Goods & Services

Recorded and pre-installed computer software using artificial intelligence (AI) for the production and processing of speech, text, images, video, sound, and code sold as a component of mobile phones; recorded and pre-installed software using artificial intelligence (AI) for simulating conversations and answering queries; recorded software using artificial intelligence (AI) for tracking and assisting users with tasks sold as a component of mobile phones; recorded and pre-installed software using artificial intelligence (AI) for use as an intelligent personal digital assistant; recorded software using artificial intelligence (AI) for providing personalized recommendations, task management advice, scheduling, and reminders sold as a component of mobile phones; recorded and pre-installed software using artificial intelligence (AI) for processing, compiling, and organizing personal information across various applications sold as a component of mobile phones; downloadable computer software using artificial intelligence (AI) for the production and processing of speech, text, images, video, sound, and code; downloadable software using artificial intelligence (AI) for simulating conversations and answering queries; downloadable software using artificial intelligence (AI) for tracking and assisting users with tasks; downloadable software using artificial intelligence (AI) for use as an intelligent personal digital assistant; downloadable software using artificial intelligence (AI) for providing personalized recommendations, task management advice, scheduling, and reminders; downloadable software using artificial intelligence (AI) for processing, compiling, and organizing personal information across various applications. Providing online non-downloadable software using artificial intelligence (AI) for the production and processing of speech, text, images, video, sound, and code; providing online non-downloadable software using artificial intelligence (AI) for simulating conversations and answering queries; providing online non-downloadable software using artificial intelligence (AI) for tracking and assisting users with tasks; providing online non-downloadable software using artificial intelligence (AI) for use as an intelligent personal digital assistant; providing online non-downloadable software using artificial intelligence (AI) for providing personalized recommendations, task management advice, scheduling, and reminders; providing online non-downloadable software using artificial intelligence (AI) for processing, compiling, and organizing personal information across various applications; providing search engines for obtaining data via the internet and other electronic communications networks; creating indexes of online information, sites and other resources available on the Internet and other electronic communications networks.

Abstract

This document describes systems and techniques for a self-destructive code device for a rechargeable battery device. For example, a system comprises a rechargeable battery device certified for use with an electronic device. An authentication code is associated with the rechargeable battery device to validate that the rechargeable battery device is authenticated for use with the electronic device. A self-destructive code device is attachable to the rechargeable battery device, the self-destructive code device being configured to present the authentication code and cause the authentication code to become unusable after that rechargeable battery device is deployed for use with the electronic device.

IPC Classes  ?

• H01M 10/42 - Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
• H01M 50/572 - Means for preventing undesired use or discharge

Abstract

Implementations set forth herein relate to off-loading, or temporarily ceasing such off-loading, computational tasks to a separate computing device based on a network metric(s) that is not limited to signal strength. Rather, a network metric for determining whether to continue relying on a network connection with a server computing device for certain computational tasks can be based on a current, or recent, interaction with the server computing device. In this way, an application executing at a computing device having a powerful antenna—but an otherwise limited network velocity, can determine to temporarily rely exclusively on local processing. For instance, an automated assistant can temporarily cease communicating audio data to a remote server computing device, during a dialog session, in response to determining a network metric fails to satisfy a threshold—even though there may appear to be adequate signal strength to effectively transmit the audio data.

IPC Classes  ?

• G10L 15/30 - Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
• G06F 21/31 - User authentication
• G10L 15/00 - Speech recognition
• G10L 15/18 - Speech classification or search using natural language modelling
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog

Abstract

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for processing a network input using a neural network to generate a network output for the network input. That is, by using a neural network that includes a sequence of layer blocks that, for each layer block, processes a block input for the particular layer block through a learned non-linear transformation to generate an initial block output for the particular layer block and combines the initial block output for the particular layer block with at least the block input in accordance with one or more learned parameters to generate the block output for the particular layer block, the described techniques maximize the neural network performance for a given neural network footprint.

IPC Classes  ?

• G06N 3/0464 - Convolutional networks [CNN, ConvNet]
• G06N 3/044 - Recurrent networks, e.g. Hopfield networks

Abstract

A self-monitoring system for a micro-LED display panel can track a health status of the micro-LED emitters over the life cycle of the display. The self-monitoring system can include, for example, light sensors and a coverglass treated with an anti-reflective coating that directs light emitted by the micro-LED array toward the light sensors. Light captured by the light sensors can then be analyzed to determine the current value of light attributes such as color, polarization, and intensity, and to compare the current values of the light attributes with their previous values to monitor changes over time.

IPC Classes  ?

• H10H 29/24 - Assemblies of multiple devices comprising at least one light-emitting semiconductor device covered by group comprising multiple light-emitting semiconductor devices
• H01L 25/16 - Assemblies consisting of a plurality of individual semiconductor or other solid-state devices the devices being of types provided for in two or more different subclasses of , , , , or , e.g. forming hybrid circuits
• H10F 55/25 - Radiation-sensitive semiconductor devices covered by groups , or being structurally associated with electric light sources and electrically or optically coupled thereto wherein the electric light source controls the radiation-sensitive semiconductor devices, e.g. optocouplers wherein the radiation-sensitive devices and the electric light source are all semiconductor devices
• H10H 29/80 - Constructional details

Abstract

A method includes receiving, by a processing device of a security analytics platform, data associated with a computing resource and assigning a first subset of a set of security rules to a first node of the security analytics platform and a second subset of the set of security rules to a second node of the security analytics platform. The first node applies, to the data, the first subset of security rules to generate first analytics data and the second node applies, to the data, the second subset of security rules to generate second analytics data. The first analytics data and the second analytics data are sent to a system associated with the computing resource.

IPC Classes  ?

• G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
• G06F 21/54 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by adding security routines or objects to programs
• G06F 21/55 - Detecting local intrusion or implementing counter-measures

Abstract

A method includes receiving, by a security analytics platform, first data associated with a computing resource, storing the first data in a first database table associated with the computing resource, and generating a first set of indicators associated with the first database table. Each indicator of the first set of indicators identifies a corresponding horizontal partition associated with the first database table. The method further includes receiving second data associated with the computing resource, storing the second data in a second database table associated with the first database table, and generating a second set of indicators associated with the second database table. The method further includes storing, based on the first and second set of indicators, a first partition of the first database table and a corresponding partition of the second database table, on a same database node.

IPC Classes  ?

• G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/23 - Updating

Abstract

A method includes receiving training data comprising a plurality of pairs of images. Each pair comprises a noisy image and a denoised version of the noisy image. The method also includes training a multi-task diffusion model to perform a plurality of image-to-image translation tasks, wherein the training comprises iteratively generating a forward diffusion process by predicting, at each iteration in a sequence of iterations and based on a current noisy estimate of the denoised version of the noisy image, noise data for a next noisy estimate of the denoised version of the noisy image, updating, at each iteration, the current noisy estimate to the next noisy estimate by combining the current noisy estimate with the predicted noise data, and determining a reverse diffusion process by inverting the forward diffusion process to predict the denoised version of the noisy image. The method additionally includes providing the trained diffusion model.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Abstract

A computing device is configured to obtain information for an application. The computing device is further configured to generate, using a machine learning model and based on the usage information, at least one intent score. The computing device is further configured to determine, based on the at least one intent score, one or more navigation settings for the application, wherein the one or more navigation settings indicate a particular page that the application should open upon launching of the application. The computing device is further configured to cause, upon launching of the application, the application to open the particular page.

IPC Classes  ?

• G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• G06F 9/451 - Execution arrangements for user interfaces

Abstract

A method includes determining a first access control band based on a first historical computing usage of a distributed computing system by one or more workloads. The method also includes determining a second access control band based on a second historical computing usage of the distributed computing system. The method also includes determining a third access control band based on an amount of computing resources of the distributed computing system not defined by the first access control band or the second access control band. The method also includes receiving a request for a particular amount of computing resources and determining one or more access control bands from the first access control band, the second access control band, and the third access control band. The method also includes allocating to the one or more workloads at least a portion of the requested particular amount of computing resources.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation

Abstract

A user computing device includes one or more first sensors that output one or more first signals based on a magnetic field measured via the one or more first sensors and one or more processors configured to execute instructions to perform operations. The operations include detecting whether the user computing device is receiving a charge from a charging device. When the user computing device is not receiving the charge, the operations include determining, based on the one or more first signals output by the one or more first sensors, whether the user computing device is proximate to the charging device, and when the user computing device is determined, based on the one or more first signals output by the one or more first sensors, to be proximate to the charging device, providing an output indicating the user computing device is not receiving the charge.

IPC Classes  ?

• H02J 7/00 - Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
• G01R 33/00 - Arrangements or instruments for measuring magnetic variables

Abstract

A system and related method for determining a temperature of an object via a mobile device having a camera that generates image data across a first field of view, and a temperature sensor that generates temperature data across a second field of view overlapping the first field of view. A computing system receives the image data, each of the plurality of images of the image data being associated with a different respective position of the mobile device. The computing system also receives the temperature data, each of the plurality of average temperatures of the temperature data corresponding to a respective one of the plurality of images. The computing system determines, based at least in part on the plurality of images and the plurality of average temperatures, a temperature of a desired object at least partially within the first and second fields of view.

IPC Classes  ?

• H04N 23/60 - Control of cameras or camera modules
• G01J 5/10 - Radiation pyrometry, e.g. infrared or optical thermometry using electric radiation detectors
• G06T 7/11 - Region-based segmentation
• G06T 7/174 - SegmentationEdge detection involving the use of two or more images
• H04N 23/61 - Control of cameras or camera modules based on recognised objects

Abstract

A system and method for exploring security rule chains in a security platform. The method includes displaying a first plurality of graphical elements of a graphical user interface (GUI), each graphical element of the first plurality of graphical elements referencing a respective chained outcome of a plurality of chained outcomes of a respective chained rule, The respective chained rule includes two or more security rules that are linked based on their respective security outcomes, receiving, via the GUI, a selection of a first graphical element of the first plurality of graphical elements, the first graphical element corresponding to a first chained outcome of the plurality of chained outcomes, and displaying a second plurality of graphical elements in a visual association with the first element, each element of the second plurality of elements referencing a respective security outcome of the two or more security rules that are serially linked.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• H04L 41/22 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks comprising specially adapted graphical user interfaces [GUI]

Abstract

Systems and methods of the present disclosure are directed to a computer-implemented method for recycling of task-specific prompts for machine-learned models. The method includes obtaining a task-specific prompt for a first machine-learned model, wherein the task-specific prompt is indicative of a task of a plurality of tasks the first machine-learned model is configured to perform. includes determining a difference between the first machine-learned model and a second machine-learned model different than the first machine-learned model. The method includes, based at least in part on the difference, modifying the task-specific prompt to obtain an updated task-specific prompt that corresponds to the second machine-learned model.

IPC Classes  ?

• G06F 16/332 - Query formulation
• G06N 20/00 - Machine learning

Abstract

Methods, a system, and a device are provided to allow co-watch devices to coordinate text interpretation services while co-watching a video or live event. A server receives an indication that a first co-watch device and a second co-watch device are preparing to co-watch a video or a live event while displaying a text interpretation of a speech component of the video or live event. An indication is sent to a first device of the first and second co-watch devices to operate as a text-processing device, generating the text interpretation, and transmitting the text interpretation to a second device of the first and second co-watch devices. The first device receives a portion of a video, processes a speech component of the portion of the video to generate a text interpretation, and sends the text interpretation to a second device.

IPC Classes  ?

• H04N 21/488 - Data services, e.g. news ticker
• H04N 21/41 - Structure of clientStructure of client peripherals
• H04N 21/4788 - Supplemental services, e.g. displaying phone caller identification or shopping application communicating with other users, e.g. chatting

Abstract

A system to broadcast an audio signal includes user equipment (UE) configured to concurrently broadcast the audio signal to a plurality of receiving devices based on a broadcast template. As the UE broadcasts the audio signal, the UE receives reception data from the plurality of receiving devices indicating the quality of the reception of the audio signal. Based on one or more event triggers occurring, the UE adjusts one or more broadcast parameters of the broadcast template based on the reception data received from the plurality of receiving devices. The UE then continues to broadcast the audio signal based on the adjusted broadcast parameters of the broadcast template.

IPC Classes  ?

• H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
• H04H 20/71 - Wireless systems

Abstract

Implementations set forth herein relate to a system that employs an automated assistant to further interactions between a user and another application, which can provide the automated assistant with permission to initialize relevant application actions simultaneous to the user interacting with the other application. Furthermore, the system can allow the automated assistant to initialize actions of different applications, despite being actively operating a particular application. Available actions can be gleaned by the automated assistant using various application-specific schemas, which can be compared with incoming requests from a user to the automated assistant. Additional data, such as context and historical interactions, can also be used to rank and identify a suitable application action to be initialized via the automated assistant.

IPC Classes  ?

• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog

Abstract

Systems and techniques for a reverberation cancellation framework include receiving a far-field audio signal from a far-field microphone array and a near-field audio signal from a near-field microphone array, where the far-field microphone array is a greater distance from an audio source than the near-field microphone array. The far-field audio signal and the near-field audio signal are synchronized. The far-field audio signal and the near-field audio signal are encoded to remove noise artifacts from the far-field audio signal and the near-field audio signal. The far-field audio signal and the near-field audio signal are decoded to output an output audio signal with the noise artifacts removed.

IPC Classes  ?

• G10L 21/0232 - Processing in the frequency domain
• G10L 21/0208 - Noise filtering
• G10L 21/10 - Transforming into visible information
• G10L 25/18 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
• G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks

Abstract

Methods and apparatus for assigning a quality metric to an object to be grasped by a mobile robot are provided. The method includes receiving at least one image including a set of objects, processing the at least one image using a trained machine learning model to assign a quality metric to a first object of the set of objects in the at least one image, and controlling the mobile robot to perform an action based, at least in part, on the quality metric assigned to the first object.

IPC Classes  ?

• G05D 1/656 - Interaction with payloads or external entities
• G05D 1/646 - Following a predefined trajectory, e.g. a line marked on the floor or a flight path
• G05D 1/65 - Following a desired speed profile
• G05D 101/15 - Details of software or hardware architectures used for the control of position using artificial intelligence [AI] techniques using machine learning, e.g. neural networks
• G05D 107/70 - Industrial sites, e.g. warehouses or factories
• G05D 111/10 - Optical signals
• G06T 7/00 - Image analysis
• G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
• G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle

Abstract

Apparatus and methods for mitigating slip conditions and estimating ground friction for a robot having a plurality of feet are provided. In one aspect, a method includes estimating a coefficient of friction for a ground surface supporting the legged robot based on sensor data, odometry data, and a terrain map of an environment. The sensor data includes a set of joint angles and a set of joint torques for a set of joints of the legged robot, and the odometry data indicates a location of the legged robot in the environment. One of the plurality of feet of the robot applies a force on the ground surface based on the estimated coefficient of friction.

IPC Classes  ?

• B25J 9/16 - Programme controls

Abstract

A system to broadcast an audio signal includes user equipment (UE) configured to concurrently broadcast the audio signal to a plurality of receiving devices based on a broadcast template. As the UE broadcasts the audio signal, the UE receives reception data from the plurality of receiving devices indicating the quality of the reception of the audio signal. Based on one or more event triggers occurring, the UE adjusts one or more broadcast parameters of the broadcast template based on the reception data received from the plurality of receiving devices. The UE then continues to broadcast the audio signal based on the adjusted broadcast parameters of the broadcast template.

IPC Classes  ?

• H04W 4/06 - Selective distribution of broadcast services, e.g. multimedia broadcast multicast service [MBMS]Services to user groupsOne-way selective calling services
• H04L 1/18 - Automatic repetition systems, e.g. Van Duuren systems
• H04L 65/611 - Network streaming of media packets for supporting one-way streaming services, e.g. Internet radio for multicast or broadcast
• H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication

Abstract

One example method includes receiving, by an artificial intelligence (AI) system, a query; generating, by the AI system and based on the query, a plurality of candidate digital components using a machine learning model; obtaining, by the AI system, evaluation results associated with the plurality of candidate digital components, each evaluation result indicating whether a corresponding candidate digital component comprises restricted content; obtaining, by the AI system, performance data indicating an acceptance level of each candidate digital component of the plurality of candidate digital components; identifying, by the AI system and based on the evaluation results and the performance data, a candidate digital component of the plurality of candidate digital components; generating, by the AI system and based on the candidate digital component, training data; and refining, by the AI system and using the training data, the machine learning model.

Abstract

Techniques are provided for speech signal repair and enhancement using an integrated network based on progressive learning. For example, a degraded speech signal is received on a speech channel and processed through an integrated repairer enhancer network (IREN) to generate a clean speech signal. Embodiments initially train a repairer network to ameliorate one or more of a first type of degradations (disrepair-related degradations). Embodiments then use transfer learning from the repairer network to train the IREN to ameliorate one or more of a second type of degradations (de-enhancement-related degradations). The resulting IREN is a single integrated machine learning network that ameliorates both types of degradations.

IPC Classes  ?

• G10L 21/02 - Speech enhancement, e.g. noise reduction or echo cancellation
• G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks

Abstract

A computing device may determine a current location, obtain user-context, and generate location-specific insights by applying an artificial intelligence model to the current location and user-context associated with the computing device. For instance, the computing device may, while operating in a locked mode, output a graphical indication of a location-specific insight to an always-on-display device and detect a user input at a location of the always-on-display device associated with the graphical indication. In response to detecting the user input, the computing device may execute an application associated with the at least one location-specific insight and output to a display device while operating in the unlocked mode, additional information to a graphical user interface of the application associated with the at least one location-specific insight.

IPC Classes  ?

• G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
• G06F 3/04883 - Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures for inputting data by handwriting, e.g. gesture or text
• G06F 3/04886 - Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures by partitioning the display area of the touch-screen or the surface of the digitising tablet into independently controllable areas, e.g. virtual keyboards or menus

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using a neural network. In particular, the neural network includes one or more nested mixture of experts (MoE) layers that each include a routing layer and a respective set of nested expert layer blocks.

IPC Classes  ?

• G06N 3/045 - Combinations of networks
• G06N 3/084 - Backpropagation, e.g. using gradient descent

Abstract

A method includes determining a viewpoint modification of a first viewpoint from which an input image represents a scene, and determining, based on the input image and the viewpoint modification, a reoriented image that (i) represents the scene from a second viewpoint that differs from the first viewpoint and (ii) includes a visual distortion of the scene. The visual distortion may be associated with the viewpoint modification. The method also includes processing the input image and the reoriented image using an image correction model configured to remove visual distortions associated with viewpoint modifications, and generating, using the image correction model and based on processing the input image and the reoriented image, an output image that includes a correction of at least part of the visual distortion in the reoriented image. The output image may represent the scene from the second viewpoint. The method additionally includes outputting the output image.

IPC Classes  ?

• G06T 5/60 - Image enhancement or restoration using machine learning, e.g. neural networks
• G06T 5/77 - RetouchingInpaintingScratch removal

Abstract

To allow user equipment (UE) to place a call on a cellular network, the UE first determines whether an operating mode associated with a fallback network is disabled. Based on the operating mode associated with the fallback network being disabled, the UE then transmits a call registration request that omits call function data to the cellular network. After receiving an acknowledgement from the cellular network, the UE removes data from the acknowledgement indicating which call functions are supported by the cellular network. Based on the modified acknowledgement, the UE then initiates a call on the cellular network using a predetermined call function.

IPC Classes  ?

• H04L 65/1016 - IP multimedia subsystem [IMS]
• H04L 65/1069 - Session establishment or de-establishment
• H04L 65/1073 - Registration or de-registration
• H04L 65/1104 - Session initiation protocol [SIP]
• H04W 36/00 - Handoff or reselecting arrangements
• H04W 48/18 - Selecting a network or a communication service
• H04W 60/00 - Affiliation to network, e.g. registrationTerminating affiliation with the network, e.g. de-registration
• H04W 88/06 - Terminal devices adapted for operation in multiple networks, e.g. multi-mode terminals

Abstract

A computing device is configured to determine that there is insufficient available memory in a memory of a secure element of the computing device for a first application. The computing device is further configured to identify a second application to evict from the memory of the secure element. The computing device is further configured to evict at least a portion of the second application from the memory of the secure element, where evicting the second application includes encrypting the second application in the memory of the secure element. The computing device is further configured to install the first application in the memory of the secure element. The computing device is further configured to execute the first application in the memory of the secure element.

IPC Classes  ?

• G06F 21/79 - Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure storage of data in semiconductor storage media, e.g. directly-addressable memories

Abstract

Methods and apparatus for determining a velocity of a conveyor associated with a mobile robot are provided. The method includes receiving first image data, the first image data including a first representation of a first object and a conveyor, the first image data captured at a first time, and determining by at least one hardware processor, a velocity of the conveyor based, at least in part, on the first representation of the first object in the first image data and a difference between the first time and a second time different from the first time.

IPC Classes  ?

• B65G 47/04 - Devices for feeding articles or materials to conveyors for feeding articles
• B65G 43/08 - Control devices operated by article or material being fed, conveyed, or discharged
• B65G 47/90 - Devices for picking-up and depositing articles or materials

Abstract

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for generating aligned output images. In particular, the described techniques include processing, for each target image of the output images and over a plurality of reverse diffusion steps, a respective first denoising input using a feature updating layer. The denoising input includes an input feature representation that in turn includes the feature representations of the target image and reference images. By processing the input feature representations of the target image and each of the reference images simultaneously using the feature updating layer, the system can ensure generation of style aligned output images.

IPC Classes  ?

• G06T 5/60 - Image enhancement or restoration using machine learning, e.g. neural networks
• G06T 5/70 - DenoisingSmoothing

Abstract

Methods and devices are provided to allow for the transfer of a display of a visual representation between a head mounted device and a computing device during the display of a video. A video is displayed on a computing device display of a computing device, a visual representation of a speech for an audio component of the video is received, the visual representation is displayed on the computing device display, and the display of the visual representation is transferred to the head mounted device to display on a head mounted device display upon determining that a head mounted device is in use.

IPC Classes  ?

• G06F 3/14 - Digital output to display device

Abstract

Aspects of the disclosed technology include techniques and mechanisms for restoring program states using microarchitectural scratchpads. A processor is configured to store, in a buffer, program state data which indicates a current state of microarchitectural registers therein during execution of a current program. Based on receiving a command to terminate execution of the current program and restore execution of a different program, the processor retrieves, from the buffer, the program state data associated with the program to be executed and restores the retrieved data.

IPC Classes  ?

• G06F 9/46 - Multiprogramming arrangements

Abstract

A data storage management method includes detecting a data change to data in a data repository, identifying metadata of the data change, and storing the metadata in a virtual file, the virtual file being in a data storage format that is compatible with one or more data analysis tools. In response to a subsequent user request to access metadata of the data in the data repository, the method may transmit one or more virtual files containing metadata identified in the user request.

IPC Classes  ?

• G06F 16/25 - Integrating or interfacing systems involving database management systems
• G06F 16/21 - Design, administration or maintenance of databases
• G06F 16/2457 - Query processing with adaptation to user needs

Abstract

Implementations relate to processing multi-turn dialogs each showing (1) dialog turns that correspond to user input(s) providing user intent(s) and associated parameter(s), and (2) dialog turns that correspond to input(s) from a virtual assistant (or a human agent/responder) that are responsive to the user input(s). A multi-turn dialog (e.g., a pre-processed variation thereof) can be processed, using a generative model, to generate one or more one-shot queries summarizing the user input(s) of the multi-turn dialog. Whether the generated one-shot queries accurately reflect the user intent(s) and/or the associated parameters can be verified, and only verified one-shot queries are selected to form part of a dataset. The dataset can be used, for example, for training machine learning model(s) for handling a single, complex user query and/or for validating machine learning model(s).

IPC Classes  ?

• G06F 40/30 - Semantic analysis
• G06F 40/169 - Annotation, e.g. comment data or footnotes
• G06F 40/226 - Validation

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a most accurate data source to be used to respond to a query and responding with a result using data from the selected data source. In one aspect, a method includes receiving, from a user, an input query related to user interactions with a platform for one or more users of the platform. The input query is processed to select a data source to be used for responding to the input query. The output includes a likelihood that a first result corresponding to the input query obtained using a first data source has a higher accuracy than each of one or more second results corresponding to the input query obtained using one or more second data sources. A result corresponding to the input query is obtained using the selected data source and the result is provided.

IPC Classes  ?

• G06F 16/242 - Query formulation
• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
• G06F 16/2458 - Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries

Abstract

A method can include receiving an image including a label identifying inclusion of at least one opacity artifact is received, generating a transformed semantic latent space based on the image using a linear transformation model. generating a noisy image based on the image, generating a first estimated image based on the transformed semantic latent space using a diffusion model, generating a second estimated image based on the transformed semantic latent space and the noisy image using the diffusion model, and training the linear transformation model based on the first estimated image, the second estimated image, and a loss that enforces a linear change in the linear transformation model.

IPC Classes  ?

• G06T 5/60 - Image enhancement or restoration using machine learning, e.g. neural networks
• G06T 5/70 - DenoisingSmoothing
• G06T 5/77 - RetouchingInpaintingScratch removal

Abstract

Access to an electronic document is provided to a first client device associated with a first user via a first application, with one or more entries of an approval data structure. When the first user engages with first GUI elements associated with the entries, indicating an approval request for a second user to approve portions of the content, the approval data structure is updated accordingly. The second client device is provided with access, via a second application, to the relevant content and approval data. If the second user engages with second GUI elements associated with the entries, providing a response to the approval request, a notification indicating this response is sent to the first client device.

IPC Classes  ?

• G06F 40/166 - Editing, e.g. inserting or deleting
• G06F 16/176 - Support for shared access to filesFile sharing support
• G06F 40/194 - Calculation of difference between files
• G06Q 10/101 - Collaborative creation, e.g. joint development of products or services

Abstract

According to at least one implementation, a method includes identifying a screen state associated with a first device and determining whether the screen state associated with the first device satisfies at least one criterion. In response to determining that the screen state associated with the first device satisfies the at least one criterion, the method further includes identifying touch input for a second device at the first device. In response to determining that the screen state associated with the first device fails to satisfy the at least one criterion, the method further includes identifying touch input for the first device at the first device.

IPC Classes  ?

• G06F 3/041 - Digitisers, e.g. for touch screens or touch pads, characterised by the transducing means

Abstract

Methods and apparatus for assigning a quality metric to an object to be grasped by a mobile robot are provided. The method includes receiving at least one image including a set of objects, processing the at least one image using a trained machine learning model to assign a quality metric to a first object of the set of objects in the at least one image, and controlling the mobile robot to perform an action based, at least in part, on the quality metric assigned to the first object.

IPC Classes  ?

• B25J 9/16 - Programme controls
• G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]

Abstract

A user equipment (UE) (102) in a mobile cellular network (100) implements one or more techniques to relax radio resource management mobility (RRM) actions. For example, the UE selects, based on a stationary confidence rank (214) indicating a confidence level in an assessment that the UE is in a stationary state, a set of conditions (216) for one or more stationary modes (138). The UE implements a stationary mode of the one or more stationary modes or a non-stationary mode based on the selected set of conditions. Responsive to whether the stationary mode or the non-stationary mode is implemented at the UE, the UE selectively performs one or more RRM relaxation actions (218) to relax or reduce RRM actions at the UE.

IPC Classes  ?

• H04W 64/00 - Locating users or terminals for network management purposes, e.g. mobility management
• G01S 5/00 - Position-fixing by co-ordinating two or more direction or position-line determinationsPosition-fixing by co-ordinating two or more distance determinations
• G06N 3/02 - Neural networks
• G06N 20/00 - Machine learning

Abstract

A computing device for generating, managing, or editing a document includes one or more memories to store instructions and one or more processors to execute the instructions to perform operations, the operations including: obtaining a prompt indicating an intended meaning of a document, receiving a plurality of inputs editing a first version of the document to produce a second version of the document, determining whether specified editing criteria associated with editing the document are satisfied, in response to the specified editing criteria being satisfied, implementing one or more machine-learned models to determine whether specified drift criteria associated with the intended meaning of the document is satisfied, based on the prompt, the edits to the first version of the document, and the second version of the document, and providing an output based on whether the specified drift criteria associated with the intended meaning of the document is satisfied.

IPC Classes  ?

• G06F 40/197 - Version control
• G06F 40/166 - Editing, e.g. inserting or deleting
• G06F 8/71 - Version control Configuration management
• G06F 16/18 - File system types
• G06F 16/332 - Query formulation
• G06F 21/10 - Protecting distributed programs or content, e.g. vending or licensing of copyrighted material
• G11B 27/031 - Electronic editing of digitised analogue information signals, e.g. audio or video signals
• H04N 21/854 - Content authoring
• G06F 40/30 - Semantic analysis
• G06T 11/60 - Editing figures and textCombining figures or text

Abstract

A system and related method for determining a temperature of an object via a mobile device having a camera that generates image data across a first field of view, and a temperature sensor that generates temperature data across a second field of view overlapping the first field of view. A computing system receives the image data, each of the plurality of images of the image data being associated with a different respective position of the mobile device. The computing system also receives the temperature data, each of the plurality of average temperatures of the temperature data corresponding to a respective one of the plurality of images. The computing system determines, based at least in part on the plurality of images and the plurality of average temperatures, a temperature of a desired object at least partially within the first and second fields of view.

IPC Classes  ?

• G01J 5/02 - Constructional details
• G01J 5/07 - Arrangements for adjusting the solid angle of collected radiation, e.g. adjusting or orienting field of view, tracking position or encoding angular position
• G01J 5/08 - Optical arrangements

Abstract

.. One of the methods includes receiving new inputs at an invocation by a fully convolutional network deployed for processing inputs with a fixed size. For each of the received new inputs, a group of fixed-size input tiles are determined, and each of the groups of fixed-size input tiles are provided to a hardware accelerator for generating respective fixed-size outputs of the invocation using the deployed fully convolutional network. From the respective fixed-size outputs, a respective final output of the invocation is generated for the output layer that is equivalent to an output that would be generated from the output layer by processing the new inputs at the invocation using the fully convolutional network deployed for processing the corresponding inputs with the respective sizes.

IPC Classes  ?

• G06N 3/045 - Combinations of networks
• G06N 3/0464 - Convolutional networks [CNN, ConvNet]
• G06N 3/048 - Activation functions
• G06N 3/0499 - Feedforward networks
• G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means

Abstract

Methods and systems, including computer-readable media, are described for high-throughput data decoding at an integrated circuit. A system receives addresses that specify memory locations storing encoded data at a memory device. Each of the addresses are provided by a respective decoder of the circuit. For each address: the system generates a request for an encoded block of an encoded data stream that includes multiple encoded blocks. Over multiple clock cycles, the system processes each of the requests corresponding to the addresses based on available buffer space for storing encoded data in at least one of the decoders. In response to processing each request, the system retrieves multiple encoded blocks from non-contiguous memory locations of the memory and decodes the encoded blocks based on an interleaved configuration used to generate a corresponding encoded data stream. A portion of the encoded blocks are decoded in parallel across two or more decoders.

IPC Classes  ?

• H03M 7/40 - Conversion to or from variable length codes, e.g. Shannon-Fano code, Huffman code, Morse code
• H03M 7/30 - CompressionExpansionSuppression of unnecessary data, e.g. redundancy reduction
• G06N 3/06 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons

Abstract

Described techniques capture a first image of a scene using a first device and cause a second device to capture a second image of the scene. A distance between the first device and the second device may be determined, and a spatial image of the scene may be generated using the first image, the second image, and the distance.

IPC Classes  ?

• H04N 13/344 - Displays for viewing with the aid of special glasses or head-mounted displays [HMD] with head-mounted left-right displays
• G02B 27/01 - Head-up displays
• H04N 13/111 - Transformation of image signals corresponding to virtual viewpoints, e.g. spatial image interpolation

Goods & Services

Downloadable computer software for use in processing and generating natural language queries; Downloadable computer software using artificial intelligence (AI) for the production of speech, text, images, video, sound, and code; Downloadable computer software for multi-modal machine-learning based language, text, speech, image, video, code, and sound processing software; Downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of science, engineering, mathematics, computing, art, music, language, entertainment, and general interest; Downloadable computer software for facilitating multi-modal natural language, speech, text, images, video, code and sound input; Downloadable chatbot software for simulating conversations, analyzing images, sound and video, summarizing text, creating content, generating code, brainstorming, trip planning, and answering queries; Downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; Downloadable chatbot software for providing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases Providing online non-downloadable software for use in large language models and artificial intelligence; providing online non-downloadable software using artificial intelligence for the production of human speech and text; providing online non-downloadable software for natural language processing, generation, understanding and analysis; providing online non-downloadable software for artificial intelligence and machine-learning based language and speech processing software; providing online non-downloadable software for creating generative models; providing online non-downloadable software for processing speech, text, sound, code, videos, images, and sound input; providing online non-downloadable software for generating speech, text, sound, code, videos, images, and sound output; research and development services in the field of artificial intelligence; research, development and evaluation of large language models and data sets; research, design and development of computer programs and software; providing online non-downloadable software for managing data sets and performing safety checks in the field of artificial intelligence; providing online non-downloadable software for multi-modal artificial intelligence and machine-learning based language, text, sound, code, video, image, speech, and sound processing software; providing temporary use of online non-downloadable software for facilitating multi-modal natural language, speech, text, sound, code, videos, images, and sound input; research and development services in the field of multi-modal computer natural language processing, artificial intelligence, and machine learning; providing temporary use of online non-downloadable software for an integrated development environment for large language models; providing online non-downloadable software for use in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; providing online non-downloadable software for accessing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases; application service provider featuring application programming interface (API) software

Abstract

Techniques for tuning an image editing operator for reducing a distractor in raw image data are presented herein. The image editing operator can access the raw image data and a mask. The mask can indicate a region of interest associated with the raw image data. The image editing operator can process the raw image data and the mask to generate processed image data. Additionally, a trained saliency model can process at least the processed image data within the region of interest to generate a saliency map that provides saliency values. Moreover, a saliency loss function can compare the saliency values provided by the saliency map for the processed image data within the region of interest to one or more target saliency values. Subsequently, the one or more parameter values of the image editing operator can be modified based at least in part on the saliency loss function.

IPC Classes  ?

• G06T 7/194 - SegmentationEdge detection involving foreground-background segmentation
• G06T 3/18 - Image warping, e.g. rearranging pixels individually
• G06T 7/11 - Region-based segmentation
• G06T 11/00 - 2D [Two Dimensional] image generation
• G06V 40/20 - Movements or behaviour, e.g. gesture recognition

Abstract

A method includes receiving an audio input that represents an utterance of a voice command that is preceded by a predefined hotword. The first computing device is configured to process voice commands that are preceded by the predefined hotword and is in proximity of a second computing device that is also configured to process voice commands that are preceded by the same, predefined hotword. The method also includes receiving a local area wireless signal from the second computing device. Based on receiving the local area wireless signal from the second computing device, the method also includes placing the first computing device into a sleep mode, bypassing further processing of the voice command, and bypassing outputting a visual indication that the first computing device is processing the voice command.

IPC Classes  ?

• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G10L 15/02 - Feature extraction for speech recognitionSelection of recognition unit
• G10L 15/08 - Speech classification or search
• G10L 15/26 - Speech to text systems
• G10L 15/30 - Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
• G10L 25/03 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters
• G10L 25/78 - Detection of presence or absence of voice signals
• G10L 25/87 - Detection of discrete points within a voice signal

Abstract

A method for detecting malware by modifying executable code includes identifying executable code that includes branch instructions. The method includes determining whether any of the branch instructions of the executable code mask maliciousness of the executable code. The determining includes modifying first one or more of the branch instructions of the executable code, causing execution of the executable code with the modified first one or more branch instructions in a first testing environment, and evaluating a result of the execution of the executable code with the modified first one or more branch instructions. The result can indicate whether the executable code is malicious. The method includes, responsive to determining that the branch instructions of the executable code mask the maliciousness of the executable code, performing one or more preventative actions with respect to the executable code.

IPC Classes  ?

• G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements

Abstract

A method for participation, in a virtual meeting, of an absent invited virtual meeting user includes receiving input of a first user that has been invited to participate in the virtual meeting. The input of the first user indicates an inability to attend the virtual meeting and provides first data to be discussed during the virtual meeting. The method includes causing a virtual meeting UI to be presented during the virtual meeting between multiple participants. The UI includes a UI element associated with the first data provided by the first user that is not present during the virtual meeting. The method includes generating a summary of the virtual meeting. The summary covers presentation of at least a portion of the first data during the virtual meeting. The method includes causing the summary to be accessible by a client device of the first user.

IPC Classes  ?

• H04L 12/18 - Arrangements for providing special services to substations for broadcast or conference

Abstract

A software defined vehicle (SDV) operating system may include components for executing software packages that declare unit types (e.g., interfaces) and define service units that each implement a unit type. For each unit type, there may be several service units that each provide a different implementation of that unit type. The SDV operating system may manage a service discovery module that registers service units for each unit type in a centralized registry. While executing a software package that declares a unit type, the service discovery module may fetch, from the centralized registry, an implementation of the unit type by a service unit defined by a different software package. While still executing the software package (i.e., at runtime), the SDV operating system may load a service unit defined by the software package with the fetched implementation. The SDV operating system may then execute the service unit based on the fetched implementation.

IPC Classes  ?

• G06F 9/445 - Program loading or initiating
• G06F 8/65 - Updates

Abstract

Systems, methods, and apparatus for self-evolving decoding at inference. In an aspect, operations include processing, by a Large Language Model (LLM) of N layers, an input by an inference operation of the LLM; obtaining, from the LLM, logits of an evolution layer of the LLM, the evolution layer being subsequent to a first layer of the LLM; for a plurality of layers that occur before the evolution layer, processing the logits of the layer with the logits of the evolution layer to generate an approximated gradient; based on the approximated gradient and the logits of the evolution layer, generating adjusted logits for the evolution layer; and processing the adjusted logits for the evolution layer to generate an output for the LLM.

IPC Classes  ?

• G06N 3/086 - Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
• G06N 3/04 - Architecture, e.g. interconnection topology

Abstract

Systems, methods, and apparatus for generating binaural audio waveform from mono waveform data. In an aspect, operations include generating, based on a mono waveform data and positional data, left signal data and right signal data, wherein the left signal data and the right signal data are initial estimates of perceived signals of the mono waveform based on the positional data; processing the left signal data and right signal data, based on the positional data, to generate amplitude scaled left signal data and amplitude scaled right signal data; and separately processing the amplitude scaled left signal data and the amplitude scaled right signal data by a denoising vocoder to generate left output signal data and right output signal data that together define a binaural audio waveform based on the mono waveform data.

IPC Classes  ?

• G10L 19/16 - Vocoder architecture
• G10L 19/008 - Multichannel audio signal coding or decoding using interchannel correlation to reduce redundancy, e.g. joint-stereo, intensity-coding or matrixing
• G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks

Abstract

Despite recent progress, existing frame interpolation methods still struggle with extremely high resolution images and challenging cases such as repetitive textures, thin objects, and fast motion. To address these issues, provided is a cascaded diffusion frame interpolation approach that excels in these scenarios while achieving competitive performance on standard benchmarks.

IPC Classes  ?

• G06T 3/4007 - Scaling of whole images or parts thereof, e.g. expanding or contracting based on interpolation, e.g. bilinear interpolation
• G06T 3/4076 - Scaling of whole images or parts thereof, e.g. expanding or contracting based on super-resolution, i.e. the output image resolution being higher than the sensor resolution using the original low-resolution images to iteratively correct the high-resolution images
• G06T 5/60 - Image enhancement or restoration using machine learning, e.g. neural networks
• G06T 5/70 - DenoisingSmoothing

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output temporal sequence of data elements conditioned on an input. In one aspect, a method comprises: obtaining the input, wherein the input comprises a noise input comprising a respective latent representation for each of a plurality of segments of the temporal sequence; updating, for each segment, the latent representation for the segment using a latent denoising neural network, the updating comprising, for each segment other than the first segment: obtaining a memory vector representing one or more hidden states generated by the latent denoising neural network when updating the latent representations for one or more preceding segments; updating the latent representation for the segment at each of a plurality of iterations; and generating the output temporal sequence of data elements by processing the latent representations for the plurality of segments.

IPC Classes  ?

• G06F 16/74 - BrowsingVisualisation therefor
• G06N 3/09 - Supervised learning

Abstract

The present disclosure describes systems and techniques directed to producing an all-in-focus image with a camera of a mobile device, in particular, cameras with shallow depth-of-field. User equipment includes a sensor for determining distance to an object in a camera's field-of-view. Based on a depth map of the field-of-view, a plurality of segments is inferred, each segment defining a unique focus area within the camera's field-of-view. An autofocus lens of the camera sweeps to a respective focal distance associated with each of the plurality of segments. The camera captures sample images at each focal distance swept by the autofocus lens. The user equipment produces an all-in-focus image by combining or merging portions of the captured sample images.

IPC Classes  ?

• G06T 5/50 - Image enhancement or restoration using two or more images, e.g. averaging or subtraction
• G03B 13/36 - Autofocus systems
• G06T 5/73 - DeblurringSharpening
• H04N 23/67 - Focus control based on electronic image sensor signals
• H04N 23/80 - Camera processing pipelinesComponents thereof
• H04N 23/959 - Computational photography systems, e.g. light-field imaging systems for extended depth of field imaging by adjusting depth of field during image capture, e.g. maximising or setting range based on scene characteristics

Abstract

A device or receive-circuit has a first receive-chain (Rx-chain) configured to receive and process Non-Terrestrial-Network-cellular (NTN-cellular) signals received from one or more NTN-cellular access nodes, and a second Rx-chain configured to receive and process Global Navigation Satellite System (GNSS) signals wirelessly transmitted from one or more GNSS satellites, with the first Rx-chain and the second Rx-chain being at least partially integrated with each other, including sharing at least an antenna structure, a low-noise amplifier (LNA), and an Rx signal path through the antenna structure and the LNA. Further, the device or receive circuit may include a Radio Frequency Front End (RFFE) of which the LNA is a component, and the RFFE may switch between or split apart the first and second Rx-chains for downstream processing, or the Rx-chains may be split apart after a downstream analog-to- digital converter (ADC) to help avoid signal degradation from the splitting.

IPC Classes  ?

• H04B 1/00 - Details of transmission systems, not covered by a single one of groups Details of transmission systems not characterised by the medium used for transmission
• G01S 19/36 - Constructional details or hardware or software details of the signal processing chain relating to the receiver frond end
• H04B 1/403 - Circuits using the same oscillator for generating both the transmitter frequency and the receiver local oscillator frequency
• H04B 7/185 - Space-based or airborne stations

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling the participation of generative models in a multi-agent system. One of the methods includes receiving an input for a task to be processed by a group of generative models to generate a final output for the task; and processing the input by the group of the generative models across a plurality of steps, including: for each intermediate step, obtaining context data at the intermediate step; generating based on the context data, control data for a target generative model in the group; providing the context data and the control data to the target generative model in the group; obtaining an output from the target generative model generated in response to the context data and the control data; and updating the context data for the task based on the output from the target generative model.

IPC Classes  ?

• G06N 3/045 - Combinations of networks
• G06N 3/0475 - Generative networks

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for upscaling segmentation masks for image processing. One of the methods includes generating, using a trained machine learning model, a semantic mask of a first image, wherein the semantic mask includes an image classification and a confidence value for pixels of the first image across N image classes; generating, using the image classification and confidence values for the pixels of the first image, a semantic mask subset that identifies a subset of classes for each of the pixels of the first image; generating an upscaled version of the semantic mask subset; generating a second semantic mask subset based on the upscaled version of the semantic mask subset; and processing an upscaled version of the first image using the second semantic mask subset to obtain a processed output image.

IPC Classes  ?

• G06T 5/20 - Image enhancement or restoration using local operators
• G06T 5/70 - DenoisingSmoothing

Abstract

Techniques and apparatuses are described for performing howling prevention. In example aspects, a hearable (102) includes an acoustic circuit (116). The hearable (102) employs howling prevention (124) to monitor for one or more conditions that can lead to the unintentional generation of howling (122) via the acoustic circuit (116). Upon detecting a condition, the hearable (102) appropriately configures the acoustic circuit (116) to prevent howling (122) from occurring. Using various sensing techniques, the hearable (102) can quickly detect the condition and proactively adjust a gain of the acoustic circuit (116) to maintain stability of the acoustic circuit (116) and avoid howling (122). With howling prevention (124), an overall user experience with hearables (102) is improved while supporting features such as active noise cancellation and/or a transparency mode. Furthermore, some hearables (102) can be configured to perform howling prevention (124) without the need for additional hardware.

IPC Classes  ?

• H04R 1/10 - EarpiecesAttachments therefor
• H04R 3/02 - Circuits for transducers for preventing acoustic reaction
• H04R 25/00 - Deaf-aid sets

Abstract

Methods, systems, and apparatus, including computer-readable storage media for content group generation for a content delivery campaign. Content groups are generated from a resource identifier and a description. Digital content items are created for each content group, including digital content from the resource identifier and the description, as well as new digital content items. Candidate content groups are ranked according to request coverage gain and optionally one or more other ranking criteria. Request coverage gain is a measure of how much more request coverage is gained through keywords of one content group relative to the request coverage of one or more other content groups. By ranking according to request coverage gain, the selected candidate content groups are differentiated relative to one another, capturing potential content requests that would otherwise be missed by a campaign of content groups not selected based on request coverage gain.

IPC Classes  ?

• G06F 16/906 - ClusteringClassification
• G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
• G06F 16/958 - Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking

Abstract

A user equipment (UE) selects (1101) a cell of a non-terrestrial network (NTN) that supports Internet-of-Things (loT) devices, transmits (1104), to a core network (CN) via the cell, a registration request message indicating that the UE requires an emergency messaging service (EMS), and establishes (1112), with the cell, a protocol data unit (PDU) session for the EMS.

IPC Classes  ?

• H04L 67/04 - Protocols specially adapted for terminals or networks with limited capabilitiesProtocols specially adapted for terminal portability
• H04L 67/141 - Setup of application sessions
• H04W 4/90 - Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]
• H04W 60/04 - Affiliation to network, e.g. registrationTerminating affiliation with the network, e.g. de-registration using triggered events
• H04W 76/50 - Connection management for emergency connections
• H04W 84/06 - Airborne or Satellite Networks

Abstract

An image generation method is performed by one or more data processing apparatus, and comprises: obtaining an image showing an object; generating one or more additional images related to the object; fine-tuning a machine-learned text-to-image model using one or more of the additional images; providing, to the machine-learned text-to-image model, a prompt to generate an output image showing the object, and obtaining, from the machine-learned text-to-image generation model, the output image.

IPC Classes  ?

• G06T 11/00 - 2D [Two Dimensional] image generation
• G06F 40/40 - Processing or translation of natural language
• G06T 3/40 - Scaling of whole images or parts thereof, e.g. expanding or contracting
• G06T 3/60 - Rotation of whole images or parts thereof
• G06T 13/00 - Animation
• G06T 15/20 - Perspective computation

Abstract

Methods, systems, and apparatus for classification. In one aspect, a method includes receiving an input and a request to classify the input into one of a plurality of classes, processing the input using a multimodal model to generate (i) a description of the input and (ii) a class prediction, processing the description of the input and the class prediction using a text encoder embedding neural network to generate a (i) text description feature embedding and (ii) a prediction feature embedding, generating, from at least the description feature embedding and the prediction feature embedding, a query feature embedding representing the input, and classifying the input into one of the plurality of classes using the query embedding.

IPC Classes  ?

• G06F 16/55 - ClusteringClassification

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for multi-vector retrieval via fixed dimensional encodings. In one aspect, a method includes: obtaining a set of embedding vectors of a query in an embedding vector space; obtaining an encoded dataset including, for each data item in a set of data items, a respective encoded vector of the data item in a target vector space; encoding the set of embedding vectors of the query in the embedding vector space into an encoded vector of the query in the target vector space; performing, with respect to the encoded vector of the query, a k-nearest neighbors search on the respective encoded vectors of the data items in the encoded dataset; and identifying, from the k-nearest neighbors search, a top-k subset of the set of data items.

IPC Classes  ?

• G06F 16/242 - Query formulation
• G06F 16/2455 - Query execution

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for composing machine learning models to perform new tasks.

IPC Classes  ?

• G06N 3/08 - Learning methods

Abstract

To monitor and report road quality, a server device is configured to receive, from a plurality of vehicles, respective reports, each of the reports indicating a geographic road location of a vehicle and a road quality indication for the geographic location; update, using the reports, a table correlating geographic road locations and road quality indications; determine average road quality indicia for a geographic road location, based on the road quality indications in the table; and in response to a query from a communication device, provide the communication device with an information update based on at least the average road quality indicia.

IPC Classes  ?

• G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
• B60W 40/06 - Road conditions
• B60W 50/04 - Monitoring the functioning of the control system
• G07C 5/00 - Registering or indicating the working of vehicles
• 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

Implementations relate to selecting a particular device, from an ecosystem of devices, to provide responses to a device-agnostic request of the user while a scenario is occurring. The user specifies a scenario and contextual features are identified from one or more devices of the ecosystem to generate scenario features indicative of the scenario occurring. The scenario features are stored with a correlation to a device that is specified by the user to handle responses while the scenario is occurring. When a subsequent device-agnostic request is received, current contextual features are identified and compared to the scenario features. Based on the comparison, the specified assistant device is selected to respond to the device-agnostic request.

IPC Classes  ?

• G06F 16/9032 - Query formulation
• G06F 16/9038 - Presentation of query results
• G06F 40/30 - Semantic analysis

Abstract

A system, such as a voice assistant device, is disclosed which includes a base that houses at least one speaker and supports a display screen. The base is configured to hold the display screen at an angle relative to a surface, creating a predefined space between the screen's lower edge and the surface. To optimize sound, multiple speakers can be oriented in different directions, with one speaker potentially facing a front grille while another is aimed in another direction behind the display. The system may further integrate a camera and a radar transceiver within the bezel of the display screen.

IPC Classes  ?

• G06F 1/16 - Constructional details or arrangements
• G02F 1/1333 - Constructional arrangements
• G02F 1/1337 - Surface-induced orientation of the liquid crystal molecules, e.g. by alignment layers
• G06F 3/16 - Sound inputSound output
• G06F 21/83 - Protecting input, output or interconnection devices input devices, e.g. keyboards, mice or controllers thereof
• G10L 15/28 - Constructional details of speech recognition systems
• H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
• H04R 1/02 - CasingsCabinetsMountings therein
• H04R 1/34 - Arrangements for obtaining desired frequency or directional characteristics for obtaining desired directional characteristic only by using a single transducer with sound reflecting, diffracting, directing or guiding means

Abstract

An example cloud-based voice interconnect system includes data processing hardware of a cloud-based computing platform, a network, and a public telecom carrier system. The data processing hardware is in communication with memory hardware storing instructions that, when executed on the data processing hardware, cause the data processing hardware to perform operations including providing a private virtualized computing environment, and implementing a private cloud-based session border controller (SBC) in the virtualized computing environment. The public telecom carrier system is connected to the private cloud-based SBC via the network, and is configured to provide telecom services between the private cloud-based SBC and customers of the public telecom carrier system.

IPC Classes  ?

• H04M 7/00 - Arrangements for interconnection between switching centres
• G06F 9/455 - EmulationInterpretationSoftware simulation, e.g. virtualisation or emulation of application or operating system execution engines
• H04L 65/1104 - Session initiation protocol [SIP]
• H04L 67/10 - Protocols in which an application is distributed across nodes in the network

Abstract

A software-based extension of the instruction set of a processor includes instructions for the processor to prefetch virtual address translations and insert the prefetched translations into a translation lookaside buffer (TLB). A page walk may be performed to find a virtual address in a group of page tables and provide the address translation to the TLB. The TLB may be arranged in multiple levels and the instructions may specify a level for the prefetched entry to be inserted. The instruction may provide a hint to the processor for selecting candidate virtual address for prefetch based on a characteristic of an address such as a likelihood of reuse, a priority level of the data in the virtual address or other characteristic. A page walk can be performed asynchronously without affecting normal operations of a program. Instructions may specify between an instruction a data TLB for insertion of a new TLB entry.

IPC Classes  ?

• G06F 12/1027 - Address translation using associative or pseudo-associative address translation means, e.g. translation look-aside buffer [TLB]
• G06F 12/0811 - Multiuser, multiprocessor or multiprocessing cache systems with multilevel cache hierarchies
• G06F 12/0862 - Addressing of a memory level in which the access to the desired data or data block requires associative addressing means, e.g. caches with prefetch

Abstract

To reduce the time needed to display an answer to a prompt received at a head-wearable device (HWD), the HWD includes an edge large-language (LLM) model implemented at the HWD. Based on the prompt, the HWD generates tokens and edge answers using the edge LLM. In response to one or more of the tokens being a delegation token and concurrently with displaying the edge answer, the HWD transmits token embeddings of the tokens to a server implementing an extended LLM. The HMD then displays a hybrid answer including the edge answer and the extended answer.

IPC Classes  ?

• G02B 27/01 - Head-up displays
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

This document describes systems and techniques directed at semiconductor fault detection. In aspects, a semiconductor device includes a physical structure that facilitates detection and localization of defects. The physical structure includes at least one conductive interconnect that extends through two or more layers of a semiconductor device, enabling an electrical detection of faults. Such systems and techniques can help improve yield, accelerate failure analysis debugging, and improve reliability of semiconductor devices.

IPC Classes  ?

• G01R 31/26 - Testing of individual semiconductor devices
• H01L 23/485 - Arrangements for conducting electric current to or from the solid state body in operation, e.g. leads or terminal arrangements consisting of lead-in layers inseparably applied to the semiconductor body consisting of layered constructions comprising conductive layers and insulating layers, e.g. planar contacts
• H01L 23/528 - Layout of the interconnection structure

Abstract

A method of semantic-based image copying includes generating a text prompt. Generating the text prompt includes by applying a source image to a first generative artificial intelligence (Al) model to generate a descriptive caption for the source image. The method also includes generating a visual embedding based on the source image, and generating a new image using a second generative Al model and based on the text prompt and the visual embedding.

IPC Classes  ?

• G06T 11/00 - 2D [Two Dimensional] image generation

Abstract

A method efficiently restructures account data indicative a first plurality of keywords each mapped to a respective query space, a first plurality of campaigns, and associations therebetween. The method includes consolidating the first plurality of campaigns into a smaller, second plurality of campaigns, based on a degree of overlap between respective query spaces to which keywords associated with different campaigns are mapped. The method also includes generating a second plurality of keywords consisting of a subset of the first plurality of keywords, which includes, for each campaign in the second plurality of campaigns, determining whether to remove associations to particular keywords based on an incremental value added by the query spaces that are mapped to those keywords. The method also includes storing restructured account data indicative of the second plurality of keywords, the second plurality of campaigns, and new associations therebetween.

IPC Classes  ?

• G06Q 30/0242 - Determining effectiveness of advertisements
• G06Q 30/0241 - Advertisements

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automated layout generation by an artificial intelligence system. Methods can include obtaining two or more discrete units of content. Based on the two or more discrete units of content a new layout is generated in a canvas. The layout generation can include: generating a bounding box as a presentation space for each given unit of content; generating positioning data specifying locations within the canvas at which each bounding box is located; assigning each bounding box to a corresponding user interface layer; and generating a compressed text representation of the new layout. The new layout can be rendered based on the text representation.

IPC Classes  ?

• G06F 8/38 - Creation or generation of source code for implementing user interfaces
• G06F 9/451 - Execution arrangements for user interfaces
• G06N 3/045 - Combinations of networks

Abstract

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for training a student language model neural network for deployment in a cascade with a teacher neural network. That is, by training a student neural network using techniques that incorporate the difficulty of accurately predicting the target token for each output position of a target output of a training example for each training example for both the student and the teacher language model neural networks, the described techniques result in a student teacher cascade with higher overall task performance per unit of computational cost.

IPC Classes  ?

• G06N 3/045 - Combinations of networks
• G06N 3/0475 - Generative networks
• G06N 3/096 - Transfer learning

Abstract

To reduce the time needed to display an answer to a prompt received at a head-wearable device (HWD) or for other reasons, the HWD includes an edge large-language (LLM) model implemented at the HWD. Based on the prompt, the HWD generates tokens and edge answers using the edge LLM. In response to one or more of the tokens being a delegation token and concurrently with displaying the edge answer, the HWD transmits token embeddings of the tokens to a server implementing an extended LLM. The HMD then displays a hybrid answer including the edge answer and the extended answer.

IPC Classes  ?

• G06N 3/045 - Combinations of networks
• G06F 3/01 - Input arrangements or combined input and output arrangements for interaction between user and computer

Abstract

A method for optimizing file storage includes receiving columnar data to store at a columnar data store with columns ordered with an initial ordering. The method includes determining, based on historical access patterns for the columnar data store, an updated ordering for the columns. The method includes storing the columnar data at a first location of the columnar data store using the updated ordering. The method includes determining that the stored columnar data is to be compacted and compressing at least a portion of the columnar data using each of a plurality of compression techniques. The method includes, based on compressing the at least a portion of the columnar data, selecting one of the plurality of compression techniques. The method includes storing the columnar data at a second location of the columnar data store using the selected one of the plurality of compression techniques.

IPC Classes  ?

• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models

Abstract

Implementations described herein relate to enabling natural language communications with an autonomous vehicle. In some implementations, processor(s) of a system can initiate and conduct a conversation with a remote communication participant that is located remotely from the autonomous vehicle whereas, in additional or alternative implementations, the processor(s) can answer an incoming electronic communication and conduct a conversation with a remote communication participant that is located remotely from the autonomous vehicle. In other additional or alternative implementations, the processor(s) can also conduct conversations with a local communication participant that is located proximate to the autonomous vehicle. Notably, the processor(s) can be implemented locally at the autonomous vehicle or remotely from the autonomous vehicle (e.g., at a remote server).

IPC Classes  ?

• H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
• G10L 13/00 - Speech synthesisText to speech systems
• H04W 4/40 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]

Goods & Services

Providing computer security training and educational testing services in a simulated enterprise network environment, featuring virtualized hardware, software, and security elements, including simulated cloud services across multicloud environments and AI components like large language models (LLMs) and AI agents; Organizing computer security competitions in a simulated enterprise network environment, featuring virtualized hardware, software, and security elements, including simulated cloud services across multicloud environments and AI components like large language models (LLMs) and AI agents; Providing training in the field of computer network attack, defense, response and investigation. Providing computer security consulting services in the field of computer network attack, defense, response and investigation; Computer programming services for developing a simulated enterprise network environment, featuring virtualized hardware, software, and security elements, including simulated cloud services across multicloud environments and AI components like large language models (LLMs) and AI agents; Computer security threat detection and analysis for protecting data provided in a simulated enterprise network environment, featuring virtualized hardware, software, and security elements, including simulated cloud services across multicloud environments and AI components like large language models (LLMs) and AI agents.

Goods & Services

(1) Downloadable computer software for use in processing and generating natural language queries; downloadable computer software using artificial intelligence (AI) for the production of speech, text, images, video, sound, and code; downloadable computer software for multi-modal machine-learning based language, text, speech, image, video, code, and sound processing software; downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of science, engineering, mathematics, computing, art, music, language, entertainment, and general interest; downloadable computer software for facilitating multi-modal natural language, speech, text, images, video, code and sound input; downloadable chatbot software for simulating conversations, analyzing images, sound and video, summarizing text, creating content, generating code, brainstorming, trip planning, and answering queries; downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; downloadable chatbot software for providing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases. (1) Providing online non-downloadable software for use in large language models and artificial intelligence; providing online non-downloadable software using artificial intelligence for the production of human speech and text; providing online non-downloadable software for natural language processing, generation, understanding and analysis; providing online non-downloadable software for artificial intelligence and machine-learning based language and speech processing software; providing online non-downloadable software for creating generative models; providing online non-downloadable software for processing speech, text, sound, code, videos, images, and sound input; providing online non-downloadable software for generating speech, text, sound, code, videos, images, and sound output; research and development services in the field of artificial intelligence; research, development and evaluation of large language models and data sets; research, design and development of computer programs and software; providing online non-downloadable software for managing data sets and performing safety checks in the field of artificial intelligence; providing online non-downloadable software for multi-modal artificial intelligence and machine-learning based language, text, sound, code, video, image, speech, and sound processing software; providing temporary use of online non-downloadable software for facilitating multi-modal natural language, speech, text, sound, code, videos, images, and sound input; research and development services in the field of multi-modal computer natural language processing, artificial intelligence, and machine learning; providing temporary use of online non-downloadable software for an integrated development environment for large language models; providing online non-downloadable software for use in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; providing online non-downloadable software for accessing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases; application service provider featuring application programming interface (API) software.

Goods & Services

Downloadable computer software for use in processing and generating natural language queries; Downloadable computer software using artificial intelligence (AI) for the production of speech, text, images, video, sound, and code; Downloadable computer software for multi-modal machine-learning based language, text, speech, image, video, code, and sound processing software; Downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of science, engineering, mathematics, computing, art, music, language, entertainment, and general interest; Downloadable computer software for facilitating multi-modal natural language, speech, text, images, video, code and sound input; Downloadable chatbot software for simulating conversations, analyzing images, sound and video, summarizing text, creating content, generating code, brainstorming, trip planning, and answering queries; Downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; Downloadable chatbot software for providing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases. Providing online non-downloadable software for use in large language models and artificial intelligence; providing online non-downloadable software using artificial intelligence for the production of human speech and text; providing online non-downloadable software for natural language processing, generation, understanding and analysis; providing online non-downloadable software for artificial intelligence and machine-learning based language and speech processing software; providing online non-downloadable software for creating generative models; providing online non-downloadable software for processing speech, text, sound, code, videos, images, and sound input; providing online non-downloadable software for generating speech, text, sound, code, videos, images, and sound output; research and development services in the field of artificial intelligence; research, development and evaluation of large language models and data sets; research, design and development of computer programs and software; providing online non-downloadable software for managing data sets and performing safety checks in the field of artificial intelligence; providing online non-downloadable software for multi-modal artificial intelligence and machine-learning based language, text, sound, code, video, image, speech, and sound processing software; providing temporary use of online non-downloadable software for facilitating multi-modal natural language, speech, text, sound, code, videos, images, and sound input; research and development services in the field of multi-modal computer natural language processing, artificial intelligence, and machine learning; providing temporary use of online non-downloadable software for an integrated development environment for large language models; providing online non-downloadable software for use in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; providing online non-downloadable software for accessing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases; application service provider featuring application programming interface (API) software.

Goods & Services

Integrated circuits; computer hardware; computer accelerator boards; integrated circuit cards and components; recorded software for accelerating the design and development of machine learning, data analysis, learning algorithms sold as a component of computer hardware and integrated circuits; recorded software for implementing a computer programming language sold as a component of computer hardware and integrated circuits; recorded and downloadable software development tools for facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning sold as a component of computer hardware and integrated circuits. Providing online non-downloadable software for implementing a computer programming language; providing online non-downloadable software for accelerating the design and development of machine learning, data analysis, learning algorithms; providing online non-downloadable software development tools for use in the fields of facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning.

Goods & Services

Integrated circuits; computer hardware; computer accelerator boards; integrated circuit cards and components; recorded software for accelerating the design and development of machine learning, data analysis, learning algorithms sold as a component of computer hardware and integrated circuits; recorded software for implementing a computer programming language sold as a component of computer hardware and integrated circuits; recorded and downloadable software development tools for facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning sold as a component of computer hardware and integrated circuits. Providing online non-downloadable software for implementing a computer programming language; providing online non-downloadable software for accelerating the design and development of machine learning, data analysis, learning algorithms; providing online non-downloadable software development tools for use in the fields of facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning.

Goods & Services

(1) Integrated circuits; computer hardware; computer accelerator boards; integrated circuit cards and components; recorded software for accelerating the design and development of machine learning, data analysis, learning algorithms sold as a component of computer hardware and integrated circuits; recorded software for implementing a computer programming language sold as a component of computer hardware and integrated circuits; recorded and downloadable software development tools for facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning sold as a component of computer hardware and integrated circuits (1) Providing online non-downloadable software for implementing a computer programming language; providing online non-downloadable software for accelerating the design and development of machine learning, data analysis, learning algorithms; providing online non-downloadable software development tools for use in the fields of facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning