ?

• G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
• F24F 11/56 - Remote control
• F24F 11/62 - Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
• H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
• G05D 21/02 - Control of chemical or physico-chemical variables, e.g. pH-value characterised by the use of electric means
• G05D 22/02 - Control of humidity characterised by the use of electric means
• G05D 23/19 - Control of temperature characterised by the use of electric means

Abstract

Environment controller and method for proportionally adjusting a respective light intensity of a plurality of lighting devices. The environment controller determines a current light intensity for each one of the plurality of lighting devices; and determines a current average light intensity by calculating the average of the current light intensities. The environment controller determines a target average light intensity; and determines a new light intensity for each one of the plurality of lighting devices, by proportionally adjusting the light intensity of each one of the plurality of lighting devices from its respective current light intensity to its respective new light intensity, so that the average of the new light intensities is equal to the target average light intensity. The current and new light intensities may be expressed as a percentage; and the target average light intensity received from a computing device or via a user interaction with the environment controller.

IPC Classes  ?

• H05B 33/08 - Circuit arrangements for operating electroluminescent light sources
• H05B 37/02 - Controlling
• H05B 39/04 - Controlling

Abstract

The present disclosure relates to an environment control system for controlling environmental conditions in a building. The environment control system comprises a plurality of sensors located in different areas of the building. Each sensor is used for determining a measured value for one of the environmental conditions in the area where the sensor is located. Further at least one of the plurality of sensors is configured for exchanging data with at least one mobile computing device for modifying a target value of one of the environmental conditions for the area where the sensor is located. The environment control system further comprises an environment controller for receiving the measured values and the modified target values from the plurality of sensors. The environment controller is further configured for comparing the measured values with the modified target values for each area of the building and generating commands for each area of the building based on a difference between the measured values and modified target values. The environment control system further comprises a plurality of room controllers, such that each room controller is installed in a room in one of the areas of the building. Each room controller is configured for exchanging data with the environment controller to obtain the measured values of the environmental conditions in the room.

IPC Classes  ?

• H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
• G05B 15/02 - Systems controlled by a computer electric
• H04W 4/33 - Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings

Abstract

The present disclosure relates to an environment control system. The environment control system comprises a plurality of sensors located in different areas of the building. Each sensor is used for determining a measured value for one of the environmental conditions in the area where the sensor is located. At least one sensor is configured for exchanging data with at least one mobile computing device for modifying a target value of one of the environmental conditions for the area where the sensor is located. The environment control system further comprises an environment controller for receiving the measured values and the modified target values from the plurality of sensors. The environment controller is further configured for comparing the measured values with the modified target values for each area of the building and generating commands for each area of the building based on a difference between the measured values and modified target values.

IPC Classes  ?

• F24F 11/56 - Remote control
• F24F 11/54 - Control or safety arrangements characterised by user interfaces or communication using one central controller connected to several sub-controllers

Abstract

Method and environment controller for validating a predictive model of a neural network. The environment controller receives at least one environmental characteristic value and determines a plurality of input variables. At least one of the plurality of input variables is based on one among the environmental characteristic value(s). The environment controller executes an environment control software module for calculating at least one output variable based on the plurality of input variables. The environment controller transmits the plurality of input variables to a training server executing a neural network training engine using the predictive model; and receives at least one inferred output variable from the training server. Each inferred output variable corresponds to one of the at least one output variable calculated by the environment control software module. The environment controller compares each inferred output variable with the corresponding calculated output variable; and sends a feedback to the training server.

IPC Classes  ?

• G06N 5/02 - Knowledge representation; Symbolic representation
• G06N 3/08 - Learning methods
• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric

Abstract

Method and environment controller using a neural network for bypassing a legacy environment control software module. The environment controller receives at least one environmental characteristic value and determines a plurality of input variables. At least one of the plurality of input variables is based on one among the at least one environmental characteristic value. The environment controller transmits the plurality of input variables to an inference server executing a neural network inference engine. The environment controller receives at least one inferred output variable from the inference server. The environment controller uses the at least one inferred output variable received from the inference server in place of at least one output variable calculated by the legacy environment control software module based on the plurality of input variables. The environment controller may prevent the execution of the legacy software module or overwrite the output variable(s) calculated by the legacy software module.

IPC Classes  ?

• G05B 21/00 - Systems involving sampling of the variable controlled
• G01M 1/38 - Combined machines or devices for both determining and correcting unbalance
• G05B 13/00 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
• G05B 15/00 - Systems controlled by a computer
• G05D 23/00 - Control of temperature
• F24F 11/30 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• F24F 11/54 - Control or safety arrangements characterised by user interfaces or communication using one central controller connected to several sub-controllers
• F24F 11/57 - Remote control using telephone networks
• F24F 11/64 - Electronic processing using pre-stored data
• F24F 11/65 - Electronic processing for selecting an operating mode
• F24F 120/20 - Feedback from users
• F24F 110/10 - Temperature
• F24F 110/20 - Humidity
• F24F 110/70 - Carbon dioxide
• F24F 120/12 - Position of occupants

Abstract

Method and environment controller using a neural network for bypassing a legacy environment control software module. The environment controller receives at least one environmental characteristic value and determines a plurality of input variables. At least one of the plurality of input variables is based on one among the at least one environmental characteristic value. The environment controller transmits the plurality of input variables to an inference server executing a neural network inference engine. The environment controller receives at least one inferred output variable from the inference server. The environment controller uses the at least one inferred output variable received from the inference server in place of at least one output variable calculated by the legacy environment control software module based on the plurality of input variables. The environment controller may prevent the execution of the legacy software module or overwrite the output variable(s) calculated by the legacy software module.

IPC Classes  ?

• F24F 11/62 - Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values

Abstract

Method and environment controller for validating a predictive model of a neural network. The environment controller receives at least one environmental characteristic value and determines a plurality of input variables. At least one of the plurality of input variables is based on one among the environmental characteristic value(s). The environment controller executes an environment control software module for calculating at least one output variable based on the plurality of input variables. The environment controller transmits the plurality of input variables to a training server executing a neural network training engine using the predictive model; and receives at least one inferred output variable from the training server. Each inferred output variable corresponds to one of the at least one output variable calculated by the environment control software module. The environment controller compares each inferred output variable with the corresponding calculated output variable; and sends a feedback to the training server.

IPC Classes  ?

• F24F 11/63 - Electronic processing
• F24F 11/56 - Remote control

Abstract

Method and computing device for inferring via a neural network environmental data of an area of a building based on visible and thermal images of the area. A predictive model generated by a neural network training engine is stored by the computing device. The computing device determines a visible image of an area based on data received from at least one visible imaging camera. The computing device determines a thermal image of the area based on data received from at least one thermal imaging device. The computing device executes a neural network inference engine, using the predictive model for inferring environmental data based on the visible image and the thermal image. The inferred environmental data comprise geometric characteristic(s) of the area, an occupancy of the area, a human activity in the area, temperature value(s) for the area, and luminosity value(s) for the area.

IPC Classes  ?

• G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
• F24F 11/63 - Electronic processing
• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• G06V 10/143 - Sensing or illuminating at different wavelengths
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 20/10 - Terrestrial scenes
• H04N 5/33 - Transforming infrared radiation

Abstract

Method and computing device for inferring via a neural network environmental data of an area of a building based on visible and thermal images of the area. A predictive model generated by a neural network training engine is stored by the computing device. The computing device determines a visible image of an area based on data received from at least one visible imaging camera. The computing device determines a thermal image of the area based on data received from at least one thermal imaging device. The computing device executes a neural network inference engine, using the predictive model for inferring environmental data based on the visible image and the thermal image. The inferred environmental data comprise geometric characteristic(s) of the area, an occupancy of the area, a human activity in the area, temperature value(s) for the area, and luminosity value(s) for the area.

IPC Classes  ?

• F24F 11/62 - Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• G06N 3/02 - Neural networks

Abstract

Method and training server for generating a predictive model for the control of an appliance by an environment controller. The predictive model allows a neural network inference engine to infer output(s) based on inputs. The training server receives room characteristic(s), current environmental characteristic value(s), and set point(s) from the environment controller. The training server determines command(s) for controlling the appliance based on the current environmental characteristic value(s), the set point(s) and the room characteristic(s). Each command is executed by the controlled appliance. The training server receives updated environmental characteristic value(s) and determines a reinforcement signal based on the set point(s), the updated environmental characteristic value(s), and a set of rules. The training server executes a neural network training engine to update the predictive model based on: inputs (the current environmental characteristic value(s), the set point(s), and the room characteristic(s)); output(s) (the command(s)); and the reinforcement signal.

IPC Classes  ?

• G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
• F24F 11/63 - Electronic processing
• G06N 3/08 - Learning methods
• G06N 5/022 - Knowledge engineering; Knowledge acquisition
• G06N 5/04 - Inference or reasoning models

Abstract

Method and training server for generating a predictive model for the control of an appliance by an environment controller. The predictive model allows a neural network inference engine to infer output(s) based on inputs. The training server receives room characteristic(s), current environmental characteristic value(s), and set point(s) from the environment controller. The training server determines command(s) for controlling the appliance based on the current environmental characteristic value(s), the set point(s) and the room characteristic(s). Each command is executed by the controlled appliance. The training server receives updated environmental characteristic value(s) and determines a reinforcement signal based on the set point(s), the updated environmental characteristic value(s), and a set of rules. The training server executes a neural network training engine to update the predictive model based on: inputs comprising the current environmental characteristic value(s), the set point(s), and the room characteristic(s); output(s) consisting of the command(s); and the reinforcement signal.

IPC Classes  ?

• G05B 17/02 - Systems involving the use of models or simulators of said systems electric
• F24F 11/62 - Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values

Abstract

n−1). Specific steps of the method are executed recursively. The present method may further be performed by a computer program product.

IPC Classes  ?

• F24F 11/64 - Electronic processing using pre-stored data
• G05B 17/02 - Systems involving the use of models or simulators of said systems electric
• H05B 47/105 - Controlling the light source in response to determined parameters

Abstract

The present environmental condition controller and method provide for controlling an environmental condition in an area of a building. For doing so, a communication interface receives an environmental condition target value (xrer), and an environmental condition measured value (x). A processing unit calculates an environmental condition adjustment value (yn) with a recursive function based on the environmental condition measured value (x), the environmental condition target value (xref) and an adaptive proportionality value (k). The processing unit also generates and transmits a command based on the environmental condition adjustment value (yn). The processing unit further stores in a memory the environmental condition adjustment value (yn) as a previously calculated environmental condition adjustment value (yn-i). Specific steps of the method are executed recursively. The present method may further be performed by a computer program product.

IPC Classes  ?

• F24F 11/63 - Electronic processing
• G05B 11/38 - Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a proportional characteristic
• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric

Abstract

Environmental condition controller and method for controlling an environmental condition in an area of a building. The controller stores a predictive model generated by a neural network training engine, and a previously calculated environmental condition adjustment value (y,i). The controller receives an environmental condition target value (xref), and an environmental condition measured value (x). The controller recursively calculates an environmental condition adjustment value (yn) by executing a neural network inference engine using the predictive model for inferring the environmental condition adjustment value (yn) based on the previously calculated environmental condition adjustment value (yn_i), the environmental condition target value (xref), the environmental condition measured value (x), and an adaptive proportionality value (k). The controller also generates and transmits a command based on the environmental condition adjustment value (yn). The controller further stores the calculated environmental condition adjustment value (yn) as the previously calculated environmental condition adjustment value (yn_ 1).

IPC Classes  ?

• F24F 11/63 - Electronic processing
• G05B 17/02 - Systems involving the use of models or simulators of said systems electric
• G06N 3/02 - Neural networks

Abstract

Method and environment controller for inferring via a neural network one or more commands for controlling an appliance. A predictive model generated by a neural network training engine is stored by the environment controller. The environment controller receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy). The environment controller receives at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level). The environment controller executes a neural network inference engine, which uses the predictive model for inferring the one or more commands for controlling the appliance based on the at least one environmental characteristic value and the at least one set point. The environment controller transmits the one or more commands to the controlled appliance.

IPC Classes  ?

• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
• G05B 17/02 - Systems involving the use of models or simulators of said systems electric
• G06N 5/04 - Inference or reasoning models
• G01N 33/00 - Investigating or analysing materials by specific methods not covered by groups
• F24F 11/30 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
• F24F 11/62 - Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• F24F 11/76 - Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity by means responsive to temperature, e.g. bimetal springs
• G06N 3/08 - Learning methods
• G06F 9/451 - Execution arrangements for user interfaces
• G01S 17/04 - Systems determining the presence of a target

Abstract

Inference server and environment controller for inferring one or more commands for controlling an appliance. The environment controller receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy) and at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level); and forwards them to the inference server. The inference server executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring the one or more commands based on the received at least one environmental characteristic value and the received at least one set point; and transmits the one or more commands to the environment controller. The environment controller forwards the one or more commands to the controlled appliance.

IPC Classes  ?

• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
• G05B 17/02 - Systems involving the use of models or simulators of said systems electric
• G06N 3/08 - Learning methods
• F24F 11/76 - Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity by means responsive to temperature, e.g. bimetal springs
• F24F 11/30 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
• F24F 11/62 - Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• G06N 5/04 - Inference or reasoning models
• F24F 120/10 - Occupancy
• F24F 110/20 - Humidity
• F24F 110/70 - Carbon dioxide
• F24F 110/10 - Temperature

Abstract

Inference server and environment controller for inferring via a neural network one or more commands for controlling an appliance. The environment controller determines at least one room characteristic. The environment controller receives at least one environmental characteristic value and at least one set point. The environment controller transmits the at least one environmental characteristic, set point and room characteristic to the inference server. The inference server executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring the one or more commands for controlling the appliance. The inference is based on the received at least one environmental characteristic value, at least one set point and at least one room characteristic. The inference server transmits the one or more commands to the environment controller, which forwards the one or more commands to the controlled appliance.

IPC Classes  ?

• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
• G05D 23/19 - Control of temperature characterised by the use of electric means
• C05B 17/02 - Other phosphatic fertilisers, e.g. soft rock phosphates, bone meal containing manganese
• G06N 5/04 - Inference or reasoning models
• G01N 33/00 - Investigating or analysing materials by specific methods not covered by groups
• F24F 11/30 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
• F24F 11/62 - Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• F24F 11/76 - Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity by means responsive to temperature, e.g. bimetal springs
• G06N 3/08 - Learning methods
• G06F 9/451 - Execution arrangements for user interfaces
• G06F 9/455 - Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
• G01S 17/04 - Systems determining the presence of a target
• F24F 120/10 - Occupancy

Abstract

Method and environment controller for inferring via a neural network one or more commands for controlling an appliance. A predictive model generated by a neural network training engine is stored by the environment controller. The environment controller determines at least one room characteristic. The environment controller receives at least one environmental characteristic value and at least one set point. The environment controller executes a neural network inference engine, which uses the predictive model for inferring the one or more commands for controlling the appliance. The inference is based on the at least one environmental characteristic value, the at least one set point and the at least one room characteristic. The environment controller transmits the one or more commands to the controlled appliance.

IPC Classes  ?

• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
• H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
• F24F 11/54 - Control or safety arrangements characterised by user interfaces or communication using one central controller connected to several sub-controllers
• G06N 3/02 - Neural networks
• G05D 22/02 - Control of humidity characterised by the use of electric means
• G05D 23/19 - Control of temperature characterised by the use of electric means
• G06N 5/04 - Inference or reasoning models

Abstract

Computing device and method for inferring a predicted number of physical blocks erased from a flash memory. The computing device stores a predictive model generated by a neural network training engine. A processing unit of the computing device executes a neural network inference engine, using the predictive model for inferring the predicted number of physical blocks erased from the flash memory based on inputs. The inputs comprise a total number of physical blocks previously erased from the flash memory, an amount of data to be written on the flash memory, and optionally an operating temperature of the flash memory. In a particular aspect, the flash memory is comprised in the computing device, and an action may be taken for preserving a lifespan of the flash memory based at least on the predicted number of physical blocks erased from the flash memory.

IPC Classes  ?

• G06F 3/06 - Digital input from, or digital output to, record carriers
• G06F 12/02 - Addressing or allocation; Relocation
• G06N 3/08 - Learning methods
• G06N 5/04 - Inference or reasoning models
• G06F 30/20 - Design optimisation, verification or simulation

Abstract

Computing device and method for inferring a predicted number of data chunks writable on a flash memory before the flash memory wears out. The computing device stores a predictive model generated by a neural network training engine. A processing unit of the computing device executes a neural network inference engine, using the predictive model for inferring the predicted number of data chunks writable on the flash memory before the flash memory wears out based on inputs. The inputs comprise a total number of physical blocks previously erased from the flash memory, a size of the data chunk, and optionally an operating temperature of the flash memory. In a particular aspect, the flash memory is comprised in the computing device, and an action may be taken for preserving a lifespan of the flash memory based at least on the predicted number of data chunks writable on the flash memory.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 7/00 - Computing arrangements based on specific mathematical models
• G11C 16/34 - Determination of programming status, e.g. threshold voltage, overprogramming or underprogramming, retention
• G11C 16/10 - Programming or data input circuits

Abstract

Inference server and computing device for inferring an optimal wireless data transfer rate. The computing device determines parameters of a data transfer through a wireless communication interface of the computing device, and transmits the parameters of the data transfer to the inference server. The inference server receives the parameters of the data transfer, executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring an optimal data transfer rate based on the parameters of the data transfer, and transmits the optimal data transfer rate to the computing device. The computing device receives the optimal data transfer rate, and configures its wireless communication interface to operate at the optimal data transfer rate. For example, the computing device consists of an environment control device (e.g. an environment controller, a sensor, a controlled appliance, and a relay).

IPC Classes  ?

• H04W 24/02 - Arrangements for optimising operational condition
• H04B 17/309 - Measuring or estimating channel quality parameters

Abstract

Method and computing device for inferring an optimal wireless data transfer rate using a neural network. The method comprises storing a predictive model generated by a neural network training engine in a memory of a computing device. The method comprises determining, by a processing unit of the computing device, parameters of a data transfer through a wireless communication interface of the computing device. The method comprises executing, by the processing unit, a neural network inference engine using the predictive model for inferring an optimal data transfer rate based on the parameters of the data transfer through the wireless communication interface. The method comprises configuring the wireless communication interface to operate at the optimal data transfer rate. For example, the computing device consists of an environment control device (ECD). The ECD may consist of an environment controller, a sensor, a controlled appliance, and a relay.

IPC Classes  ?

• H04W 24/02 - Arrangements for optimising operational condition
• H04B 17/309 - Measuring or estimating channel quality parameters

Abstract

Method and computing device for inferring an optimal wireless data transfer rate using a neural network. The method comprises storing a predictive model generated by a neural network training engine in a memory of a computing device. The method comprises determining, by a processing unit of the computing device, parameters of a data transfer through a wireless communication interface of the computing device. The method comprises executing, by the processing unit, a neural network inference engine using the predictive model for inferring an optimal data transfer rate based on the parameters of the data transfer through the wireless communication interface. The method comprises configuring the wireless communication interface to operate at the optimal data transfer rate. For example, the computing device consists of an environment control device (ECD). The ECD may consist of an environment controller, a sensor, a controlled appliance, and a relay.

IPC Classes  ?

• H04W 28/06 - Optimising, e.g. header compression, information sizing
• H04W 48/16 - Discovering; Processing access restriction or access information
• H04L 47/36 - Flow control; Congestion control by determining packet size, e.g. maximum transfer unit [MTU]
• G06N 3/02 - Neural networks
• H04W 48/18 - Selecting a network or a communication service
• H04W 28/22 - Negotiating communication rate
• H04W 24/02 - Arrangements for optimising operational condition
• H04W 28/02 - Traffic management, e.g. flow control or congestion control
• H04W 84/18 - Self-organising networks, e.g. ad hoc networks or sensor networks

Goods & Services

Software as a services (SaaS) services featuring software for the electromechanical management of buildings, namely, software for the development, management, engineering, estimating, graphical user interface, and controlling of building heating, ventilation, air conditioning systems, lighting, access, and fire and security alarms; Providing temporary use of non-downloadable software for developing, connecting, engineering, estimating, operating, managing and controlling building heating, ventilation, air conditioning systems, lighting, access, and fire and security alarms; computer services, namely, providing a website featuring technology for the electromechanical management of buildings, namely, software that enables users to monitor, estimate, engineer, development, management, graphical user interface and controlling of buildings covering building heating, ventilation, air conditioning systems, lighting, access, and fire and security alarms

Abstract

The present relates to a system and a bidirectional differential pressure sensor. The system and bidirectional differential pressure sensor comprise a first adaptor comprising an end configured to receive a first pipe, and a second adaptor comprising an end configured to receive a second pipe. The system and bidirectional differential pressure sensor further comprise a pressure sensing element determining a pressure differential between fluid received via the first adaptor with respect to fluid received via the second adaptor. The system or bidirectional differential pressure sensor further comprise a processing unit executing an algorithm for generating an adjusted pressure differential based on the pressure differential determined by the pressure sensing element.

IPC Classes  ?

• G01L 27/00 - Testing or calibrating of apparatus for measuring fluid pressure
• G01L 13/00 - Devices or apparatus for measuring differences of two or more fluid pressure values
• G01L 19/08 - Means for indicating or recording, e.g. for remote indication

Abstract

A system comprising a housing, a printed circuit board and a differential pressure sensor located inside the housing, and first and second male adaptors. The first and second male adaptors respectively extend through first and second openings in the housing. The first male adaptor comprises a proximal end configured to receive a first pipe, a distal end secured to the differential pressure sensor, and an internal fluid conduit for transmitting fluid received from the first pipe to the differential pressure sensor. The second male adaptor comprises a proximal end configured to receive a second pipe, a distal end secured to the differential pressure sensor, and an internal fluid conduit for transmitting fluid received from the second pipe to the differential pressure sensor. The differential pressure sensor is configured to determine a pressure differential between fluid received via the first male connector and fluid received via the second male connector.

IPC Classes  ?

• G01L 27/00 - Testing or calibrating of apparatus for measuring fluid pressure
• G01L 19/00 - MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE - Details of, or accessories for, apparatus for measuring steady or quasi-steady pressure of a fluent medium insofar as such details or accessories are not special to particular types of pressure gauges
• G01L 19/14 - Housings

Abstract

Environment controllers for the configuration and control of a plurality of smart light fixtures. A master environment controller exchange data with a fixture management server and a plurality of slave environment controllers. Each slave environment controller is in charge of controlling a plurality of light fixtures. The master environment controller receives a command targeting a particular light fixture from the fixture management server. The environment controller determines a target slave environment controller in charge of controlling the particular light fixture, among the plurality of slave environment controllers. The master environment controller forwards the received command to the target slave environment controller. The target slave environment controller further transmits the forwarded command to the particular light fixture. Alternatively, an independent environment controller is in charge of controlling a plurality of light fixtures under the direct supervision of a fixture management server, without using an intermediate master environment controller.

IPC Classes  ?

• H04M 1/725 - Cordless telephones
• H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
• H04W 4/00 - Services specially adapted for wireless communication networks; Facilities therefor
• H04W 84/12 - WLAN [Wireless Local Area Networks]

Abstract

An environment control device (ECD) providing a Wi-Fi hotspot for accessing the Internet. The ECD comprises a communication module with a Wi-Fi hotspot functionality for establishing a Wi-Fi hotspot at the ECD, and a mesh client functionality for communicating over a mesh network. The communication module provides for exchanging environmental data with at least another ECD, over one of the Wi-Fi hotspot or mesh network. It also provides for receiving upstream Internet data from at least one user terminal over the Wi-Fi hotspot, and forwarding the upstream Internet data to another ECD over the mesh network. It further provides for receiving downstream Internet data from another ECD over the mesh network, and forwarding the downstream Internet data to the at least one user terminal over the Wi-Fi hotspot. The ECD providing the Wi-Fi hotspot for accessing the Internet may be a daisy-chained ECD controlled by a master ECD.

IPC Classes  ?

• H04W 76/02 - Connection set-up
• H04W 4/00 - Services specially adapted for wireless communication networks; Facilities therefor
• H04L 12/24 - Arrangements for maintenance or administration
• H04W 76/10 - Connection setup
• H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
• H04W 84/18 - Self-organising networks, e.g. ad hoc networks or sensor networks
• H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
• H04W 84/12 - WLAN [Wireless Local Area Networks]

Abstract

An environment control device (ECD) providing a Wi-Fi hotspot for accessing the Internet. The ECD comprises a communication module with a Wi-Fi hotspot functionality for establishing a Wi-Fi hotspot at the ECD, and a mesh client functionality for communicating over a mesh network. The communication module provides for exchanging environmental data with at least another ECD, over one of the Wi-Fi hotspot or mesh network. It also provides for receiving upstream Internet data from at least one user terminal over the Wi-Fi hotspot, and forwarding the upstream Internet data to another ECD over the mesh network. It further provides for receiving downstream Internet data from another ECD over the mesh network, and forwarding the downstream Internet data to the at least one user terminal over the Wi-Fi hotspot. The ECD providing the Wi-Fi hotspot for accessing the Internet may be a daisy-chained ECD controlled by a master ECD.

IPC Classes  ?

• H04W 40/22 - Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
• H04W 12/02 - Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
• H04W 80/10 - Upper layer protocols adapted for session management, e.g. SIP [Session Initiation Protocol]
• H04W 84/18 - Self-organising networks, e.g. ad hoc networks or sensor networks

Abstract

The present disclosure relates to an environment control device (ECD) and a method. The ECD and method comprise a first communication interface, a second communication interface of the Wi-Fi type, and a processing unit. The processing unit sends a DHCP-DISCOVER message via the first communication interface. The processing unit also configures the ECD to operate the second communication interface as a Wi-Fi Access Point if a DHCP-OFFER message is received in response to the DHCP-DISCOVER message via the first communication interface. The processing unit also configures the ECD to operate the second communication interface as a Wi-Fi hotspot if no DHCP-OFFER message is received in response to the DHCP-DISCOVER message via the first communication interface..

IPC Classes  ?

• G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
• F24F 11/02 - Arrangement or mounting of control or safety devices
• H04W 48/16 - Discovering; Processing access restriction or access information
• H04W 8/26 - Network addressing or numbering for mobility support

Abstract

The present disclosure relates to an environment control device (ECD) and a method. The ECD and method comprise a first communication interface, a second communication interface of the Wi-Fi type, and a processing unit. The processing unit sends a DHCP-DISCOVER message via the first communication interface. The processing unit also configures the ECD to operate the second communication interface as a Wi-Fi Access Point if a DHCP-OFFER message is received in response to the DHCP-DISCOVER message via the first communication interface. The processing unit also configures the ECD to operate the second communication interface as a Wi-Fi hotspot if no DHCP-OFFER message is received in response to the DHCP-DISCOVER message via the first communication interface.

IPC Classes  ?

• G05B 19/042 - Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
• H04W 8/26 - Network addressing or numbering for mobility support
• H04W 48/16 - Discovering; Processing access restriction or access information
• F24F 11/56 - Remote control
• F24F 11/58 - Remote control using Internet communication

Abstract

The present disclosure relates to an environment control device (ECD) and a method. The ECD and method comprise a first communication interface, a second communication interface of the Wi-Fi type, and a processing unit. The processing unit sends a DHCP-DISCOVER message via the first communication interface. The processing unit also configures the ECD to operate the second communication interface as a Wi-Fi Access Point if a DHCP-OFFER message is received in response to the DHCP-DISCOVER message via the first communication interface. The processing unit also configures the ECD to operate the second communication interface as a Wi-Fi hotspot if no DHCP-OFFER message is received in response to the DHCP-DISCOVER message via the first communication interface.

IPC Classes  ?

• H04M 3/00 - Automatic or semi-automatic exchanges
• H04W 48/18 - Selecting a network or a communication service
• H04W 76/02 - Connection set-up
• H04L 29/12 - Arrangements, apparatus, circuits or systems, not covered by a single one of groups characterised by the data terminal
• H04L 12/741 - Header address processing for routing, e.g. table lookup
• H04W 84/12 - WLAN [Wireless Local Area Networks]
• H04W 84/22 - Self-organising networks, e.g. ad hoc networks or sensor networks with access to wired networks
• H04W 88/10 - Access point devices adapted for operation in multiple networks, e.g. multi-mode access points

Abstract

A common zero volt reference AC/DC power supply with positive and negative rectification, and method of operation thereof. The power supply comprises an input for receiving an AC input voltage and an output for outputting a DC output voltage, the AC input and DC output having a common zero volt reference. The power supply comprises a first rectifier and a second rectifier, for respectively performing a half-wave rectification of a positive half cycle and negative half cycle of the AC input voltage. The power supply comprises control logic for detecting the negative half cycle of the AC input voltage and activating the second rectifier upon the detection. The power supply comprises a power converter for converting a DC rectified voltage received from at least one of the first and second rectifiers into the DC output voltage.

IPC Classes  ?

• H02M 7/217 - Conversion of ac power input into dc power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only

Abstract

The present environment controller is adapted for being powered in low-voltage daisy-chained power configuration. The environment controller comprises a low-voltage daisy-chainable power supply comprising a Power Factor Conversion (PFC) flyback converter. The low-voltage daisy-chainable power supply receives a low-voltage power and outputs a high PFC low-voltage power for powering the environment controller.

IPC Classes  ?

• G05B 15/02 - Systems controlled by a computer electric
• H02M 1/42 - Circuits or arrangements for compensating for or adjusting power factor in converters or inverters

Abstract

A master device, daisy-chained devices, and a method for configuring the daisy-chained devices are provided. The master device generates a signal having a pre-determined base frequency, and outputs the signal generated to a first device in the daisy chain communication configuration. Each daisy-chained device receives an input signal, having an input frequency, from a previous daisy-chained device. Each daisy-chained device generates an output signal having an output frequency different to and based on the input frequency of the received signal, and outputs the output signal to a following daisy-chained device. Each daisy-chained device further determines an address of a communication interface, for exchanging data with the master device, based on the input frequency of the received signal. For example, the output frequency of the output signal is half the input frequency of the received signal.

IPC Classes  ?

• G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
• H04L 12/24 - Arrangements for maintenance or administration
• H04L 12/40 - Bus networks

Abstract

A master device, daisy-chained devices, and a method for configuring the daisy-chained devices are provided. The master device generates a signal having a pre-determined base frequency, and outputs the signal generated to a first device in the daisy chain communication configuration. Each daisy-chained device receives an input signal, having an input frequency, from a previous daisy-chained device. Each daisy-chained device generates an output signal having an output frequency different to and based on the input frequency of the received signal, and outputs the output signal to a following daisy-chained device. Each daisy-chained device further determines an address of a communication interface, for exchanging data with the master device, based on the input frequency of the received signal. For example, the output frequency of the output signal is half the input frequency of the received signal.

IPC Classes  ?

• H04L 41/0806 - Configuration setting for initial configuration or provisioning, e.g. plug-and-play
• H04L 41/082 - Configuration setting characterised by the conditions triggering a change of settings the condition being updates or upgrades of network functionality
• H04L 12/40 - Bus networks

Goods & Services

Microprocessor-based interface controllers and software for controlling the electromechanical management of buildings covering building's heating, ventilation, air conditioning systems, lighting, access, fire and security alarms, telecommunications and computer networks. Installation and maintenance of computerized controls for the operation of a building's heating, ventilation and air conditioning systems, lighting, access, fire and security alarms, telecommunications and computer networks. Manufacturing of computerized controls for the operation of a building's heating, ventilation and air conditioning systems, fire and security alarms, telecommunications and computer networks to the order and specification of others.

Goods & Services

Web-based building management system platform for the development, management and graphical user interface of a building management network/system, namely HVAC, Lighting and Access control and energy management.

Goods & Services

Web-based computer software platform for the development, management and graphical user interface of a building management network and system, namely, HVAC, lighting and access control and energy management

Goods & Services

(1) Computer software in the field of building management systems for the development, management and graphical user interface of a building management network/system, namely HVAC, Lighting and Access control and energy management

Abstract

The present disclosure relates to an environment control device (ECD) and a method using a wireless communication infrastructure for exchanging environmental data. The wireless communication infrastructure comprises a first Wi-Fi hotspot, and at least one of a second Wi-Fi hotspot and a mesh network. The ECD comprises a communication module for exchanging environmental data with at least another device over the wireless communication infrastructure. The communication module is capable of establishing the first Wi-Fi hotspot, associating with the second Wi-Fi hotspot, and communicating over the mesh network. The ECD further comprises a processing module capable of processing environmental data received from the other device via the wireless communication infrastructure, and/or transmitting generated environmental data to the other device via the wireless communication infrastructure. The ECD may consist of an environment controller, a sensor, a controlled appliance, and a relay for wired devices.

IPC Classes  ?

• H04Q 9/00 - Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
• H04W 48/20 - Selecting an access point
• H04W 24/00 - Supervisory, monitoring or testing arrangements
• H04W 84/18 - Self-organising networks, e.g. ad hoc networks or sensor networks

Abstract

The present disclosure relates to an environment control device (ECD) and a method using a wireless communication infrastructure for exchanging environmental data. The wireless communication infrastructure comprises a first Wi-Fi hotspot, and at least one of a second Wi-Fi hotspot and a mesh network. The ECD comprises a communication module for exchanging environmental data with at least another device over the wireless communication infrastructure. The communication module is capable of establishing the first Wi-Fi hotspot, associating with the second Wi-Fi hotspot, and communicating over the mesh network. The ECD further comprises a processing module capable of processing environmental data received from the other device via the wireless communication infrastructure, and / or transmitting generated environmental data to the other device via the wireless communication infrastructure. The ECD may consist of an environment controller, a sensor, a controlled appliance, and a relay for wired devices.

IPC Classes  ?

• H04W 84/18 - Self-organising networks, e.g. ad hoc networks or sensor networks
• F24F 11/58 - Remote control using Internet communication

Goods & Services

Programmable building automation controllers and control applications, for the configuration, control and monitoring of HVAC (Heating, Ventilation, Air-Conditionning) and energy management applications, in commercial and institutional buildings.

Goods & Services

Programmable building automation controllers and control applications, for the configuration, control and monitoring of HVAC and energy management applications, in commercial and institutional buildings

Goods & Services

(1) Programmable building automation controllers and control applications, for the configuration, control and monitoring of HVAC and energy management applications, in commercial and institutional buildings.

Goods & Services

(1) Graphics oriented Web-based energy management dashboard that provides proven visualization of vital building metrics through an easy-to-use, browser-based dashboard application.

Goods & Services

(1) Web-based building management system platform for the development, management and graphical user interface of a building management network/system, namely HVAC, Lighting and Access control and energy management.

Goods & Services

(1) Microprocessor-based interface controllers and software for controlling the electromechanical management of buildings covering building's heating, ventilation, air conditioning systems, lighting, access, fire and security alarms, and computer networks.

Goods & Services

Thermostats, sensors, regulators, timers, relays and microprocessor-based interface controllers and software for controlling heating, ventilation, air conditioning systems for the environmental and energy management of buildings.

Goods & Services

Microprocessor-based interface controllers and software for providing a real time evaluation of the environmental impact of user's decision regarding the management of buildings' heating, ventilation, air conditioning systems.

Goods & Services

Thermostats, temperature sensors, voltage regulators, timers, electric relays and microprocessor-based controllers and software for controlling heating, ventilation, or air conditioning systems

Goods & Services

(1) Microprocessor-based interface controllers and software for providing a real time evaluation of the environmental impact of user's decision regarding the management of buildings' heating, ventilation, air conditioning systems. (1) Manufacturing, installation and maintenance of computerized controls for the provision of a real time evaluation of the environmental impact of user's decision regarding the management of buildings' heating, ventilation, air conditioning systems.