In example embodiments, a dataset labeling software process combines diversity selection and with one or more uncertainty selection techniques in a multi-stage process that produces a labeled training dataset for training an image-based ML model. In an initial stage, diversity selection alone may be used to seed a labeled training dataset. In a subsequent stage, diversity selection may be combined with a first procedure for computing uncertainty (e.g., uncertainty selection based on ensemble learning) to build the labeled training dataset until a first stopping condition is met. Optionally, in a still further stage, diversity selection may be combined with a second, different procedure for computing uncertainty (e.g., uncertainty selection based on Monte Carlo (MC) dropout) to further build the labeled training dataset until a second stopping condition is met.
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
2.
Semantic multimodal search of engineering documents
In example embodiments, a semantic multimodal search function is provided in an engineering application for searching engineering documents. In an indexing phase, the application indexes a library of engineering documents to build an embedding database. For each of the engineering documents, a region detector extracts regions that each correspond to a different mode of technical or engineering data, and a set of ML models is used to generate embeddings that represent the semantic significance of technical or engineering data in each of the regions. In a query phase, the application receives search input and uses the region detector to extract regions that each correspond to a different mode of input. The application uses the ML models to generate embeddings that represent the semantic significance of input in each of the regions, and then performs a vector search between these embeddings and those in the embedding database.
In example embodiments, techniques are provided for customizing a ML model for a specific user absent user-coding. The techniques may provide a “black box” service to the user where the intricacies of ML model training are abstracted, and the user simply provides source data and makes high level selections. The techniques may be used with a variety of types of ML model architectures and ML Pipelines.
In various example embodiments, model calibration techniques are used to identify one or more external structural loads on a structure. Initially, material or geometry attributes of elements of a model are adjusted to minimize discrepancy between measured responses and modeled responses. Thereafter, the model is used to search for external structural loads that minimize discrepancy between the measured responses and the modeled responses. Discrepancy may be measured by an error function that looks to dynamic responses and/or static responses. A solution that minimizes discrepancy may be determined using a genetic algorithm that generates a set of proposed external structural loads, applies the set to the model to produce the modeled responses, computes an error function that measures the difference between the measured responses and the modeled responses, and evolves the solutions to minimize an error produced by the error function, with such operation proceeding until a stopping criteria is reached.
In example embodiments, a visualization application uses geographically relevant style images as guidance to automatically generate realistic 2D renders of a 3D infrastructure model. The application generates a synthetic render of the 3D infrastructure model and retrieves a set of style images that correspond to a geographics position associated with the model. The synthetic 2D render, the set of style images and, optionally, one or more user-provided text guidance phrases and/or mask images are applied to a realistic 2D render generator of the application. The realistic 2D render generator performs image translation (guided by the optional text guidance phrases and/or mask images) to adjust visual appearance of the infrastructure in the synthetic 2D render based on the visual appearance of the set of style images and to generate realistic context based on what appears in the set of style images, thereby producing a realistic 2D render.
In example embodiments, a component management service identifies related components using a combination of ML model geometry-based classification and text-based classification. The component management service accesses a plurality of components, wherein each component is associated with a geometry mesh and textual metadata. It classifies each component based on geometric similarity by providing the geometry mesh as input to one or more ML models. A geometry classification confidence score is produced for each classification. The component management service also classifies each component based on textual similarity by providing the textual metadata as input to the one or more ML models. A textual classification confidence score is produced for each classification. It calculates an overall confidence score for each classification by combining the geometry classification confidence score and the textual metadata classification confidence score. It displays components together that have an overall confidence score that exceeds a threshold for a same classification.
G06F 30/20 - Design optimisation, verification or simulation
G06F 9/451 - Execution arrangements for user interfaces
G06F 16/383 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
G06F 18/22 - Matching criteria, e.g. proximity measures
In example embodiments, a hybrid classification/anomaly machine learning architecture is provided that combines a classification model and an anomaly model to perform an engineering task. The classification model and the anomaly model may be used in parallel, and their inference results compared, with their consistency used to improve confidence, and their inconsistency used to detect when additional training or other improvement is required and to capture data useful in such additional training/improvement.
In example embodiments, a proprietary implementation of tile-based and/or non-tile-based graphics processing is provided at run-time as a binary for use in an open-source infrastructure modeling platform. An open-source repository from which the infrastructure modeling platform is built includes a backend module and definitions of the interface of a visualization module from a private repository that implements the tile-based and/or non-tile-based processing. The open-source repository lacks code for the underlying implementation of the visualization module. The implementation of the visualization module is instead maintained in a private repository and built therefrom. In operation, the backend module calls an exposed function of a DLL that returns a pointer to a binary implementation of the visualization module. When graphics of an infrastructure model are required, the backend module utilizes the defined interface with the pointer to request tile-based and/or non-tile-based processing from the visualization module.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Cloud computing featuring software for connecting, operating, and managing sensors in the internet of things (IoT); cloud computing featuring software for managing sensor data; cloud computing featuring software for monitoring infrastructure assets; cloud computing featuring software for acquiring, organizing and managing sensor data; providing temporary use of online non-downloadable software for visualizing and analyzing sensor data; providing temporary use of online non-downloadable software for monitoring infrastructure assets; providing temporary use of online non-downloadable software for creating digital twins; providing temporary use of online non-downloadable software for providing reports and alarms based on sensor data; software as a service (SAAS), namely, hosting software for use by others for connecting, operating, and managing sensors in the internet of things (IoT); software as a service (SAAS), namely, hosting software for use by others for managing sensor data; software as a service (SAAS), namely, hosting software for use by others for monitoring infrastructure assets.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Cloud computing featuring software for connecting, operating, and managing sensors in the internet of things (IoT); cloud computing featuring software for managing sensor data; cloud computing featuring software for monitoring infrastructure assets; cloud computing featuring software for acquiring, organizing and managing sensor data; providing temporary use of online non-downloadable software for visualizing and analyzing sensor data; providing temporary use of online non-downloadable software for monitoring infrastructure assets; providing temporary use of online non-downloadable software for creating digital twins; providing temporary use of online non-downloadable software for providing reports and alarms based on sensor data; software as a service (SAAS), namely, hosting software for use by others for connecting, operating, and managing sensors in the internet of things (IoT); software as a service (SAAS), namely, hosting software for use by others for managing sensor data; software as a service (SAAS), namely, hosting software for use by others for monitoring infrastructure assets
11.
Techniques for extracting links and connectivity from schematic diagrams
In example embodiments, techniques are provided for using a combination of multiple ML models and signal processing to extract links and connectivity from a schematic diagram in an image-only format. A first ML model (i.e. link segmenter) may produce a first set of predictions about the positions of link segments in the schematic diagram (e.g., in the form of a segmentation map). A second ML model (i.e. keypoint detector) may produce a second set of predictions about starting and stopping points of link segments in the schematic diagram (e.g., in the form of one or more heatmaps). A signal processing module may combine the first set of predictions and the second set of predictions to produce a description of links and connectivity they provide (e.g., combining the segmentation map with data from the one or more heatmaps). The results of the combining may be saved as a graph connectivity matrix.
In example embodiments, anomaly and change detection software of a cloud-based design review service is provided for detecting anomalies and/or changes in 3D roadway models. The software analyzes the constituent meshes of the 3D roadway model that represent components and extracts template drops at locations along a horizontal alignment to produce an ordered list of template drops. The software then looks to differences in depths, widths, cross slopes and/or other geometric properties manifest in individual template drops, or between preceding/subsequent template drops of the ordered list, to detect anomalies and/or changes. Indications of the components associated with the detected anomalies and/or changes are displayed in a visualization of the 3D roadway model in a user interface.
In example embodiments, a proprietary implementation of solid modeling is provided at run-time as a binary to an open-source infrastructure modeling platform. The binary includes functionality of a solid modeling module that manipulates and uses BReps that represent geometry of elements of an infrastructure model. When an application that utilizes the open-source infrastructure modeling platform requires solid modeling, it may have a backend module call an exposed function of a DLL that returns a pointer to the binary. The backend module uses the pointer to create a session, which may be divided into a number of individual partitions that each correspond to one of its individual threads. BReps may be assigned to individual partitions. When a thread requires BReps to be manipulated and/or used, the corresponding partition may be used to acquire the needed BReps, perform the solid modeling operations, and either return results or an error.
In various example embodiments, techniques are provided for detecting surface corrosion on infrastructure. A training dataset that includes images of infrastructure that have at least some surface corrosion is received. A red-green-blue (RGB) color channel-based classifier is optimized, wherein the optimizing selects one or more color indices for pixels and determines one or more bounds for each of the selected color indices that indicate a pixel is a corrosion pixel or non-corrosion pixel. The optimized RGB color channel-based classifier is applied to the images to label corrosion segments in the images and produce a labeled training dataset with labeled corrosion segments. The labeled training dataset is used to train a semantic deep learning model to enable the semantic deep learning model to detect corrosion segments.
In example embodiments, techniques are provided for determining next command recommendations using a trained recurrent neural network model. A command prediction module of an application gathers command data and user characteristic data for a user, and cleans the command data to produce an input dataset. The command prediction module applies the input dataset to a trained recurrent neural network model, where the trained recurrent neural network model is configured to produce a separate next command prediction for each of a plurality of different values of one or more user characteristics. The command prediction module selects one or more recommended next commands from within the next command prediction produced for a value of one or more user characteristics that correspond to the user characteristic data for the user, and provides the one or more recommended next commands for display in a user interface of the application.
In an example embodiment, software employs a combinational optimization algorithm to automatically add supports to an infrastructure model to both meet design requirements and minimize a criteria. The combinational optimization algorithm searches a massive search space while progressively reducing its size with valid solutions found by splitting the search space into a number of bins where each bin covers a different range of values of the criteria up to a maximum possible value, generating possible solutions for the bins, verifying possible solutions to ensure they satisfy design requirements, and updating the search space based on possible solutions by decreasing the maximum possible value based on valid solutions, and excluding tested invalid solutions from future generating. The process is repeated until a stopping condition is met yielding a final valid solution and supports are added to the infrastructure model of the types and at the locations indicated therein.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06F 30/20 - Design optimisation, verification or simulation
17.
COMBINATIONAL OPTIMIZATION OF SUPPORTS GIVEN DESIGN REQUIREMENTS
In an example embodiment, software employs a combinational optimization algorithm to automatically add supports to an infrastructure model to both meet design requirements and minimize a criteria. The combinational optimization algorithm searches a massive search space while progressively reducing its size with valid solutions found by splitting the search space into a number of bins where each bin covers a different range of values of the criteria up to a maximum possible value, generating possible solutions for the bins, verifying possible solutions to ensure they satisfy design requirements, and updating the search space based on possible solutions by decreasing the maximum possible value based on valid solutions, and excluding tested invalid solutions from future generating. The process is repeated until a stopping condition is met yielding a final valid solution and supports are added to the infrastructure model of the types and at the locations indicated therein.
In various example embodiments, techniques are provided for updating content of a markup language document. A software process receives a markup language document having one or more sections and a corresponding enhancement document that includes a plurality of commands describing updates to the markup language document. The software process converts the markup language document into a first hierarchical graph in which each section of the markup language document is arranged as a parent of any subsections of the respective section. The software process also converts the enhancement document into a second hierarchical graph including one or more of the commands. The software process merges the first hierarchical graph and the second hierarchical graph, the merging to apply commands of the second hierarchical graph to the first hierarchical graph to produce an updated hierarchical graph. The software process then outputs an updated markup language document based on the updated hierarchical graph.
In example embodiments, a superelevation tool extracts and displays superelevation data from a 3D roadway model by accessing roadway meshes and a horizonal alignment from the 3D roadway model, extracting a plurality of template drops from the one or more roadway meshes at locations along the horizonal alignment to produce an ordered list of template drops and processing the template drops of the ordered list to identify top-facing roadway edges in each template drop that represent top pavement surface of the roadway at the location of the template drop, iteratively searching for a superelevation candidate and detecting superelevation data from the superelevation candidate at least in part by comparing cross-slopes of the top-facing roadway edges of consecutive template drops in the ordered list, wherein a superelevation candidate includes at least two or more template drops having cross-slopes that are locked, and providing a visualization of the detected superelevation data.
In one embodiment, a technique for numerical integration in material point method (MPM)-based geotechnical analysis and simulation is provided. Input terms for an element of a background mesh are received. The input terms including material points in the element that describe a continuum of soil, rock and/or groundwater. A set of constraints is created that defines an optimization problem. The set of constraints provide that numerical integration of the material points weighted by unknown integration weights equal exact integration for finite element shape functions. The optimization problem defined by the constraints is solved by an optimization algorithm to minimize numerical integration error for polynomials up to a given order to produce a set of integration weights. The set of integration weights is scaled to conserve the mass of the material points to produce optimized integration weights. The optimized integration weights are used in numerical integration performed in MPM-based geotechnical analysis and simulation.
Techniques are provided for presenting a unified digital deliverable (UDD) of an infrastructure. An assemblage of the infrastructure may include at least one plan sheet of the infrastructure and a three-dimensional (3D) model of the infrastructure. An object from a perspective of the infrastructure may be selected. Based on the selection, a processor may (1) present the UDD of the infrastructure, (2) navigate to different perspectives of the infrastructure included in the UDD, and/or (2) present data that corresponds to the selected object and that is maintained with the UDD. For example, an object may be selected from a plan sheet. A processor may present the UDD of the infrastructure. The UDD may include selectable objects corresponding to the infrastructure such that a user can select an object from the UDD to navigate through different perspectives and investigate and learn about the planning, design, constructions, and/or operation of the infrastructure.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
22.
SYSTEMS, METHODS, AND MEDIA FOR PRESENTING A UNIFIED DIGITAL DELIVERABLE OF AN INFRASTRUCTURE
Techniques are provided for presenting a unified digital deliverable (UDD) of an infrastructure. An assemblage (210) of the infrastructure may include at least one plan sheet of the infrastructure and a three-dimensional model of the infrastructure. An object from a perspective of the infrastructure may be selected (215). Based on the selection, a processor may present the UDD of the infrastructure (245), navigate to different perspectives of the infrastructure included in the UDD, and/or present data that corresponds to the selected object and that is maintained with the UDD. A processor may present the UDD of the infrastructure. The UDD may include selectable objects corresponding to the infrastructure.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
In example embodiments, a new model for modeling monopiles in is provided that, in addition to distributed lateral load along the monopile, considers distributed moment along the length of the pile, base moment at the pile tip, and base shear force at the pile tip. The new model may avoid the overly conservative designs for large diameter piles (e.g., 10 m+) with small length-to-diameter ratios (e.g., <6), while using standardized reaction curves (i.e., p-y curves and t-z curves) and considering axial and combined loading.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
neural network modelneural network model, where the trained recurrent neural network model is configured to produce a separate next command prediction for each of a plurality of different values of one or more user characteristics. The command prediction module selects one or more recommended next commands from within the next command prediction produced for a value of one or more user characteristics that correspond to the user characteristic data for the user, and provides the one or more recommended next commands for display in a user interface of the application.
G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
In example embodiments, improved techniques are provided for classifying elements of an infrastructure model that represents linear infrastructure (e.g., roads). The techniques may extract a set of cross sections perpendicular to a centerline of the linear infrastructure from the infrastructure model, generate a graph representation of each cross section to produce a set of graphs having nodes that represent elements and edges that represent contextual relationships, provide the set of graphs to a trained graph neural network (GNN) model, and produce therefrom class predictions for the elements. The class predictions may include one or more predicted classes for each element with a respective confidence. A best predicted class for each element may be selected and assigned to the element, thereby creating a new version of the infrastructure model. For elements that extend through multiple cross sections, the selection may involve aggregating predicted classes originating from the different graphs.
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
In example embodiments, improved techniques are provided for classifying elements of an infrastructure model that represents linear infrastructure (e.g., roads). The techniques may extract a set of cross sections perpendicular to a centerline of the linear infrastructure from the infrastructure model, generate a graph representation of each cross section to produce a set of graphs having nodes that represent elements and edges that represent contextual relationships, provide the set of graphs to a trained graph neural network (GNN) model, and produce therefrom class predictions for the elements. The class predictions may include one or more predicted classes for each element with a respective confidence. A best predicted class for each element may be selected and assigned to the element, thereby creating a new version of the infrastructure model. For elements that extend through multiple cross sections, the selection may involve aggregating predicted classes originating from the different graphs.
G06N 3/04 - Architecture, e.g. interconnection topology
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
27.
Systems, methods, and media for near real-time anomaly event detection and classification with trend change detection for smart water grid operation management
Techniques are provided for near real-time anomaly event detection and classification with trend change detection for smart water grid operation management. In the first phase, a trend change is detected in each of one or more sensors by comparing new sensor data of a sensor with a historical trend pattern of the same sensor. In the second phase, and after the trend changes are detected, a valid event evaluation time window can be determined based on combining and analyzing the detected trend changes for flow and pressure sensors, e.g., at least one flow sensor and at least one pressure sensor from the same supply zone of the smart water grid. The valid event evaluation time window can be used with anomaly events that are detected in near-real time to classify the anomaly events in near-real time as valid, e.g., true anomaly events, or invalid, e.g., false alarms.
In one embodiment, material points are received that cover at least a portion of an element of a background mesh that describes a continuum of soil, rock and/or groundwater. MPM-based geotechnical analysis and simulation is conducted at least in part by performing a numerical integration over the material points to produce a system matrix and right-hand side vector. The numerical integration applies hierarchical shape functions to the material points. The MPM-based geotechnical analysis and simulation also may subtract out contributions of any lower-order polynomials from higher-order polynomials of the hierarchical shape functions when interpolating one or more state variables for the material points to the background mesh. The MPM-based geotechnical analysis and simulation also may subtract out contributions any lower-order polynomials from higher-order polynomials of the hierarchical shape functions when calculating one or more boundary conditions for the material points.
In example embodiments, a user interface of a software application is provided for visualizing high-dimensional datasets, which simultaneously displays marginal distributions and joint distributions of variables that represent different attributes (e.g., properties) of entities (e.g., elements of infrastructure). The marginal distributions and joint distributions are combined into a single visualization that may be shown in a single window of the application. The visualization may include a graph (e.g., a bar chart) for each of the variables showing the marginal distribution of the variable, wherein each graph is displayed along a different portion of a perimeter of a closed shape (e.g., a circle). The visualization may also include graphics (e.g., lines) connecting portions of the bar charts showing the joint distribution for possible pairs of variables, wherein each graphic (e.g., line) is displayed with visual properties (e.g., a thickness) that indicates co-occurrence frequency of values of the variables.
In an example embodiment, a structural health monitoring software application provides non-contact structural health monitoring using a video of a structure captured by a video camera. The application selects an area of interest and divides the area of interest into a grid of cells. One or more machine vision algorithms are selected from a set of multiple machine vision algorithms provided by the application, wherein the set of multiple machine vision algorithms includes at least one phase-based algorithm and at least one template matching algorithm. The application applies the one or more machine vision algorithms to the video of the structure to determining a displacement of each cell, detects defects or damage based on differences in the displacement of cells, and displays an indicator of the detected defects or damage in a user interface.
G06F 18/22 - Matching criteria, e.g. proximity measures
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 20/40 - ScenesScene-specific elements in video content
In example embodiments, techniques are described for implementing serverless property stores to hold properties that persist application customization data, such as settings. A serverless property store employs an "edge base" paradigm, wherein an edge computing device executes a property store service that maintains a local, periodically-synchronized copy of a portion of a database that stores properties (i.e., a local property cache"). A cloud container of a blob storage service of a cloud datacenter maintains a master copy of the database (i.e., a "property store database"). Read operations on a client computing device may be performed against the local property cache. Write operations may likewise be performed against the local property cache, however, they may be serialized via a write lock maintained in the cloud container. Multiple serverless property stores may be employed to store different properties each having different scopes.
In one embodiment, a technique for providing absolute orientation of a mobile device involves aligning the orientation sensor of the mobile device with the Earth's coordinate system. An initial orientation from the orientation sensor of the mobile device is accessed, a user is prompted to move the mobile device in a direction of the initial orientation, and using a position sensor, a set of position information that describes positions of the mobile device is captured while the mobile device is being moved in the direction. Based on the set of position information, a vector of movement is determined. Using the vector of movement, an orientation difference is calculated between the Earth's coordinate system and a coordinate system of the mobile device. Upon demand, an absolute orientation of the mobile device may be produced by accessing a live orientation from the orientation sensor and adding the orientation difference.
G01C 21/00 - NavigationNavigational instruments not provided for in groups
G01C 17/00 - CompassesDevices for ascertaining true or magnetic north for navigation or surveying purposes
G01C 21/08 - NavigationNavigational instruments not provided for in groups by terrestrial means involving use of the magnetic field of the earth
G01C 21/16 - NavigationNavigational instruments not provided for in groups by using measurement of speed or acceleration executed aboard the object being navigatedDead reckoning by integrating acceleration or speed, i.e. inertial navigation
G01C 25/00 - Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
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
G01S 19/47 - Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
G06F 3/01 - Input arrangements or combined input and output arrangements for interaction between user and computer
In example embodiments, techniques are described for implementing serverless property stores to hold properties that persist application customization data, such as settings. A serverless property store employs an “edge base” paradigm, wherein an edge computing device executes a property store service that maintains a local, periodically-synchronized copy of a portion of a database that stores properties (i.e., a local property cache”). A cloud container of a blob storage service of a cloud datacenter maintains a master copy of the database (i.e., a “property store database”). Read operations on a client computing device may be performed against the local property cache. Write operations may likewise be performed against the local property cache, however, they may be serialized via a write lock maintained in the cloud container. Multiple serverless property stores may be employed to store different properties each having different scopes.
In various example embodiments, machine vision-based techniques for non- contact SHM are provided that may integrate both phase-based algorithms and template matching algorithms to enable selection of one or more machine vision algorithms that are effective at measuring responses (e.g., displacement, strain, acceleration, velocity, etc.) under present conditions. Results of a single algorithm or a combination of results from multiple algorithms may be returned. In such techniques, improved template matching algorithms may be employed that provide sub-pixel accuracy. Responses may be adjusted to cancel out camera vibration and video noise. Defects or damage may be determined by tracking changes in displacement within an area of interest.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 20/40 - ScenesScene-specific elements in video content
35.
Techniques for utility structure modeling and simulation
Systems and methods are described for modeling and analyzing utility structures according to applied loads. Particularly, a model engine can utilize inputs related to a utility structure, environmental conditions to which the utility structure is subjected, and engineering standards expected of the utility structure, and analyze the structure's loading and performance based on analysis configuration inputs. An engine or multiple engines can be run locally or can be instantiated in a cloud to assist with multiple or complex calculations. Hybrid and geometric non-linear analyses and outputs can be performed or provided.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
36.
Techniques for predicting railroad track geometry exceedances
In example embodiments, techniques are provided for using machine learning to predict railroad track geometry exceedances to enable proactive maintenance. A machine learning model of a rail operational analytics application may be trained to directly output a probability of future railroad track geometry exceedances for each portion of track of a railroad. Training may be performed using all available railroad track data, and the task of selecting which data is relevant to predicting probability of railroad track geometry exceedances may be devolved to the machine learning model. Further, assumptions about the specific railroad and data characteristics may be avoided, providing the machine learning model flexibility, and allowing for dynamic changes in the problem formulation.
Techniques are provided for modifying coloring of images utilizing machine learning. A trained model is generated utilizing machine learning with training data that includes images of a plurality of different scenes with different illumination characteristics. New original images of a scene may each be downsampled and transformed to a corresponding output image utilizing the trained model. A color transformation from each original image to its corresponding output image may be determined. In an embodiment, the color transformation is determined utilizing a spline fitting approach. The determined color transformations may be applied to each of the original images to generate corrected images. Specifically, the color transformation that is applied to a particular original image is the color transformation determined for the input image that corresponds to the particular original image. The corrected images are utilized to generate a digital model of the scene, and the digital model has accurate model texture.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Downloadable computer software for modeling and simulating the movement of people, namely, for modeling travel patterns of people and simulating the effects of those travel patterns; downloadable computer software for modeling and simulating the movement of vehicles, namely, modeling and simulating the movement of traffic; downloadable computer software for modeling and simulating the movement of passengers and public transit systems; downloadable computer software for modeling and simulating multimodal travel, namely, for modeling and simulating the movement of private motor vehicles, public transit, and people walking and cycling; downloadable computer software for transport forecasting and transport planning, namely, for planning and forecasting transportation systems for the mobility of people; downloadable computer software for travel demand modeling, namely, modeling and analyzing travel demand patterns of people; downloadable computer software for developing, using, and visualizing transport models, namely, models of the movement of private motor vehicles, public transit, and people walking and cycling; downloadable computer software for modeling and simulation in the field of freight transportations; downloadable computer software for land-use modeling; downloadable computer software for modeling and simulating traffic; downloadable electronic data files featuring data and models for transport forecasting and transport planning, namely, for planning and forecasting transportation systems for the mobility of people; downloadable electronic data files featuring models of the movement of people, namely, for modeling travel patterns of people. (1) Cloud computing featuring software for modeling and simulating the movement of people, namely, for modeling travel patterns of people and simulating the effects of those travel patterns; cloud computing featuring software for modeling and simulating the movement of vehicles, namely, modeling and simulating the movement of traffic; cloud computing featuring software for modeling and simulating the movement of passengers and public transit systems; cloud computing featuring software for modeling and simulating multimodal travel, namely, for modeling and simulating the movement of private motor vehicles, public transit, and people walking and cycling; cloud computing featuring software for transport forecasting and transport planning, namely, for planning and forecasting transportation systems for the mobility of people; cloud computing featuring software for travel demand modeling, namely, modeling and analyzing travel demand patterns of people; cloud computing featuring software for modeling and simulation in the field of freight transportation; cloud computing featuring software for land-use modeling; cloud computing featuring software for modeling and simulating traffic; software as a service (SAAS), namely, hosting software for use by others for modeling and simulating the movement of people, namely, for modeling travel patterns of people and simulating the effects of those travel patterns; software as a service (SAAS), namely, hosting software for use by others for modeling and simulating the movement of vehicles, namely, modeling and simulating the movement of traffic; software as a service (SAAS), namely, hosting software for use by others for modeling and simulating the movement of passengers and public transit systems; software as a service (SAAS), namely, hosting software for use by others for modeling and simulating multi-modal travel, namely for modeling and simulating the movement of private motor vehicles, public transit, and people walking and cycling; software as a service (SAAS), namely, hosting software for use by others for transport forecasting and transport planning, namely, for planning and forecasting transportation systems for the mobility of people; software as a service (SAAS), namely, hosting software for use by others for modeling and simulation in the field of freight transportation; software as a service (SAAS), namely, hosting software for use by others for land-use modeling; software as a service (SAAS), namely, hosting software for use by others for modeling and simulating traffic.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software for modeling and simulating the movement of people; downloadable computer software for modeling and simulating the movement of vehicles and transportation networks; downloadable computer software for modeling and simulating the movement of passengers and public transit systems; downloadable computer software for modeling and simulating multimodal travel, namely for modeling and simulating the movement of private vehicles, public transit, and active transport modes; downloadable computer software for transport forecasting and transport planning; downloadable computer software for travel demand modeling; downloadable computer software for developing, using, and visualizing transport models; downloadable computer software for modeling and simulating the movement of freight; downloadable computer software for land-use modeling; downloadable computer software for modeling and simulating traffic; downloadable electronic data files featuring data and models for transport forecasting and transport planning; downloadable electronic data files featuring models of the movement of people. Cloud computing featuring software for modeling and simulating the movement of people; cloud computing featuring software for modeling and simulating the movement of vehicles and transportation networks; cloud computing featuring software for modeling and simulating the movement of passengers and public transit systems; cloud computing featuring software for modeling and simulating multimodal travel, namely for modeling and simulating the movement of private vehicles, public transit, and active transport modes; cloud computing featuring software for transport forecasting and transport planning; cloud computing featuring software for travel demand modeling; cloud computing featuring software for modeling and simulating the movement of freight; cloud computing featuring software for land-use modeling; cloud computing featuring software for modeling and simulating traffic; software as a service (SAAS), namely, hosting software for use by others for modeling and simulating the movement of people; SAAS, namely, hosting software for use by others for modeling and simulating the movement of vehicles and transportation networks; SAAS, namely, hosting software for use by others for modeling and simulating the movement of passengers and public transit systems; SAAS, namely, hosting software for use by others for modeling and simulating multi-modal travel, namely for modeling and simulating the movement of private vehicles, public transit, and active transport modes; SAAS, namely, hosting software for use by others for transport forecasting and transport planning; SAAS, namely, hosting software for use by others for modeling and simulating the movement of freight; SAAS, namely, hosting software for use by others for land-use modeling; SAAS, namely, hosting software for use by others for modeling and simulating traffic.
40.
Systems, methods, and media for accessing derivative properties from a post relational database utilizing a logical schema instruction that includes a base object identifier
Techniques are provided for accessing derivative properties from a database utilizing a logical schema instruction that includes a base object identifier. A logical schema instruction may be received and analyzed to identify a predefined keyword. A portion of the logical schema instruction, that is located in relation to the predefined keyword, may be identified. The portion may include a base object identifier and a property identifier, e.g., a derivative property identifier. In an embodiment, the identified portion of the logical instruction is not translated to a database schema instruction at prepare time. Instead, an extract function is executed at runtime access and analyze a database table that corresponds to a base object associated with the base class identifier. A property in the database table that corresponds to the property identifier may be identified. Advantageously, a base object identifier may be utilized to access a derivative property.
In example embodiments, techniques are described for implementing a code service for managing codes for elements in a digital twin of infrastructure according to an edge computing paradigm. The techniques may use an "edge base," wherein each edge computing device (e.g., a client computing device or VM) executes a code service that maintains a local, periodically-synchronized copy of a portion of a code database for the digital twin. A cloud container of a blob storage service of a cloud datacenter may maintain a master copy of the code database. Read operations by an application on a client computing device may be performed against its as-of-last-synchronization local code database. Write operations by the application may likewise be performed against the local code database, serialized via a write lock maintained in the cloud container that permits only a single client computing device to modify its local code database at a time.
In example embodiments, techniques are described for implementing a code service for managing codes for elements in a digital twin of infrastructure according to an edge computing paradigm. The techniques may use an “edge base,” wherein each edge computing device (e.g., a client computing device or VM) executes a code service that maintains a local, periodically-synchronized copy of a portion of a code database for the digital twin. A cloud container of a blob storage service of a cloud datacenter may maintain a master copy of the code database. Read operations by an application on a client computing device may be performed against its as-of-last-synchronization local code database. Write operations by the application may likewise be performed against the local code database, serialized via a write lock maintained in the cloud container that permits only a single client computing device to modify its local code database at a time.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
43.
SYSTEMS, METHODS, AND MEDIA FOR ACCESSING DERIVATIVE PROPERTIES FROM A POST RELATIONAL DATABASE UTILIZING A LOGICAL SCHEMA INSTRUCTION THAT INCLUDES A BASE OBJECT IDENTIFIER
Techniques are provided for accessing derivative properties from a database utilizing a logical schema instruction that includes a base object identifier. A logical schema instruction may be received and analyzed to identify a predefined keyword. A portion of the logical schema instruction, that is located in relation to the predefined keyword, may be identified. The portion may include a base object identifier and a property identifier, e.g., a derivative property identifier. In an embodiment, the identified portion of the logical instruction is not translated to a database schema instruction at prepare time. Instead, an extract function is executed at runtime access and analyze a database table that corresponds to a base object associated with the base class identifier. A property in the database table that corresponds to the property identifier may be identified. Advantageously, a base object identifier may be utilized to access a derivative property.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer software; computer software for creating digital representations of physical assets, processes and systems; computer software for creating digital models of infrastructure; computer software for planning, inspecting, simulating, designing, engineering, procuring, operating, commissioning, maintaining, evaluating performance of, evaluating reliability of, decommissioning and documenting infrastructure projects and assets; computer software for infrastructure modeling; computer software for modeling buildings, industrial facilities, plants, energy production and delivery facilities, civil and structural engineering projects, roadways, rail and transit networks, water and wastewater networks and utility networks; computer software for planning, inspecting, simulating, designing, engineering, procuring, operating, commissioning, maintaining, evaluating performance of, evaluating reliability of, decommissioning and documenting buildings, industrial facilities, plants, energy production and delivery facilities, civil and structural engineering projects, roadways, rail and transit networks, water and wastewater networks and utility networks. Cloud computing; cloud computing featuring software for creating digital representations of physical assets, processes and systems; cloud computing featuring software for creating digital models of infrastructure; cloud computing featuring software for planning, inspecting, simulating, designing, engineering, procuring, operating, commissioning, maintaining, evaluating performance of, evaluating reliability of, decommissioning and documenting infrastructure projects and assets; cloud computing featuring software for modeling buildings, industrial facilities, plants, energy production and delivery facilities, civil and structural engineering projects, roadways, rail and transit networks, water and wastewater networks and utility networks; software as a service (SAAS); software as a service (SAAS), namely, hosting software for creating digital representations of physical assets, processes and systems; software as a service (SAAS), namely, hosting software for creating digital models of infrastructure; software as a service (SAAS), namely, hosting software for planning, inspecting, simulating, designing, engineering, procuring, operating, commissioning, maintaining, evaluating performance of, evaluating reliability of, decommissioning and documenting infrastructure projects and assets; software as a service (SAAS), namely, hosting software for modeling buildings, industrial facilities, plants, energy production and delivery facilities, civil and structural engineering projects, roadways, rail and transit networks, water and wastewater networks and utility networks.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software for creation of computer models; downloadable computer software for creation of computer models of civil and structural engineering works; downloadable computer software for planning, inspecting, simulating, designing, engineering, procuring, operating, commissioning, maintaining, evaluating performance of, evaluating reliability of, decommissioning and documenting civil and structural engineering works; downloadable computer software for creation of computer models of roadways, railroads, bridges, industrial workflows, energy production and delivery workflows, and telecommunications systems; downloadable computer software for planning, inspecting, simulating, designing, engineering, procuring, operating, commissioning, maintaining, evaluating performance of, evaluating reliability of, decommissioning and documenting roadways, railroads, bridges, industrial workflows, energy production and delivery workflows, and telecommunications systems. Cloud computing featuring online non-downloadable software for creation of computer models; cloud computing featuring online non-downloadable software for creation of computer models of civil and structural engineering works; providing temporary use of online non-downloadable software for creation of computer models of roadways, railroads, bridges, industrial workflows, energy production and delivery workflows, and telecommunications systems; providing temporary use of online non-downloadable software for planning, inspecting, simulating, designing, engineering, procuring, operating, commissioning, maintaining, evaluating performance of, evaluating reliability of, decommissioning and documenting roadways, railroads, bridges, industrial workflows, energy production and delivery workflows, and telecommunications systems; software as a service (SAAS), namely, hosting software for creation of computer models; software as a service (SAAS), namely, hosting software for creation of computer models of civil and structural engineering works; software as a service (SAAS), namely, hosting software for creation of computer models of roadways, railroads, bridges, industrial workflows, energy production and delivery workflows, and telecommunications systems; software as a service (SAAS), namely, hosting software for planning, inspecting, simulating, designing, engineering, procuring, operating, commissioning, maintaining, evaluating performance of, evaluating reliability of, decommissioning and documenting roadways, railroads, bridges, industrial workflows, energy production and delivery workflows, and telecommunications systems.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software for creation of computer models; downloadable computer software for creation of computer models of civil and structural engineering works; downloadable computer software for planning, inspecting, simulating, designing, engineering, procuring, operating, commissioning, maintaining, evaluating performance of, evaluating reliability of, decommissioning and documenting civil and structural engineering works; downloadable computer software for creation of computer models of roadways, railroads, bridges, industrial workflows, energy production and delivery workflows, and telecommunications systems; downloadable computer software for planning, inspecting, simulating, designing, engineering, procuring, operating, commissioning, maintaining, evaluating performance of, evaluating reliability of, decommissioning and documenting roadways, railroads, bridges, industrial workflows, energy production and delivery workflows, and telecommunications systems. Cloud computing featuring online non-downloadable software for creation of computer models; cloud computing featuring online non-downloadable software for creation of computer models of civil and structural engineering works; providing temporary use of online non-downloadable software for creation of computer models of roadways, railroads, bridges, industrial workflows, energy production and delivery workflows, and telecommunications systems; providing temporary use of online non-downloadable software for planning, inspecting, simulating, designing, engineering, procuring, operating, commissioning, maintaining, evaluating performance of, evaluating reliability of, decommissioning and documenting roadways, railroads, bridges, industrial workflows, energy production and delivery workflows, and telecommunications systems; software as a service (SAAS), namely, hosting software for creation of computer models; software as a service (SAAS), namely, hosting software for creation of computer models of civil and structural engineering works; software as a service (SAAS), namely, hosting software for creation of computer models of roadways, railroads, bridges, industrial workflows, energy production and delivery workflows, and telecommunications systems; software as a service (SAAS), namely, hosting software for planning, inspecting, simulating, designing, engineering, procuring, operating, commissioning, maintaining, evaluating performance of, evaluating reliability of, decommissioning and documenting roadways, railroads, bridges, industrial workflows, energy production and delivery workflows, and telecommunications systems.
47.
Compiling user code as an extension of a host application in a browser environment
In an example embodiment, a technique is provided for compiling user code. A browser, executing on a local computing device, receives a request to compile the user code. A bundler, executing in the browser, retrieves contents of the user code and dependencies of the user code on one or more host packages of a host application. The bundler transforms, compiles and bundles the user code to produce a compiled bundle. The transforming imports each host package as a property of a global window object of the browser, wherein the property has a name that includes the host package name appended with a predetermined string, and a value that indicates an entry point into the host package. The compiles the user code as an extension of the host application in order to utilize the host packages in an already compiled form. The compiled bundle is then provided as an output.
A sample may be generated for each point of a plurality of point clouds that represent a scene. A visibility ray may be created between each point of the plurality of point clouds and the one or more sources that generated the point. One or more sample, if any, that intersect a visibility ray may be identified. Each point corresponding to an intersecting sample may be determined to represent or likely represent an unwanted object if the visibility ray is from a different source that did not generate the point and the point is not coherent with any points generated by the different source. A visibility score for each point determined to represent or likely represent an unwanted object may be adjusted. A model may be generated, wherein the model does not include the unwanted object in the scene but includes the permanent object with see-through characteristics in the scene.
Techniques are provided for automatically transforming textual data, representing an image, into P&ID components. In an embodiment, a text file may represent an image of a plant and include a plurality of image objects representing plant components. A text class identifier corresponding to each image object may be identified in the text file. An insertable P&ID component may be identified for each image object based on determining that the text class identifier is included in an application hierarchical data structure. An ordered insertion may be performed to insert each insertable P&ID component into a P&ID schematic drawing utilizing an ordered hierarchy. Additionally, P&ID data may be generated for each P&ID component inserted into the P&ID schematic drawing. As a result, a P&ID or intelligent P&ID is generated from textual data that represents an image that is simply a pictorial representation of a plant.
G06Q 10/067 - Enterprise or organisation modelling
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
G06F 30/20 - Design optimisation, verification or simulation
Techniques are provided for automatically transforming textual data, representing an image, into P&ID components. In an embodiment, a text file may represent an image of a plant and include a plurality of image objects representing plant components. A text class identifier corresponding to each image object may be identified in the text file. An insertable P&ID component may be identified for each image object based on determining that the text class identifier is included in an application hierarchical data structure. An ordered insertion may be performed to insert each insertable P&ID component into a P&ID schematic drawing utilizing an ordered hierarchy. Additionally, P&ID data may be generated for each P&ID component inserted into the P&ID schematic drawing. As a result, a P&ID or intelligent P&ID is generated from textual data that represents an image that is simply a pictorial representation of a plant.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
51.
Systems, methods, and media for filtering points of a point cloud utilizing visibility factors to generate a model of a scene
A sample may be generated for each point of a plurality of point clouds that represent a scene. A visibility ray may be created between each point of the plurality of point clouds and the one or more sources that generated the point. One or more sample, if any, that intersect a visibility ray may be identified. Each point corresponding to an intersecting sample may be determined to represent or likely represent an unwanted object if the visibility ray is from a different source that did not generate the point and the point is not coherent with any points generated by the different source. A visibility score for each point determined to represent or likely represent an unwanted object may be adjusted. A model may be generated, wherein the model does not include the unwanted object in the scene but includes the permanent object with see-through characteristics in the scene.
Techniques are provided for determining a stopping condition for 3D model decimation process. In an embodiment, a size of the 3D model may be determined based on a minimum oriented bounding box, wherein the 3D model includes vertices, edges, and faces that define a shape of a physical object. A smallest dimension value of the minimum oriented bounding box may be selected to represent the size of the 3D model. The selected dimensions value may be multiplied by a decimation factor to generate a stopping condition value. A decimation process may be performed on the 3D model until all remaining elements (e.g., edges) of the 3D model have an error value that is equal to or greater than the stopping condition value. As such, the level of detail of the 3D model is simplified while also preserving the shape of the 3D model.
G06T 3/4023 - Scaling of whole images or parts thereof, e.g. expanding or contracting based on decimating pixels or lines of pixelsScaling of whole images or parts thereof, e.g. expanding or contracting based on inserting pixels or lines of pixels
In example embodiments, techniques are provided for classifying elements of infrastructure models using a convolutional graph neural network (GNN). Graph-structured data structures are generated from infrastructure models, in which nodes represent elements and edges represent contextual relationships among elements (e.g., based on proximity, functionality, parent-child relationships, etc.). During training, the GNN learns embeddings from the nodes and edges of the graph-structured data structures, the embeddings capturing contextual clues that distinguish between elements that may share similar geometry (e.g., cross section, volume, surface area, etc.), yet serve different purposes.
G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
In example embodiments, techniques are provided for classifying elements of infrastructure models using a convolutional graph neural network (GNN). Graph-structured data structures are generated from infrastructure models, in which nodes represent elements and edges represent contextual relationships among elements (e.g., based on proximity, functionality, parent-child relationships, etc.). During training, the GNN learns embeddings from the nodes and edges of the graph-structured data structures, the embeddings capturing contextual clues that distinguish between elements that may share similar geometry (e.g., cross section, volume, surface area, etc.), yet serve different purposes.
In various example embodiments, techniques are provided for efficient and reliable anomaly detection and evaluation in a water distribution system (e.g., a smart water distribution system) using both flow and pressure time series data from sensors of the system. The techniques may implement a multi-step workflow that involves decomposing the time series data to remove seasonality and rendering the time series data stationary, detecting outliers of the stationary time series data, classifying sensor events in response to flow or pressure of detected outliers exceeding high or low thresholds for at least a given number of time steps, classifying anomaly events by correlating one or more sensor events related to flow with one or more sensor events related to pressure or by clustering a plurality of sensor events in temporal proximity, and determining a quantitative score for each of the detected anomaly events that indicates a level of significance or importance.
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G01F 1/88 - Indirect mass flowmeters, e.g. measuring volume flow and density, temperature, or pressure with differential-pressure measurement to determine the volume flow
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
In example embodiments, techniques are provided that use two different ML models (a symbol association ML model and a link association ML model), one to extract associations between text labels and one to extract associations between symbols and links, in a schematic diagram (e.g., P&ID) in an image-only format. The two models may use different ML architectures. For example, the symbol association ML model may use a deep learning neural network architecture that receives for each possible text label and symbol pair both a context and a request, and produces a score indicating confidence the pair is associated. The link association ML model may use a gradient boosting tree architecture that receives for each possible text label and link pair a set of multiple features describing at least the geometric relationship between the possible text label and link pair and produces a score indicating confidence the pair is associated.
In example embodiments, techniques are provided for labeling elements of an infrastructure model with classes. The techniques may be implemented by a labeling tool that uses an ML model to create element selections and provides a cycle review mode to speed review within such selections. The labeling tool may further provide for two file loading and a number of visualization schemes to speed comparison of label files and prediction files.
In example embodiments, techniques are provided for labeling elements of an infrastructure model with classes. The techniques may be implemented by a labeling tool that uses an ML model to create element selections and provides a cycle review mode to speed review within such selections. The labeling tool may further provide for two file loading and a number of visualization schemes to speed comparison of label files and prediction files.
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
59.
Systems, methods, and media for generating a signed distance field to a surface of a point cloud for a material point method utilized for geotechnical engineering
In an embodiment, a process may divide elements of a mesh into sub-elements utilizing field nodes. The process may determine if each field node is inside or outside a material point cloud. The process may calculate a distance from each outside field node to a surface of the material point cloud based on a surface vector in a normal direction and a deformed volume of a material point. The process may calculate a distance from each inside field node to the surface of the material point cloud based on a surface vector, in a normal direction and associated with an outside field node closest to the inside field node, that takes into account a deformed volume of a material point. The process may utilize the distances to generate a signed distance field that may be used to perform calculations to simulate a behavior of a physical material/object that exhibit deformations.
G06F 30/23 - Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06T 17/20 - Wire-frame description, e.g. polygonalisation or tessellation
G06T 19/20 - Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software for modeling and simulating the movement of people; downloadable computer software for modeling and simulating the movement of vehicles and transportation networks; downloadable computer software for modeling and simulating the movement of passengers and public transit systems; downloadable computer software for modeling and simulating multi-modal travel, namely for modeling and simulating the movement of private vehicles, public transit, and active transport modes; downloadable computer software for transport forecasting and transport planning; downloadable computer software for travel demand modeling; downloadable computer software for developing, using, and visualizing transport models; downloadable computer software for modeling and simulating the movement of freight; downloadable computer software for land-use modeling; downloadable computer software for modeling and simulating traffic; downloadable electronic data files featuring data and models for transport forecasting and transport planning; downloadable electronic data files featuring models of the movement of people. Cloud computing featuring software for modeling and simulating the movement of people; cloud computing featuring software for modeling and simulating the movement of vehicles and transportation networks; cloud computing featuring software for modeling and simulating the movement of passengers and public transit systems; cloud computing featuring software for modeling and simulating multi-modal travel, namely for modeling and simulating the movement of private vehicles, public transit, and active transport modes; cloud computing featuring software for transport forecasting and transport planning; cloud computing featuring software for travel demand modeling; cloud computing featuring software for modeling and simulating the movement of freight; cloud computing featuring software for land-use modeling; cloud computing featuring software for modeling and simulating traffic; software as a service (SAAS), namely, hosting software for use by others for modeling and simulating the movement of people; SAAS, namely, hosting software for use by others for modeling and simulating the movement of vehicles and transportation networks; SAAS, namely, hosting software for use by others for modeling and simulating the movement of passengers and public transit systems; SAAS, namely, hosting software for use by others for modeling and simulating multi-modal travel, namely for modeling and simulating the movement of private vehicles, public transit, and active transport modes; SAAS, namely, hosting software for use by others for transport forecasting and transport planning; SAAS, namely, hosting software for use by others for modeling and simulating the movement of freight; SAAS, namely, hosting software for use by others for land-use modeling; SAAS, namely, hosting software for use by others for modeling and simulating traffic.
61.
Identifying switchable elements to isolate a location from sources
In example embodiments, a shortest path first-based isolation trace function is provided to determines what switchable elements need to be closed to stop flow of a quality to a location in an infrastructure model arranged as a network. The shortest path first-based isolation trace function may perform shortest-path traces from the location to each source. For each successful shortest-path trace finding one or more switchable elements, the first switchable element encountered on the path of the trace is added to a solution set, and marked as active to prevent further traversal in subsequent shortest-path traces. When all the shortest-path traces are complete, the solution set may be returned as a result. If no switchable element is found on a path of one of the shortest-path traces, it may be concluded that the location cannot be isolated and such conclusion returned as the result instead of the solution set.
In example embodiments, techniques are described for using workspace databases to provide workspace resources to customize sessions of applications. File-based workspace databases are maintained in workspace files in a local file system. Cloud-based workspace databases are maintained in a cloud-based blob storage container of a storage account of a cloud storage system. Each cloud-based blob storage container may hold multiple cloud-based workspace databases, including multiple versions of the same database. To use a cloud-based workspace database, a backend module of an application may create an in-memory cloud container object that represents a connection to the cloud-based blob storage container. It may be attached to an in-memory object configured to manage a local cache of blocks of workspace databases. Access to the cloud-based blob storage container may be managed by access tokens provided by a container authority. Modifications to existing workspace databases or addition of new workspace databases may be performed using a workspace editor.
H04L 67/06 - Protocols specially adapted for file transfer, e.g. file transfer protocol [FTP]
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
In example embodiments, techniques are described for using workspace databases to provide workspace resources to customize sessions of applications. File-based workspace databases are maintained in workspace files in a local file system. Cloud-based workspace databases are maintained in a cloud-based blob storage container of a storage account of a cloud storage system. Each cloud-based blob storage container may hold multiple cloud-based workspace databases, including multiple versions of the same database. To use a cloud-based workspace database, a backend module of an application may create an in-memory cloud container object that represents a connection to the cloud-based blob storage container. It may be attached to an in-memory object configured to manage a local cache of blocks of workspace databases. Access to the cloud-based blob storage container may be managed by access tokens provided by a container authority. Modifications to existing workspace databases or addition of new workspace databases may be performed using a workspace editor.
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Writing of proposals and bids for companies; compiling materials for inclusion in proposals and bids for companies; business management and organization consultancy; business consulting and advisory services in the field of enterprise asset management; business consulting and advisory services in the field of asset performance management; business consulting and advisory services in the field of infrastructure asset management; business consulting and advisory services in the field of digital engineering; business consultation in the field of business process and business procedure design.
(2) Training in the use of computer software; business training; training services in the field business processes and business procedures
(3) Technical consulting services; technical consulting services in the field of enterprise asset management; technical consulting services in the field of asset performance management; technical consulting services in the field of infrastructure asset management; technical consulting services in the field of digital engineering; technical consulting services for digital engineering systems integration; technical consulting services for infrastructure project management; Technical consulting services for infrastructure performance prediction and analytics; Technical consulting services in the field of building information modeling; engineering services; engineering services in the field of infrastructure asset management; providing temporary use of non-downloadable cloud computing software; providing temporary use of non-downloadable cloud computing software for enterprise asset management; providing temporary use of non-downloadable cloud computing software for asset performance management; software as a service; software as a service for enterprise asset management; software as a service for asset performance management; application service provider, namely, hosting computer software applications of others; cybersecurity services in the nature of restricting unauthorized access to computer systems.
In example embodiments, techniques are described for using workspace databases to provide workspace resources to customize sessions of applications. To write workspace resources a backend module of an application may obtain a write lock on a cloud-based blob storage container, and ensure a block of a workspace database to be modified is local in a cloud cache. It may execute one or more database commands to modify the block in the cloud cache, and change an identifier of the block in a local copy of a manifest that includes a list of the blocks of the cloud-based blob storage container. It may further upload the modified block and the local copy of the manifest to the cloud-based blob storage container, wherein the uploaded local copy of the manifest replaces the manifest in the cloud-based blob storage container.
H04L 67/06 - Protocols specially adapted for file transfer, e.g. file transfer protocol [FTP]
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
66.
Simplifying complex rail turnout geometry for computing and converging connections
In various embodiments, techniques are provided for determining a connection between a rail turnout and another rail turnout or other rail element by a geometry connection process of rail network design software, by reducing the actual complex geometry of the rail turnout to a simplified arc, which at one end is tangent to the geometry of a connecting element at end of the rail turnout and at the other end is tangent to the geometry of a parent base element of the rail turnout. The simplified arc is utilized instead of the actual complex geometry of the rail turnout by a connection computation engine to determine the connection in the model (e.g., by fitting a connection solution using least squares).
B61L 27/53 - Trackside diagnosis or maintenance, e.g. software upgrades for trackside elements or systems, e.g. trackside supervision of trackside control system conditions
67.
Evolutionary deep learning with extended Kalman filter for modeling and data assimilation
In example embodiments, an enhanced deep belief learning model with an extended Kalman filter (EKF) is used for training and updating a deep belief network (DBN) with new data to produce a DBN model useful in making predictions on a variety of types of datasets, including data captured from infrastructure-attached sensors describing the condition of the infrastructure. The EKF is employed to estimate operation parameters of the DBN and generate the model's output covariance. Further, in example embodiments, the configuration of the DBN model may be optimized by a competent genetic algorithm.
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Writing of project proposals and bids for companies; business information services, namely, compiling materials for inclusion in project proposals and bids for companies; business management and organization consultancy; business consulting and advisory services in the field of enterprise asset management; business consulting and advisory services in the field of asset performance management; business consulting and advisory services in the field of infrastructure asset management; business consulting and advisory services in the field of digital engineering; business consultation in the field of business process and business procedure design Training in the use of computer software; business training; training services in the field business processes and business procedures Technical consulting services in the field of enterprise asset management; technical consulting services in the field of asset performance management; technical consulting services in the field of infrastructure asset management; technical consulting services in the field of digital engineering; technical consulting services for digital engineering systems integration; technical consulting services for infrastructure project management; Technical consulting services for infrastructure performance prediction and analytics; Technical consulting services in the field of building information modeling; engineering services in the field of infrastructure asset management; providing temporary use of non-downloadable cloud computing software for enterprise asset management; providing temporary use of non-downloadable cloud computing software for asset performance management; software as a service featuring software for enterprise asset management; software as a service featuring software for asset performance management; application service provider, namely, hosting computer software applications of others; cybersecurity services in the nature of restricting unauthorized access to computer systems
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Writing of project proposals and bids for companies; business information services, namely, compiling materials for inclusion in project proposals and bids for companies; business management and organization consultancy; business consulting and advisory services in the field of enterprise asset management; business consulting and advisory services in the field of asset performance management; business consulting and advisory services in the field of infrastructure asset management; business consulting and advisory services in the field of digital engineering; business consultation in the field of business process and business procedure design Training services, namely, training in the use of computer software in the fields of enterprise asset management, asset performance management and digital engineering; business training; training services in the field business processes and business procedures Technical consulting services in the field of enterprise asset management; technical consulting services in the field of asset performance management; technical consulting services in the field of infrastructure asset management; technical consulting services in the field of digital engineering; technical consulting services for digital engineering systems integration; technical consulting services for infrastructure project management; Technical consulting services for infrastructure performance prediction and analytics; Technical consulting services in the field of building information modeling; engineering services in the field of infrastructure asset management; providing temporary use of non-downloadable cloud computing software for enterprise asset management; providing temporary use of non-downloadable cloud computing software for asset performance management; software as a service featuring software for enterprise asset management; software as a service featuring software for asset performance management; application service provider, namely, hosting computer software applications of others; cybersecurity services in the nature of restricting unauthorized access to computer systems
70.
Techniques for decoupling access to infrastructure models
In example embodiments, techniques are provided for decoupling user access to infrastructure models from proprietary software that maintains and updates the infrastructure models. A backend application may include an infrastructure modeling backend module that, among other functions, handles communication with an infrastructure modeling frontend module of a frontend application that provides user access to the infrastructure model, infrastructure modeling hub services that maintain repositories for the infrastructure model, and an infrastructure modeling native module that creates, performs operations upon, and updates local instances of a database that stores the infrastructure model. While the infrastructure modeling backend module may pass information obtained from the infrastructure modeling frontend module and infrastructure modeling hub services to the infrastructure modeling native module, it may be functionally separated from the software of the infrastructure modeling native module that understands how to maintain and update infrastructure models, including interacting with local instances of the database.
In various example embodiments, techniques are provided for training and/or using a semantic deep learning model, such as a segmentation-enabled CNN model, to detect corrosion and enable its quantitative evaluation. An application may include a training dataset generation tool capable of semi-automatic generation of a training dataset that includes images with labeled corrosion segments. The application may use the labeled training dataset to train a semantic deep learning model to detect and segment corrosion in images of an input dataset at the pixel-level. The application may apply an input dataset to the trained semantic deep learning model to produce a semantically segmented output dataset that includes labeled corrosion segments. The application may include an evaluation tool that quantitatively evaluates corrosion in the semantically segmented output dataset, to allow severity of the corrosion to be classified.
In example embodiments, a multi-stage PFS diagram generation technique is used to iteratively define the layout of a PFS diagram from a subset of engineering data in a 3D model of an industrial process. The multi-stage PFS diagram generation technique may repeatedly call an automatic layout generator, which each time solves for one unknown quality of the PFS diagram (e.g., relative positions of components in the PFS diagram, positions on components in the PFS diagram, sizes of the components in the PFS diagram). The PFS diagram may be adapted based on user preferences, for example to define the subset of engineering data, or to constrain aspects of its layout. Updated PFS diagrams may be generated by selecting different user preferences.
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]
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
H01L 23/528 - Layout of the interconnection structure
G05B 17/02 - Systems involving the use of models or simulators of said systems electric
73.
TECHNIQUES FOR DETECTING AND SUGGESTING FIXES FOR DATA ERRORS IN DIGITAL REPRESENTATIONS OF INFRASTRUCTURE
In example embodiments, machine learning techniques are provided for ensuring quality and consistency of the data in a digital representation of infrastructure (e.g., a BIM or digital twin). A machine learning model learns the structure of the digital representation of infrastructure, and then detects and suggests fixes for data errors. The machine learning model may include an embedding generator, an autoencoder, and decoding logic, employing embeddings and metamorphic truth to enable the handling of heterogenous data, with missing and erroneous property values. The machine learning model may be trained in an unsupervised manner from the digital representation of infrastructure itself (e.g., by assuming that a significant portion is correct). An SME review workflow may be provided to correct predictions and inject ground truth to improve performance.
In example embodiments, machine learning techniques are provided for ensuring quality and consistency of the data in a digital representation of infrastructure (e.g., a BIM or digital twin). A machine learning model learns the structure of the digital representation of infrastructure, and then detects and suggests fixes for data errors. The machine learning model may include an embedding generator, an autoencoder, and decoding logic, employing embeddings and metamorphic truth to enable the handling of heterogenous data, with missing and erroneous property values. The machine learning model may be trained in an unsupervised manner from the digital representation of infrastructure itself (e.g., by assuming that a significant portion is correct). An SME review workflow may be provided to correct predictions and inject ground truth to improve performance.
In example embodiments, techniques are provided for decoupling user access to infrastructure models from proprietary software that maintains and updates the infrastructure models. A backend application may include an infrastructure modeling backend module that, among other functions, handles communication with an infrastructure modeling frontend module of a frontend application that provides user access to the infrastructure model, infrastructure modeling hub services that maintain repositories for the infrastructure model, and an infrastructure modeling native module that creates, performs operations upon, and updates local instances of a database that stores the infrastructure model. While the infrastructure modeling backend module may pass information obtained from the infrastructure modeling frontend module and infrastructure modeling hub services to the infrastructure modeling native module, it may be functionally separated from the software of the infrastructure modeling native module that understands how to maintain and update infrastructure models, including interacting with local instances of the database.
In various example embodiments, relevant changes between 3D models of a scene are detected and classified by transforming the 3D models into point clouds and applying a deep learning model to the point clouds. The model may employ a Siamese arrangement of sparse lattice networks each including a number of modified BCLs. The sparse lattice networks may each take a point cloud as input and extract features in 3D space to provide a primary output with features in 3D space and an intermediate output with features in lattice space. The intermediate output from both sparse lattice networks may be compared using a lattice convolution layer. The results may be projected into the 3D space of the point clouds using a slice process and concatenated to the primary io outputs of the sparse lattice networks. Each concatenated output may be subject to a convolutional network to detect and classify relevant changes.
A drilling interpretation and volumes estimator (DRIVER) system may be provided. The DRIVER system may help facilitate a cost-effective discovery of patterns in mineral exploration drilling data that a mining company may not have the human or computer resources to look for. The DRIVER system may be able to reason with those patterns against previously-documented knowledge and may produce conclusions of value to a user, such as a mining professional.
G01V 99/00 - Subject matter not provided for in other groups of this subclass
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
78.
Techniques for generating one or more scores and/or one or more corrections for a digital twin representing a utility network
Techniques are provided for generating score(s) and/or correction(s) for a digital twin representing a utility network. One or more bridges transform data, from a plurality of system and associated with a utility network, to a different format, e.g., relational database format. A process generates a digital twin of the utility network utilizing the data in the different format. A data quality service (DQS) performs evaluations and/or analyses of the digital twin to generate a baseline score and an updated score representing a state of the digital twin if corrections are applied. If the updated score meets or is above a threshold value, the DQS automatically applies and save the corrections to the digital twin. If the updated score does not meet the threshold value, the DQS presents a failure notification and one or more graphical representations of the utility network such that incremental corrections can be made.
G06F 30/18 - Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
G06F 16/25 - Integrating or interfacing systems involving database management systems
In example embodiments, techniques are provided for calculating camera rotation using translations between sensor-derived camera positions (e.g., from GPS) and pairwise information, producing a sensor-derived camera pose that may be integrated in an early stage of SfM reconstruction. A software process of a photogrammetry application may obtain metadata including sensor-derived camera positions for a plurality of cameras for a set of images and determine optical centers based thereupon. The software process may estimate unit vectors along epipoles from a given camera of the plurality of cameras to two or more other cameras. The software process then may determine a camera rotation that best maps unit vectors defined based on differences in the optical centers to the unit vectors along the epipoles. The determined camera rotation and the sensor-derived camera position form a sensor-derived camera pose that may be returned and used.
Techniques are provided for generating score(s) and/or correction(s) for a digital twin representing a utility network. One or more bridges transform data, from a plurality of system and associated with a utility network, to a different format, e.g., relational database format. A process generates a digital twin of the utility network utilizing the data in the different format. A data quality service (DQS) performs evaluations and/or analyses of the digital twin to generate a baseline score and an updated score representing a state of the digital twin if corrections are applied. If the updated score meets or is above a threshold value, the DQS automatically applies and save the corrections to the digital twin. If the updated score does not meet the threshold value, the DQS presents a failure notification and one or more graphical representations of the utility network such that incremental corrections can be made.
G06F 30/18 - Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
G06F 16/25 - Integrating or interfacing systems involving database management systems
In example embodiments, techniques are provided for visualizing a 3D model in an interactive editing workflow. A user modifies one or more elements of a model of the 3D model, by inserting one or more new elements having geometry, changing the geometry of one or more existing elements and/or deleting one or more existing elements having geometry. An updated view of the 3D model is then rendered to reflect the modification to the one or more elements in part by obtaining, for each new element or changed existing element of the model visible in the view, a polygon mesh that represents geometry of the individual element, obtaining a set of tiles that each include a polygon mesh that represent collective geometry of a set of elements intersecting the tile's volume, displaying the polygon mesh for each new element or changed existing element, and displaying the set of tiles while hiding any deleted or changed existing elements therein.
In one example embodiment, a software application obtains a set of images that include an aerial cable and generates a 3D model from the set of images. The 3D model initially excludes a representation of the aerial cable. The software application processes each image of the set of images to extract pixels that potentially represent cables and determines a position in 3D space of the 3D model of a pair of attachment points for the aerial cable. The software application defines a vertical plane in 3D space of the 3D model based on the pair of cable attachment points. For each of one or more images of the set of images, the software application projects at least some of the pixels that potentially represent cables onto the vertical plane. The software application then calculates a curve representation (e.g., a catenary equation) for the aerial cable based on the pixels projected onto the vertical plane, and adds a cable model defined by the curve representation to the 3D model to represent the aerial cable.
A method of modifying or controlling a highway traffic system may include training a machine learning model using historical traffic data corresponding to a roadway traffic system in which the historical traffic data is indicative of traffic patterns over a historical time interval. The method may include obtaining, by the machine learning model, traffic data corresponding to the roadway traffic system and determining a probability of traffic congestion occurrence based on the obtained traffic data corresponding to the roadway traffic system. The method may include comparing the probability of traffic congestion occurrence to a traffic control probability threshold, and responsive to the probability of traffic congestion exceeding the traffic control probability threshold, adjusting operations associated with one or more traffic controls that correspond to the roadway traffic system. The machine learning model may be retrained after a time interval using the obtained traffic data corresponding to the roadway traffic system.
In one embodiment, techniques are provided for improved security threat modeling and threat intelligence for infrastructure managed by ICSs. The techniques may leverage an existing model of an ICS created in a CAD application, add to the model security properties specifying configuration of respective electronic components of the ICS, and analyze the resulting combination, together with information from a threat database to automatically generate output such as a threat model diagram, threat model report or an interactive threat intelligence dashboard. A visualization of the output may be displayed together with, or include, a graphical rendering of the infrastructure managed to aid in its interpretation.
G06F 3/048 - Interaction techniques based on graphical user interfaces [GUI]
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 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
G05B 17/02 - Systems involving the use of models or simulators of said systems electric
G06F 30/20 - Design optimisation, verification or simulation
G06F 111/20 - Configuration CAD, e.g. designing by assembling or positioning modules selected from libraries of predesigned modules
85.
CLASSIFYING ELEMENTS AND PREDICTING PROPERTIES IN AN INFRASTRUCTURE MODEL THROUGH PROTOTYPE NETWORKS AND WEAKLY SUPERVISED LEARNING
In example embodiments, a software service may employ a neural network to learn a non-linear mapping that transforms element features into embeddings. The neural network may be trained to distribute the embeddings in multi-dimensional embedding space, such that distance between the embeddings is meaningful to the class or category classification, or property prediction, task at hand. The neural network may be trained using weakly supervised machine learning, using weakly labeled infrastructure models. Embeddings for groups may be used to determine prototypes. Elements of an infrastructure model may be classified into classes or categories, or their properties predicted, as the case may be, by finding a nearest prototype.
In example embodiments, a software service may employ a neural network to learn a non-linear mapping that transforms element features into embeddings. The neural network may be trained to distribute the embeddings in multi-dimensional embedding space, such that distance between the embeddings is meaningful to the class or category classification, or property prediction, task at hand. The neural network may be trained using weakly supervised machine learning, using weakly labeled infrastructure models. Embeddings for groups may be used to determine prototypes. Elements of an infrastructure model may be classified into classes or categories, or their properties predicted, as the case may be, by finding a nearest prototype.
In one embodiment, techniques are provided for aligning source infrastructure data to be compatible with a conceptual schema (e.g., BIS) implemented through an underlying database schema (e.g., DgnDb). Data aligned according to the conceptual schema may serve as a “digital twin” of real-world infrastructure usable throughout various phases of an infrastructure project, with physical information serving as a “backbone”, and non-physical information maintained relative thereto, forming a cohesive whole, while avoiding unwanted data redundancies. Source-format-specific bridge software processes may be provided that that know how to read and interpret source data of a respective source format, and express it in terms of the conceptual schema. The aligned data may be sent to an update agent that interprets the aligned data and computes a changeset therefrom, which may be stored for eventual application to a particular instance of a database maintained according to an underlying database schema of the conceptual schema.
G06F 16/25 - Integrating or interfacing systems involving database management systems
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
In example embodiments, techniques are provided for integrating pieces of heavy equipment into a virtual construction modeling workflow by including representations of the pieces of the heavy equipment in a 3D environment of a virtual construction model, evaluating capabilities and clashes in the context of the 3D environment, and adding descriptions of the pieces of heavy equipment and operational details to work packages. Each piece of heavy equipment is associated with a unique ID, an effective range (e.g., lifting radius) and other parameters. Using a client the user links the piece of heavy equipment to one or more work packages by associating its unique ID with the work package. The work package is associated with a physical extent in the virtual construction model which falls within the effective range of the equipment. Operational details (e.g., scheduling, cost, usage rates, maintenance, etc.) are defined in connection with the work package.
Techniques are provided for generating and retrieving change summary data and aggregated model version data for an infrastructure model. A process obtains a briefcase representing a particular version of the infrastructure model and one or more changesets. The process applies the changeset(s) to the briefcase to construct a briefcase that represents a newer version of the infrastructure model. The process compares the briefcases to generate a change summary indicating modifications between the two versions. Further, the process generates aggregated model version data as the infrastructure model transitions to newer versions. The process updates the aggregated model version data utilizing the change summaries such that the aggregated model version data is comprehensive regarding each element that is and was included in the infrastructure model from its genesis to its current state. A client device issues requests to obtain a particular version of the infrastructure model and/or to obtain modifications between versions.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
G06F 111/02 - CAD in a network environment, e.g. collaborative CAD or distributed simulation
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
G06F 9/451 - Execution arrangements for user interfaces
90.
Automatic creation of models of overhead line structures
In one or more embodiments, techniques are provided for modeling overhead line structures of electric railways that utilize a flexible, reusable structure template to automatically generate a 3D model of the overhead line structure. Each structure template includes a set of points that represent joints of the overhead line structure and components that represent elements of the overhead line structure. A feature definition of each joint and component includes properties, constraints and cell mappings. By mapping key points of reference lines for an overhead line structure to key points in an applicable structure templet for the overhead line structure, and applying the constraints and, in some cases the cell mappings, a 3D model of the overhead line structure is automatically generated. The 3D model may be a “low detail” stick representation for fast modeling, or, using the cell mappings, a “high detail” cell-based representation for very realistic modeling.
In example embodiments, techniques are provided for enabling use of an AI-generated TIN in generation of a 3D design model by defining site objects (e.g., pads) using multiple (e.g., three) phases (i.e. states). A conceptual phase may be associated with a conceptual data structure, a preliminary phase may be associated with the conceptual data structure and a preliminary data structure, a final phase may be associated with the conceptual data structure, the preliminary data structure, and a final data structure. If changes are made in the conceptual phase, for example, as a result of AI optimization, they may be propagated up to the preliminary data structure and final data structure via the vertical draping. Changes made in the preliminary phase or final phase may be propagated down to the conceptual data structure by treating boundaries and breaklines as spatial constraints.
In example embodiments, techniques are provided for refining tiles of an HLOD tree representing a model in order to display a view. A frontend module selects a tile represented by a node of the HLOD sub-tree and obtains information describing geometry of the selected tile. It determines that the selected tile requires refinement to support the view of the model based on the information describing geometry of the selected tile. A tile refinement strategy is determined from a plurality of tile refinement strategies. The frontend module applies the determined tile refinement strategy to the selected tile to generate one or more child tiles that have a higher resolution than the selected tile, the one or more child tiles represented by child nodes added to the HLOD sub-tree. The frontend module displays the view of the model at least in part by showing tiles represented by nodes of the HLOD sub-tree.
A method may be implemented to prioritize and analyze data exchanged in a connected vehicle transit network. The method may include receiving, at a roadside unit, vehicle data from a connected vehicle. The method may further include prioritizing the vehicle data received from the connected vehicle based on a level of urgency, network latency or available computing resources.
G08G 1/01 - Detecting movement of traffic to be counted or controlled
G08G 1/087 - Override of traffic control, e.g. by signal transmitted by an emergency vehicle
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
H04W 4/44 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
94.
AUTOMATIC IDENTIFICATION OF MISCLASSIFIED ELEMENTS OF AN INFRASTRUCTURE MODEL
In example embodiments, techniques are provided to automatically identify misclassified elements of an infrastructure model using machine learning. In a first set of embodiments, supervised machine learning is used to train one or more classification models that use different types of data describing elements (e.g., a geometric classification model that uses geometry data, a natural language processing (NLP) classification model that uses textual data, and an omniscient (Omni) classification model that uses a combination of geometry and textual data; or a single classification model that uses geometry data, textual data, and a combination of geometry and textual data). Predictions from classification models (e.g., predictions from the geometric classification model, NLP classification model and the Omni classification model) are compared to identify misclassified elements, or a prediction of misclassified elements directly produced (e.g., from the single classification model). In a second set of embodiments, unsupervised machine learning is used to detect abnormal associations in data describing elements (e.g., geometric data and textual data) that indicate misclassifications. Identified misclassifications are displayed to a user for review and correction.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
In example embodiments, techniques are provided for visually comparing digital content for an infrastructure project according to time using 4-D construction modeling software. The 4-D construction modeling software includes a cloud-based 4-D comparison service and a local 4-D modeling client. The 4-D comparison service includes a digital content alignment service and a 4-D difference engine. The digital content alignment service aligns different pieces of digital content and produces views that provide visual comparison between different pieces of digital content. The 4-D difference engine automatically determines differences between different pieces of digital content. The 4-D modeling client includes a 4-D comparison user interface (UI) process that receives user input used to generate, and then displays a generated visual comparison between different pieces of digital content. The 4-D comparison UI utilizes time control channels for selecting digital content and comparison controls for selecting a type of visual comparison.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
96.
Automatic identification of misclassified elements of an infrastructure model
In example embodiments, techniques are provided to automatically identify misclassified elements of an infrastructure model using machine learning. In a first set of embodiments, supervised machine learning is used to train one or more classification models that use different types of data describing elements (e.g., a geometric classification model that uses geometry data, a natural language processing (NLP) classification model that uses textual data, and an omniscient (Omni) classification model that uses a combination of geometry and textual data; or a single classification model that uses geometry data, textual data, and a combination of geometry and textual data). Predictions from classification models (e.g., predictions from the geometric classification model, NLP classification model and the Omni classification model) are compared to identify misclassified elements, or a prediction of misclassified elements directly produced (e.g., from the single classification model). In a second set of embodiments, unsupervised machine learning is used to detect abnormal associations in data describing elements (e.g., geometric data and textual data) that indicate misclassifications. Identified misclassifications are displayed to a user for review and correction.
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
97.
Crack detection, assessment and visualization using deep learning with 3D mesh model
In various example embodiments, techniques are provided for crack detection, assessment and visualization that utilize deep learning in combination with a 3D mesh model. Deep learning is applied to a set of 2D images of infrastructure to identify and segment surface cracks. For example, a Faster region-based convolutional neural network (Faster-RCNN) may identify surface cracks and a structured random forest edge detection (SFRED) technique may segment the identified surface cracks. Alternatively, a Mask region-based convolutional neural network (Mask-RCNN) may identify and segment surface cracks in parallel. Photogrammetry is used to generate a textured three-dimensional (3D) mesh model of the infrastructure from the 2D images. A texture cover of the 3D mesh model is analyzed to determine quantitative measures of identified surface cracks. The 3D mesh model is displayed to provide a visualization of identified surface cracks and facilitate inspection of the infrastructure.
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 20/20 - ScenesScene-specific elements in augmented reality scenes
98.
VISUALIZATION OF MASSIVE 3D MODELS IN INTERACTIVE EDITING WORKFLOWS
In example embodiments, techniques are provided for visualizing a 3D model in an interactive editing workflow. A user modifies one or more elements of a model of the 3D model, by inserting one or more new elements having geometry, changing the geometry of one or more existing elements and/or deleting one or more existing elements having geometry. An updated view of the 3D model is then rendered to reflect the modification to the one or more elements in part by obtaining, for each new element or changed existing element of the model visible in the view, a polygon mesh that represents geometry of the individual element, obtaining a set of tiles that each include a polygon mesh that represent collective geometry of a set of elements intersecting the tile's volume, displaying the polygon mesh for each new element or changed existing element, and displaying the set of tiles while hiding any deleted or changed existing elements therein.
09 - Scientific and electric apparatus and instruments
Goods & Services
Computer software; computer software for transport planning; computer software for travel demand modeling and simulation; computer software for modeling and simulating the mobility of people; computer software for population synthesis, travel demand forecasting, transit planning, traffic planning, and travel economic, emissions and environmental analysis; computer software for producing travel demand models; electronic data files featuring models for transport planning; electronic data files featuring models of the mobility of people.
09 - Scientific and electric apparatus and instruments
Goods & Services
(1) Computer software for transport planning, namely for planning transportation systems for the movement of people; computer software for travel demand modeling and simulation, namely for modeling travel patterns of people and simulating the effects of those travel patterns on transportation networks; computer software for modeling and simulating the mobility of people; computer travel demand models, namely computer models of the travel patterns of people; electronic data files featuring models of the mobility of people.
