One embodiment of the present invention sets forth a technique for extracting data from an architectural drawing. The technique includes performing one or more operations via one or more machine learning models to extract a first image of a floorplan area from the architectural drawing. The technique also includes generating a boundary segmentation based on the first image of the floorplan area, wherein the boundary segmentation includes one or more boundary types for one or more portions of the floorplan area.
One embodiment sets forth a technique for generating answers to questions about a software application that is featured in a learning video. According to some embodiments, the technique includes the steps of (1) generating at least one description based on at least one image-based input associated with the learning video, (2) generating a combined value based on the at least one description and a text-based question, (3) obtaining a plurality of articles based on the combined value, (4) generating, via at least one generative artificial intelligence (AI) model, an answer to the text-based question based on the plurality of articles, and (5) causing at least a portion of the answer to be output via at least one user interface.
G09B 7/02 - Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by the student
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
G06V 20/40 - ScenesScene-specific elements in video content
One embodiment sets forth a technique for generating answers to questions about a software application that is featured in a learning video. According to some embodiments, the technique includes the steps of (1) generating at least one description based on at least one image-based input associated with the learning video, (2) generating a combined value based on the at least one description and a text-based question, (3) obtaining a plurality of articles based on the combined value, (4) generating, via at least one generative artificial intelligence (AI) model, an answer to the text-based question based on the plurality of articles, and (5) causing at least a portion of the answer to be output via at least one user interface.
Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided generative task scheduling, include: obtaining a dataset describing a schedule of one or more projects to be rescheduled, the dataset including tasks to be scheduled, resource requirements, and dependencies between or among the tasks; generating variations of the schedule, each of the variations having different characteristics that determine for the tasks in the schedule when each task is scheduled in time, and each of the variations satisfying the resource requirements and the dependencies; selecting among the different characteristics for the tasks in the variations of the schedule to form a revised schedule of the one or more projects; and providing the revised schedule of the one or more projects for display to a user or for output to manage the one or more projects.
One embodiment of the present invention sets forth a technique for performing style transfer. The technique includes determining a distribution associated with a plurality of style codes for a plurality of three-dimensional (3D) shapes, where each style code included in the plurality of style codes represents a difference between a first 3D shape and a second 3D shape, and where the second 3D shape is generated by applying one or more augmentations to the first 3D shape. The technique also includes sampling from the distribution to generate an additional style code and executing a trained machine learning model based on the additional style code to generate an output 3D shape having style-based attributes associated with the additional style code and content-based attributes associated with an object. The technique further includes generating a 3D model of the object based on the output 3D shape.
A computer-implemented method for generating and evaluating robotic workcell solutions includes: determining a plurality of locations within a workcell volume, wherein each location corresponds to a possible workcell solution; for each location included in the plurality of locations, determining a value for a first robot-motion attribute for a first robot based on position information associated with the location and a trajectory associated with a component of the first robot; and, for each location included in the plurality of locations, computing a first value for a first performance metric based on the value for the first robot-motion attribute.
A method and system provide for generating a stormwater overland flow map. Simulation inputs (ground surface data) and simulation outputs (stormwater overland flow maps) are obtained from a deluge simulation model that simulates where water will channel and accumulate on a surface. A convolutional neural network (CNN) is trained to approximate the simulation outputs of the deluge simulation model. The CNN is a sequence of CNN models that each represent a time step and each CNN model in the sequence takes CNN output from a previous CNN model as its input. The CNN output is a video output and a visual representation of stormwater overland flow over time. A new input is a first format is obtained. A collection of custom objects representing points of a grid are stored and includes z-values of points on the surface and stormwater controls. The grid is populated and then processed in/by the CNN.
G06F 30/20 - Design optimisation, verification or simulation
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
A method and system provide the ability to process a construction domain query. A natural language user query is obtained within a construction software system. The user query is pre-processed to validate the query. Text from the query is embed into search vectors for a semantic search. A data source having multiple different sections is obtained. The semantic search is performed within each of the sections and identifies semantically relevant sections. The relevant sections are consolidated into a contextual data prompt that is input into an LLM. The LLM, which is trained based on construction data, generates a response that identifies the relevant sections. The response and an identification of the relevant sections is output.
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable software for creating, rendering, executing, and displaying animation, visual effects, video and computer games, and digital media content; downloadable animation software manuals; downloadable educational course materials in the field of animation Educational services, namely, conducting classes, workshops, and seminars in the field of computer graphics, animation creation and generation, and distribution of training materials in connection therewith; organization of exhibitions for cultural or educational purposes Design and development of computer systems and software; conversion of data and computer programs other than physical conversion; conversion of data or documents from physical to electronic media; software design, development, installation, maintenance, and updating; research and development of new products for third parties in the field of software; consulting services in the fields of selection, implementation, and use of computer software systems for others
10.
TECHNIQUES FOR GENERATIVE DESIGN BASED ON LARGE LANGUAGE MODELS
Techniques for generative design based on large language models include receiving one or more design prompts; generating, based on the one or more design prompts, a plurality of design tokens using a large language model; generating a conceptual design layout from the plurality of design tokens; and optimizing the conceptual design layout to generate a compliant design layout.
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
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
11.
GENERATIVE ARTIFICIAL INTELLIGENCE (AI) CONSTRUCTION SPECIFICATION INTERFACE
A method and system provide the ability to process a construction domain query. A natural language user query is obtained within a construction software system. The user query is pre-processed to validate the query. Text from the query is embed into search vectors for a semantic search. A data source having multiple different sections is obtained. The semantic search is performed within each of the sections and identifies semantically relevant sections. The relevant sections are consolidated into a contextual data prompt that is input into an LLM. The LLM, which is trained based on construction data, generates a response that identifies the relevant sections. The response and an identification of the relevant sections is output.
In various embodiments, a generative design application can leverage multi-disciplinary optimization to solve a design problem associated with a 3D model and interdependent design variables. The generative design application includes an optimization engine that can solve the design problem, or portions thereof, by iteratively performing optimization techniques on the interdependent design variables to generate an 3D model that maximizes or minimizes one or more objectives and meets one or more constraints, while simultaneously considering the effect of each interdependency of the design variables. Furthermore, the generative design application can leverage a natural language interface to augment the creation of the design problem for optimization. The generative design application can also leverage free-form deformation during generative design to parameterize a portion of the 3D model as part of the design problem.
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
G06F 40/40 - Processing or translation of natural language
A method and system provide the ability to extend immutable data. A first version of immutable design data is created. The first version of immutable design data is edited to create a second version of immutable design data. Subsequent to the creation of the second version of immutable data, the first version of immutable design data is augmented with first extendable data without creating a new version of immutable data.
Methods, systems, and apparatus, including medium-encoded computer program products for loading and rendering include: obtaining a 3D spatial access tree data structure encoding location information for objects in a 3D model of an environment, wherein the 3D model is stored on a remote computer system; ranking a set of the objects in the 3D model to form an object hierarchy based at least on distances between each object of the set of objects and a specified viewpoint for a user within the environment, as determined using the three-dimensional spatial access tree data structure; selecting a proper subset of the set of objects to be rendered based on the object hierarchy and a current model load limit; downloading the proper subset to the local memory; and rendering the proper subset from the local memory to the display device based on the specified viewpoint within the environment for the user.
Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures include: rendering, to a display device of a first user, a first user's view into an extended reality environment, the first user's view being generated using a first pose being tracked for the first user; identifying that a first avatar associated with the first user has entered a shared perspective mode with a second avatar associated with a second user of the XR environment, wherein the shared perspective mode has a single frame of reference in the XR environment that is shared by the first user and the second user; and performing, while in the shared perspective mode, the rendering to the display device of the first user.
G06T 19/20 - Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
G06F 3/01 - Input arrangements or combined input and output arrangements for interaction between user and computer
G06F 3/0346 - Pointing devices displaced or positioned by the userAccessories therefor with detection of the device orientation or free movement in a 3D space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors
16.
COMPUTER AIDED AUTOMATED SHAPE SYNTHESIS OF PLATE-LIKE STRUCTURES
Methods, systems, and apparatus, including medium-encoded computer program products, for automated shape synthesis including obtaining two or more specified regions of geometry defined in a data structure used by the shape synthesis computer program, producing candidate solutions, each candidate solution of the candidate solutions produced using a different plate generation algorithm to synthesize plates that join the two or more specified regions of geometry with each other through the plates, each of the plates is defined by a base plane, a 2D profile, and a thickness, where the candidate solutions represent new geometry defined in the data structure, and providing, for presentation in a user interface, the candidate solutions and controls to select one or more of the candidate solutions for use in manufacturing one or more physical structures using one or more computer-controlled manufacturing systems, or for use in displaying the selected candidate solutions on a display screen.
G06F 30/20 - Design optimisation, verification or simulation
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 119/18 - Manufacturability analysis or optimisation for manufacturability
17.
COMPUTER AIDED AUTOMATED MODELING OF EDITABLE SKETCHES
Methods, systems, and apparatus, including medium-encoded computer program products, for converting a representation of a 2D curve into a set of simple sketch contours. A 2D curve and predefined geometries are obtained and are converted into a primitive sketch geometry by approximating free regions as segmented from the 2D curve to a set of 2D primitives so as to minimize a number of 2D primitives used in the primitive sketch geometry. The conversion of the 2D curve includes: processing candidate segments determined based on segmenting the free regions according to identified points on the 2D curve that meet a threshold for curve contact with a geometry of the predefined geometries to determine a set of segments for the free regions, and fitting the set of segments to the set of 2D primitives. The primitive sketch geometry is provided for rendering, editing, and/or simulating at a computer-aided design program.
Techniques for generative design based on large language models include receiving a plurality of design examples; evaluating each of the design examples using performance metrics to generate corresponding design attributes for each of the design examples; storing the design examples in a design grid as initial candidate design layouts at a location in the design grid based on the corresponding design attributes; selecting one or more candidate design layouts from the design grid as parent candidate design layouts; generating a new candidate design layout from the parent candidate design layouts; evaluating the new candidate design layout using the performance metrics to generate new design attributes; storing the new candidate design layout in the design grid based on the new design attributes; and generating training data for a large language model based on the candidate design layouts in the design grid and the corresponding design attributes for the candidate design layouts.
A technique for modifying character animations via a user interface, including displaying a timeline that includes frame numbers as time units, displaying a set of keyposes of the character along the timeline based on a set of frame numbers associated with the set of keyposes, displaying a set of trajectories that is superimposed on the set of keyposes, wherein each trajectory in the set of trajectories is displayed as a curve that passes through a particular joint of the character across the set of keyposes, and receiving a modification of a first keypose included in the set of keyposes via the user interface. A technique for automatically extracting a set of keyposes from a character animation based on the plurality of joint trajectories. A technique for retiming segments of a character animation.
A technique for modifying character animations via a user interface, including displaying a timeline that includes frame numbers as time units, displaying a set of keyposes of the character along the timeline based on a set of frame numbers associated with the set of keyposes, displaying a set of trajectories that is superimposed on the set of keyposes, wherein each trajectory in the set of trajectories is displayed as a curve that passes through a particular joint of the character across the set of keyposes, and receiving a modification of a first keypose included in the set of keyposes via the user interface. A technique for automatically extracting a set of keyposes from a character animation based on the plurality of joint trajectories. A technique for retiming segments of a character animation.
A technique for modifying character animations via a user interface, including displaying a timeline that includes frame numbers as time units, displaying a set of keyposes of the character along the timeline based on a set of frame numbers associated with the set of keyposes, displaying a set of trajectories that is superimposed on the set of keyposes, wherein each trajectory in the set of trajectories is displayed as a curve that passes through a particular joint of the character across the set of keyposes, and receiving a modification of a first keypose included in the set of keyposes via the user interface. A technique for automatically extracting a set of keyposes from a character animation based on the plurality of joint trajectories. A technique for retiming segments of a character animation.
G06F 3/04815 - Interaction with a metaphor-based environment or interaction object displayed as three-dimensional, e.g. changing the user viewpoint with respect to the environment or object
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06T 13/40 - 3D [Three Dimensional] animation of characters, e.g. humans, animals or virtual beings
A method and system provide for generating a stormwater overland flow map. Simulation inputs (ground surface data) and simulation outputs (stormwater overland flow maps) are obtained from a deluge simulation model that simulates where water will channel and accumulate on a surface. A convolutional neural network (CNN) is trained to approximate the simulation outputs of the deluge simulation model. The CNN is a sequence of CNN models that each represent a time step and each CNN model in the sequence takes CNN output from a previous CNN model as its input. The CNN output is a video output and a visual representation of stormwater overland flow over time. A new input is a first format is obtained. A collection of custom objects representing points of a grid are stored and includes z-values of points on the surface and stormwater controls. The grid is populated and then processed in/by the CNN.
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
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
A method and system provide the ability to perform a navigation operation of a three-dimensional (3D) model. A 3D model is rendered on a touch screen of a multi-touch device from a camera viewing point a first object of the model is located a first distance from the camera viewing point. An operation (e.g., pan or zoom) is activated using a multi-touch gesture. The operation is performed and behavior of the gesture is adaptive based on the first distance. In alternative embodiments, an inside-outside test is utilized to determine/identify the operation (e.g., an orbit or look-around) is performed. Further, progressive rendering may prioritize objects under the user's focus as defined by finger placement.
G06T 19/00 - Manipulating 3D models or images for computer graphics
G06F 3/04815 - Interaction with a metaphor-based environment or interaction object displayed as three-dimensional, e.g. changing the user viewpoint with respect to the environment or object
G06F 3/0488 - Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures
24.
TECHNIQUES FOR MOTION EDITING FOR CHARACTER ANIMATIONS
A technique for modifying character animations via a user interface, including displaying a timeline that includes frame numbers as time units, displaying a set of keyposes of the character along the timeline based on a set of frame numbers associated with the set of keyposes, displaying a set of trajectories that is superimposed on the set of keyposes, wherein each trajectory in the set of trajectories is displayed as a curve that passes through a particular joint of the character across the set of keyposes, and receiving a modification of a first keypose included in the set of keyposes via the user interface. A technique for automatically extracting a set of keyposes from a character animation based on the plurality of joint trajectories. A technique for retiming segments of a character animation.
In various embodiments, a generative design application can leverage multi-disciplinary optimization to solve a design problem associated with a 3D model and interdependent design variables. The generative design application includes an optimization engine that can solve the design problem, or portions thereof, by iteratively performing optimization techniques on the interdependent design variables to generate an 3D model that maximizes or minimizes one or more objectives and meets one or more constraints, while simultaneously considering the effect of each interdependency of the design variables. Furthermore, the generative design application can leverage a natural language interface to augment the creation of the design problem for optimization. The generative design application can also leverage free-form deformation during generative design to parameterize a portion of the 3D model as part of the design problem.
In various embodiments, a generative design application can leverage multi-disciplinary optimization to solve a design problem associated with a 3D model and interdependent design variables. The generative design application includes an optimization engine that can solve the design problem, or portions thereof, by iteratively performing optimization techniques on the interdependent design variables to generate an 3D model that maximizes or minimizes one or more objectives and meets one or more constraints, while simultaneously considering the effect of each interdependency of the design variables. Furthermore, the generative design application can leverage a natural language interface to augment the creation of the design problem for optimization. The generative design application can also leverage free-form deformation during generative design to parameterize a portion of the 3D model as part of the design problem.
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
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
27.
TECHNIQUES FOR GENERATIVE DESIGN USING MULTI-DISCIPLINARY OPTIMIZATION
In various embodiments, a generative design application can leverage multi-disciplinary optimization to solve a design problem associated with a 3D model and interdependent design variables. The generative design application includes an optimization engine that can solve the design problem, or portions thereof, by iteratively performing optimization techniques on the interdependent design variables to generate an 3D model that maximizes or minimizes one or more objectives and meets one or more constraints, while simultaneously considering the effect of each interdependency of the design variables. Furthermore, the generative design application can leverage a natural language interface to augment the creation of the design problem for optimization. The generative design application can also leverage free-form deformation during generative design to parameterize a portion of the 3D model as part of the design problem.
A technique for generating and displaying treemaps representing tree hierarchies, including receiving, via a treemap user interface, a user selection to view a treemap of a tree hierarchy, determining, for a first parent node included in a plurality of nodes of the tree hierarchy, a total number of descendent nodes of the first parent node within the tree hierarchy, computing, for the first parent node, a first size of a first box based on the total number of descendent nodes of the first parent node, and displaying to the user, via the treemap user interface, the treemap that represents the tree hierarchy and includes the first box, wherein the first box represents the first parent node and has the first size.
In various embodiments, a computer-implemented method for generating context-enriched responses comprises generating a context enrichment based on a context input, combining the context enrichment with a prompt input to generate a context-enriched prompt, and executing a generative machine learning (ML) model on the context-enriched prompt to generate a context-enriched response
A computer-implemented method for generating design objects for computer-aided drawing (CAD) design, comprises combining at least two of a first input received from a first client device and one or more persistent intents to generate a composite prompt, inputting the composite prompt into a trained machine learning (ML) model for execution, receiving a design object generated by the trained ML model in response to the composite prompt; and displaying the design object in a design space that includes the CAD design.
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
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 various embodiments, a computer-implemented method for generating prompt inputs that describe design modifications includes receiving a first prompt input that indicates a first modification to a three-dimensional (3D) design, determining a first context input based on one or more attributes of the 3D design, and causing a first generative machine learning (ML) model to generate a second prompt input based on the first prompt input and the first context input, where the second prompt input indicates a second modification to the 3D design.
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 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G06F 3/04845 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
In various embodiments, a building layout application generates building designs that reduce virus transmissions. The building layout application generates a first layout design set for a building based on a first design vector set and a baseline layout design. The building layout application executes, multiple times, a stochastic multi-agent virus simulator on a layout design included in the first layout design set and a simulation configuration to generate simulation results. The building layout application computes a risk score associated with both the layout design and a virus based on the simulation results. The building layout application executes evolutionary operations(s) on the first design vector set based on the risk score and at least one other risk score to generate a second design vector set. The building layout application generates a second layout design set for the building based on the second design vector set and the baseline layout design.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
G06F 30/20 - Design optimisation, verification or simulation
33.
CONTEXT-ENRICHED PROMPT GENERATION FOR DOMAIN EXPLORATION
In various embodiments, a computer-implemented method for generating context-enriched responses comprises generating a context enrichment based on a context input, combining the context enrichment with a prompt input to generate a context-enriched prompt, and executing a generative machine learning (ML) model on the context-enriched prompt to generate a context-enriched response.
A computer-implemented method for generating design objects for computer-aided drawing (CAD) design, comprises combining at least two of a first input received from a first client device and one or more persistent intents to generate a composite prompt, inputting the composite prompt into a trained machine learning (ML) model for execution, receiving a design object generated by the trained ML model in response to the composite prompt; and displaying the design object in a design space that includes the CAD design.
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
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
35.
TECHNIQUES FOR DEFINING RELATIONSHIPS BETWEEN OBJECTS WITHIN A USER INTERFACE
Various embodiments include a computer-implemented method for generating three-dimensional (3D) assemblies, including receiving a relationship input that associates two or more 3D models included in a 3D assembly, receiving a prompt input that includes a portion of text that describes the relationship input, causing a generative machine learning model to generate a design constraint based on the relationship input and the prompt input, and causing the 3D assembly to incorporate the design constraint.
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
One embodiment of the present invention sets forth a technique for performing style transfer. The technique includes generating an input shape representation that includes a plurality of points near a surface of an input three-dimensional (3D) shape, where the input 3D shape includes content-based attributes associated with an object. The technique also includes determining a style code based on a difference between a first latent representation of a first 3D shape and a second latent representation of a second 3D shape, where the second 3D shape is generated by applying one or more augmentations to the first 3D shape. The technique further includes generating, based on the input shape representation and style code, an output 3D shape having the content-based attributes of the input 3D shape and style-based attributes associated with the style code, and generating a 3D model of the object based on the output 3D shape.
In various embodiments, a computer-implemented method for generating recommendations for a generative design, comprises receiving a selection of a prompt volume within a design space, wherein the design space is generated by a design exploration application, and the prompt volume defines a sphere of influence within the prompt volume, identifying one or more design objects within the prompt volume, generating a plurality of candidate actions associated with the one or more design objects, and displaying, within a recommendation window included in the design space, at least one candidate action from the plurality of candidate actions.
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 30/17 - Mechanical parametric or variational design
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
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable software for manufacturing project management,
configuration and design; downloadable software for
production management and floor tracking, tracking and
analyzing production flow, maintenance operations,
workflows, scheduling, inventory management, and scheduling
and managing production teams, machines and materials in
industrial manufacturing; downloadable software for
connecting, operating, and managing networked devices in the
internet of things, namely, devices used in industrial
manufacturing; software development tools for product
design, manufacturing and managing networked devices in the
internet of things, namely, devices used in industrial
manufacturing; downloadable software for simulation,
visualization, collaboration, data management, communication
with networked devices in the internet of things,
manufacturing process management, optimization and design. Provision of online non-downloadable cloud-based software
for manufacturing project management, configuration and
design; provision of online non-downloadable cloud-based
software for production management and floor tracking,
tracking and analyzing production flow, maintenance
operations, workflows, scheduling, inventory management, and
scheduling and managing production teams, machines and
materials in industrial manufacturing; provision of online
non-downloadable cloud-based software for connecting,
operating, and managing networked devices in the internet of
things, namely devices used in industrial manufacturing;
software as a service (SaaS) services featuring
cloud-connected software tools for product design,
manufacturing and internet of things; providing temporary
use of non-downloadable software via a web site for
simulation, visualization, collaboration, data management,
internet of things communication management, manufacturing
process management, optimization and design.
39.
CONTEXTUAL RECOMMENDATIONS FOR THREE-DIMENSIONAL DESIGN SPACES
In various embodiments, a computer-implemented method for generating recommendations for a generative design, comprises receiving a selection of a prompt volume within a design space, wherein the design space is generated by a design exploration application, and the prompt volume defines a sphere of influence within the prompt volume, identifying one or more design objects within the prompt volume, generating a plurality of candidate actions associated with the one or more design objects, and displaying, within a recommendation window included in the design space, at least one candidate action from the plurality of candidate actions.
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
40.
PROMPT SUGGESTIONS BASED ON HISTORY AND LARGE LANGUAGE MODEL KNOWLEDGE
In various embodiments, a computer-implemented method for generating prompt inputs that describe design modifications includes receiving a first prompt input that indicates a first modification to a three-dimensional (3D) design, determining a first context input based on one or more attributes of the 3D design, and causing a first generative machine learning (ML) model to generate a second prompt input based on the first prompt input and the first context input, where the second prompt input indicates a second modification to the 3D design.
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
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
41.
TRAINING MACHINE LEARNING MODELS TO PERFORM NEURAL STYLE TRANSFER IN THREE-DIMENSIONAL SHAPES
One embodiment of the present invention sets forth a technique for training a machine learning model to perform style transfer. The technique includes applying one or more augmentations to a first input three-dimensional (3D) shape to generate a second input 3D shape. The technique also includes generating, via a first set of neural network layers, a style code based on a first latent representation of the first input 3D shape and a second latent representation of the second input 3D shape. The technique further includes generating, via a second set of neural network layers, a first output 3D shape based on the style code and the second latent representation, and performing one or more operations on the first and second sets of neural network layers based on a first loss associated with the first output 3D shape to generate a trained machine learning model.
A method of modifying metadata associated with a media file is described the method comprising receiving a first media file; receiving a first metadata associated with the first media file; receiving an identifier of a second media file; and modifying the first metadata to include the identifier of the second media file.
G06F 16/78 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
One embodiment of a method for generating program code to control a robot includes receiving user input specifying a task to be performed by the robot, processing the user input via a first machine learning model to generate a plurality of subtasks for performing the task, and for each subtask included in the plurality of subtasks, processing the subtask via a second machine learning model to generate program code for controlling the robot to perform the subtask.
In various embodiments, a computer-implemented method for generating digital content comprises generating a multiparty interface that communicates with at least a trained machine learning (ML) model, a first client device, and a second client device; combining at least a first input from the first client device and a second input from the second client device to generate a composite prompt, transmitting the composite prompt to the trained ML model for execution, receiving a digital content item from the trained ML model that was generated in response to the composite prompt, and displaying the digital content item in the multiparty interface.
In various embodiments, a computer-implemented method for displaying object information associated with a computer-aided design, the method comprising displaying a design space that includes a plurality of design objects, generating a prompt that includes a set of object identifiers corresponding to a first set of design objects included in the plurality of design objects and a first query for a set of object labels corresponding to the first set of design objects, transmitting the prompt to at least one trained machine learning (ML) model for processing, receiving, from the at least one trained ML model, a first ML response that includes the set of object labels corresponding to the first set of design objects, and displaying the set of object labels within the design space.
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
46.
INTERACTIVE SPACE FOR TRACKING INTERACTIONS WITH AN ARTIFICIAL INTELLIGENCE MODEL
In various embodiments, a computer-implemented method for displaying a prompt space comprises displaying a design space comprising one or more design objects, receiving a selection of a current location within the design space, and displaying the prompt space at a placement location within the design space based on the current location.
In various embodiments, a computer-implemented method for generating digital content comprises generating a multiparty interface that communicates with at least a trained machine learning (ML) model, a first client device, and a second client device; combining at least a first input from the first client device and a second input from the second client device to generate a composite prompt, transmitting the composite prompt to the trained ML model for execution, receiving a digital content item from the trained ML model that was generated in response to the composite prompt, and displaying the digital content item in the multiparty interface.
In various embodiments, a computer-implemented method for displaying object information associated with a computer-aided design, the method comprising displaying a design space that includes a plurality of design objects, generating a prompt that includes a set of object identifiers corresponding to a first set of design objects included in the plurality of design objects and a first query for a set of object labels corresponding to the first set of design objects, transmitting the prompt to at least one trained machine learning (ML) model for processing, receiving, from the at least one trained ML model, a first ML response that includes the set of object labels corresponding to the first set of design objects, and displaying the set of object labels within the design space.
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 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
A method and system provide for automating drawing. A drawing of two or more entities is obtained and a resolution is determined. Based on the resolution, the drawing is quantized into a cell map. The cell map is a collection of multiple cells stored in a contiguous memory. Each of the multiple cells is quantized geometry data for and provides a smallest tangible unit of information about a corresponding entity. Each of the multiple cells is a number that represents domain specific information about the corresponding entity. The quantizing rounds off of values beyond a specified threshold to a resolution tolerance of the resolution. The cell map is utilized to automate modifications to the drawing.
A computer-implemented method includes receiving an offset amount for a first boundary representation of a first three-dimensional object, forming a second boundary representation of a second three-dimensional object, the second three-dimensional object being an offset version of the first three-dimensional object, and processing the second boundary representation of the second three-dimensional object for output by a physical device. The forming includes using multi-threading to process respective ones of the connected surface elements to produce offset surface elements for the second boundary representation of the second three-dimensional object in accordance with the offset amount, and identifying portions of the connected surface elements that will not have corresponding portions in the offset surface elements by evaluating a distance field representation of the first three-dimensional object using sampling points taken from the connected surface elements, the offset surface elements, or both.
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 111/18 - Details relating to CAD techniques using virtual or augmented reality
51.
TECHNIQUES FOR CONTROLLING ROBOTS USING DYNAMIC GAIN TUNING
One embodiment of a method for controlling a robot includes generating, via a first trained machine learning model, a robot motion and a predicted force associated with the robot motion, determining, via a second trained machine learning model, a gain associated with the predicted force, generating one or more robot commands based on the robot motion and the gain, and causing a robot to move based on the one or more robot commands.
A method and system provide for extracting an isosurface. A set of three-dimensional (3D) asymmetric sampling grids are created based on a sampling resolution and a region of interest. The set of grids cover a 3D scalar field and are tiles made up of tetrahedrons. The 3D scalar field is evaluated based on the set of 3D asymmetric sampling grids to generate a value for each tile, convert the values to cells, and assign an index to each cell. For each cell, the index is utilized to identify, in a lookup table, an enumerated tetrahedron. For each identified enumerated tetrahedron that contains an isosurface crossing, an isosurface connection is estimated between new points of edges of the identified tetrahedron. A mesh is created to connect the new points and utilized as the extracted isosurface.
A method and system provide the ability to determine a hydrant fire flow. Inputs are obtained A critical element is identified. Based on a physical-based heuristic, a new hydrant fire flow guess is determined. The search direction is evaluated and used to maintain/override (using a heuristic method) the fire flow guess. The new guess is assigned as a hydrant demand. Network pressure and flow values are updated. The constraints are evaluated. The guess is reduced if at least one constraint has been violated and increased of all constraints have been satisfied. The new guess is evaluated for convergence and if not converged, the process repeats.
G06F 30/28 - Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
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
A method and system provide the ability to determine a hydrant fire flow. Inputs are obtained. A critical element is identified. Based on a physical-based heuristic, a new hydrant fire flow guess is determined. The search direction is evaluated and used to maintain/override (using a heuristic method) the fire flow guess. The new guess is assigned as a hydrant demand. Network pressure and flow values are updated. The constraints are evaluated. The guess is reduced if at least one constraint has been violated and increased of all constraints have been satisfied. The new guess is evaluated for convergence and if not converged, the process repeats.
A62C 3/16 - Fire prevention, containment or extinguishing specially adapted for particular objects or places in electrical installations, e.g. cableways
E03B 1/04 - Methods or layout of installations for water supply for domestic or like local supply
A method and system provide the ability to build a game world. A story is obtained that provides a textual narrative of a sequence of events. Plot facilities and a set of constraints are extracted from the story. Each of the plot facilities is a conceptual location where an event happens in the story. Each constraint defines a spatial relation between plot facilities. A map is generated based on the set of constraints by: generating a terrain of two dimensional (2D) polygons that is each associated with a biome type, and assigning each plot facility to a point on the terrain. The assigning complies with a maximum number of constraints and utilizes reinforcement learning (RL) to optimize positions of the points.
A63F 13/60 - Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or game-integrated level editor
A method and system provide the ability to utilize three-dimensional (3D) models to perform a predictive task. Multiple 3D models, consisting of non-Euclidean data, are obtained. Each 3D model is translated into a relational graph with nodes and edges. Each relational graph is processed using a graph neural network (GNN) that computes a node representation per node. The node representations are aggregated into a structural representation of the 3D model. Multiple different views of the 3D model are captured and passed through a convolutional neural network (CNN) to compute a view representation of each view. The view representations are aggregated into a single visual representation. The GNN and CNN are trained using a multiview contrastive training objective to maximize agreement between the structural representation and the single visual representation to form final learned representations. The final learned representation is utilized to perform the predictive task.
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G06F 18/2321 - Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
42 - Scientific, technological and industrial services, research and design
Goods & Services
Software as a service (SAAS) services featuring software using artificial intelligence for enabling users to animate, light, and compose CG (computer generated) 3D characters into videos and 3D environments; providing a website featuring on-line non-downloadable software using artificial intelligence for enabling users to animate, light, and compose CG (computer generated) 3D characters into videos and 3D environments; providing temporary use of on-line non-downloadable cloud computing software using artificial intelligence for enabling users to animate, light, and compose CG (computer generated) 3D characters into videos and 3D environments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Software as a service (SAAS) featuring software in the nature of a cloud-based integrated workflows and workflow environments and data and technology services for creating, collaborating, developing, rendering, manipulating, executing, editing, viewing, reviewing, displaying, and managing on-set camera footage, film dailies, and digital images for use in the field of entertainment
42 - Scientific, technological and industrial services, research and design
Goods & Services
Software as a service (SAAS) featuring software in the nature of a cloud-based integrated workflows and workflow environments and data and technology services for creating, collaborating, developing, rendering, manipulating, executing, editing, viewing, reviewing, displaying, and managing on-set camera footage, film dailies, and digital images for use in the field of entertainment
37 - Construction and mining; installation and repair services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Business consultancy services in the building and
construction industry, including progress claim management
services, construction contract management and
administration services, the provision of all these services
via a telecommunications system or an on-line system,
including via a global telecommunications network. Building and construction services including the provision
of information relating to building construction, the
provision of construction information, building and
construction project management, advisory services in the
field of building and construction and the provision of all
these services via a telecommunications system or an on-line
system, including via a global telecommunications network. Software as a service; website hosting, including hosting
databases and hosting on-line calculators; internet portal
services [designing or hosting] in the building and
construction industry including, progress claims management,
contract management and financial management; computer
software programming and design services; computer software
consultancy, computer software technical support services,
and the online provision of web-based software
(non-downloadable); and the provision of all these services
via a telecommunications system or an on-line system,
including via a global telecommunications network; providing
non-downloadable computer software for server computers,
desktop computers, portable and handheld electronic devices,
laptop computers, tablet computers, global positioning
system enabled devices, personal digital assistants,
smartphones and mobile telephones, all such computer
software accessible via a telecommunications system or an
on-line system, including via a global telecommunications
network; information technology consultancy services in the
field of building and construction and the provision of this
service via a telecommunications system or an on-line
system, including via a global telecommunications network.
37 - Construction and mining; installation and repair services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Business consultancy services in the building and
construction industry, including progress claim management
services, construction contract management and
administration services, the provision of all these services
via a telecommunications system or an on-line system,
including via a global telecommunications network. Building and construction services including the provision
of information relating to building construction, the
provision of construction information, building and
construction project management, advisory services in the
field of building and construction and the provision of all
these services via a telecommunications system or an on-line
system, including via a global telecommunications network. Software as a service; website hosting, including hosting
databases and hosting on-line calculators; internet portal
services [designing or hosting] in the building and
construction industry including, progress claims management,
contract management and financial management; computer
software programming and design services; computer software
consultancy, computer software technical support services,
and the online provision of web-based software
(non-downloadable); and the provision of all these services
via a telecommunications system or an on-line system,
including via a global telecommunications network; providing
non-downloadable computer software for server computers,
desktop computers, portable and handheld electronic devices,
laptop computers, tablet computers, global positioning
system enabled devices, personal digital assistants,
smartphones and mobile telephones, all such computer
software accessible via a telecommunications system or an
on-line system, including via a global telecommunications
network; information technology consultancy services in the
field of building and construction and the provision of this
service via a telecommunications system or an on-line
system, including via a global telecommunications network.
62.
GENERATIVE DESIGN SHAPE OPTIMIZATION BASED ON A TARGET PART RELIABILITY FOR COMPUTER AIDED DESIGN AND MANUFACTURING
Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using generative design processes. A method includes: obtaining a design space and design criteria for a modeled object including a design constraint on an acceptable likelihood of failure, wherein a statistical model that relates a structural performance metric to specific likelihoods of failure for material(s) is used to translate between the acceptable likelihood of failure and a value for the structural performance metric; iteratively modifying a generatively designed shape of the modeled object in the design space in accordance with the design criteria including the design constraint to stay under the acceptable likelihood of failure for the physical structure, wherein the numerical simulation includes computing the structural performance metric, which is evaluated against the design constraint; and providing the generatively designed shape of the modeled object for use in manufacturing a physical structure.
G05B 19/4099 - Surface or curve machining, making 3D objects, e.g. desktop manufacturing
G05B 19/4093 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by part programming, e.g. entry of geometrical information as taken from a technical drawing, combining this with machining and material information to obtain control information, named part programme, for the NC machine
G06F 30/17 - Mechanical parametric or variational design
63.
SKETCH ANALYSIS FOR GENERATIVE DESIGN VIA MACHINE LEARNING MODELS
In various embodiments, a computer-implemented method for performing an analysis of a sketch to identify one or more objects for a generative design, the method comprising receiving the sketch via a user interference, executing a first trained machine learning (ML) model that generates an identification of a first object included in the sketch based on one or more contextual features that are associated with the first object and also are included in the sketch, transmitting the identification to a second machine learning model, and executing a second trained ML model that generates a first design object based on the identification.
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
64.
GENERATIVE DESIGN SHAPE OPTIMIZATION WITH SINGULARITIES AND DISCONNECTION PREVENTION FOR COMPUTER AIDED DESIGN AND MANUFACTURING
Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures include a method including: obtaining one or more design criteria for a modeled object; iteratively modifying a three dimensional shape of the modeled object in accordance with the one or more design criteria, wherein the iteratively modifying comprises regulating shape change velocities for an implicit surface representation of the three dimensional shape that exceed a reference velocity; and providing the three dimensional shape of the modeled object for use in manufacturing a physical structure corresponding to the modeled object using one or more computer-controlled manufacturing systems. Further, regulating the shape change velocities can include reducing the shape change velocities above the reference velocity in accordance with a function.
B33Y 50/00 - Data acquisition or data processing for additive manufacturing
G05B 19/41 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by interpolation, e.g. the computation of intermediate points between programmed end points to define the path to be followed and the rate of travel along that path
Methods, systems, and apparatus, including medium-encoded computer program products, for collaboration and view sharing between users when performing editing operations over a shared document. A first portion of a shared document is displayed to a first user in a user interface of a first instance of a collaboration application of a first user. The displayed first portion comprises a first location of the first user within the shared document. In the user interface, an indication specifying a relative locational direction from the first location towards a second location of a second user within the shared document is provided. A second portion of the shared document is being displayed to the second user through a second instance of the collaboration application during a conference call between a set of users, where the displayed second portion includes the second location of the second user within the shared document.
In various embodiments, a computer-implemented method for performing an analysis of a sketch to identify one or more objects for a generative design, the method comprising receiving the sketch via a user interference, executing a first trained machine learning (ML) model that generates an identification of a first object included in the sketch based on one or more contextual features that are associated with the first object and also are included in the sketch, transmitting the identification to a second machine learning model, and executing a second trained ML model that generates a first design object based on the identification.
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 various embodiments, examples of the disclosure provide systems and methods for generating a scene image. A plurality of input image tiles based upon at least one user input are obtained. Spatial positioning of each input image tile included in the plurality of input image tiles relative to at least one other input image tile included in the plurality of input image tiles is detected, and then a scene composition of the scene determined based upon the spatial positioning of each input image tile included in the plurality of image tiles. A scene prompt associated with the scene is obtained, and a machine learning model blends the plurality of image tiles based upon the scene prompt to generate the scene.
In various embodiments, a computer-implemented method for generating a design object comprises generating a prompt within a design space generated by a design exploration application, wherein the prompt has a prompt definition that includes at least design intent text, and a prompt volume that occupies a portion of the design space and exerts a sphere of influence within the prompt volume, executing a trained machine learning (ML) model on the prompt to generate the design object, and displaying the design object within the prompt volume.
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
69.
TECHNIQUES FOR AUTOMATICALLY DESIGNING STRUCTURAL SYSTEMS FOR BUILDINGS
In various embodiments, a gravity design application automatically generates a design for a structural system of a building. The gravity design application performs partitioning operation(s) based on an outline of a first floor included in a computer-aided design of the building to generate a set of segments. Subsequently, the gravity design application generates a set of segment designs based on the set of segments, constraint(s), and design objective(s). The set of segment designs includes at least one segment design for each of the segments included in the set of segments. The gravity design application determines a combination of floor designs from multiple sets of floor designs based the design objective(s), where each set of floor designs is associated with a different floor of the computer-aided design of the building. The gravity design application generates the design for the structural system of the building based on the combination of floor designs.
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
G06F 111/06 - Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
70.
TECHNIQUES FOR STORING AND ACCESSING DATA USING AN INFORMATION MODEL
Techniques are disclosed for storing and accessing data in an information model. In some embodiments, data is stored in the information model using assets, components, and relationships, or using any other suitable transformation of the data. Each asset is a container for components. Each component stores data according to a schema that defines the organization of data in the component. Assets and components can be related to other assets and/or other components. A file can be imported into the information model by extracting data from the file, creating assets and components in the information model based on the extracted data, and wiring together the created assets and components. In addition or alternatively, an application can invoke an API to write data to the information model. An application can also invoke the API to query the information model, such as to read data from the information model.
In various embodiments, a computer-implemented method for displaying a prompt space, the method comprising displaying a design space comprising one or more design objects, receiving a selection of a current location within the design space, and displaying the prompt space at a placement location within the design space based on the current location. In other embodiments, a computer-implemented method for displaying a prompt history, the method comprising displaying a design space that includes a first design object, displaying a first prompt-history marker within the design space, the first prompt-history marker representing a first prompt history associated with the first design object, and in response to receiving a selection for viewing the first prompt history, displaying the first prompt history in a prompt space within the design space.
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 30/17 - Mechanical parametric or variational design
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 various embodiments, a computer-implemented method for generating a design object comprises generating a prompt within a design space generated by a design exploration application, wherein the prompt has a prompt definition that includes at least design intent text, and a prompt volume that occupies a portion of the design space and exerts a sphere of influence within the prompt volume, executing a trained machine learning (ML) model on the prompt to generate the design object, and displaying the design object within the prompt volume.
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 30/17 - Mechanical parametric or variational design
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
Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design, including obtaining, by a shape modeling computer program, a selection of first geometry defined in a data structure used by the shape modeling computer program to represent a three-dimensional model of an object and an indication of an amount of desired prismatification, wherein the first geometry is defined in the data structure using a control mesh for a smooth surface representation of the first geometry. The shape modeling computer program produces second geometry defined in the data structure based on the indication of the amount of desired prismatification, wherein the second geometry replaces the first geometry in representing the three-dimensional model of the object. The shape modeling computer program provides the three-dimensional model of the object, with the second geometry included in the three-dimensional model.
In various embodiments, a computer-implemented method for generating a design object comprises combining at least two of a design intent text or one or more non-textual inputs to generate a multimodal prompt, executing a trained machine learning (ML) model on the multimodal prompt to generate a design object, and displaying the design object in a design space.
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 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
75.
DESIGN SPACE WITH INTEGRATED PROMPT SPACE FOR MACHINE LEARNING MODELS
In various embodiments, a computer-implemented method for displaying a prompt space, the method comprising displaying a design space comprising one or more design objects, receiving a selection of a current location within the design space, and displaying the prompt space at a placement location within the design space based on the current location. In other embodiments, a computer-implemented method for displaying a prompt history, the method comprising displaying a design space that includes a first design object, displaying a first prompt-history marker within the design space, the first prompt-history marker representing a first prompt history associated with the first design object, and in response to receiving a selection for viewing the first prompt history, displaying the first prompt history in a prompt space within the design space.
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 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
76.
DESIGN SPACE WITH INTEGRATED PROMPT SPACE FOR MACHINE LEARNING MODELS
In various embodiments, a computer-implemented method for displaying a prompt space, the method comprising displaying a design space comprising one or more design objects, receiving a selection of a current location within the design space, and displaying the prompt space at a placement location within the design space based on the current location. In other embodiments, a computer-implemented method for displaying a prompt history, the method comprising displaying a design space that includes a first design object, displaying a first prompt-history marker within the design space, the first prompt-history marker representing a first prompt history associated with the first design object, and in response to receiving a selection for viewing the first prompt history, displaying the first prompt history in a prompt space within the design space.
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 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided generative task scheduling, include: obtaining a dataset describing a schedule of one or more projects to be rescheduled, the dataset including tasks to be scheduled, resource requirements, and dependencies between or among the tasks; generating variations of the schedule, each of the variations having different characteristics that determine for the tasks in the schedule when each task is scheduled in time, and each of the variations satisfying the resource requirements and the dependencies; selecting among the different characteristics for the tasks in the variations of the schedule to form a revised schedule of the one or more projects; and providing the revised schedule of the one or more projects for display to a user or for output to manage the one or more projects.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Downloadable software for manufacturing project management, configuration and design; downloadable software for production management and floor tracking, tracking and analyzing production flow, maintenance operations, workflows, scheduling, inventory management, and scheduling and managing production teams, machines and materials in industrial manufacturing; downloadable software for connecting, operating, and managing networked devices in the internet of things, namely, devices used in industrial manufacturing; software development tools for product design, manufacturing and managing networked devices in the internet of things, namely, devices used in industrial manufacturing; downloadable software for simulation, visualization, collaboration, data management, communication with networked devices in the internet of things, manufacturing process management, optimization and design. (1) Provision of online non-downloadable cloud-based software for manufacturing project management, configuration and design; provision of online non-downloadable cloud-based software for production management and floor tracking, tracking and analyzing production flow, maintenance operations, workflows, scheduling, inventory management, and scheduling and managing production teams, machines and materials in industrial manufacturing; provision of online non-downloadable cloud-based software for connecting, operating, and managing networked devices in the internet of things, namely devices used in industrial manufacturing; software as a service (SaaS) services featuring cloud-connected software tools for product design, manufacturing and internet of things; providing temporary use of non-downloadable software via a web site for simulation, visualization, collaboration, data management, internet of things communication management, manufacturing process management, optimization and design.
79.
Techniques for storing and accessing data using an information model
Techniques are disclosed for storing and accessing data in an information model. In some embodiments, data is stored in the information model using assets, components, and relationships, or using any other suitable transformation of the data. Each asset is a container for components. Each component stores data according to a schema that defines the organization of data in the component. Assets and components can be related to other assets and/or other components. A file can be imported into the information model by extracting data from the file, creating assets and components in the information model based on the extracted data, and wiring together the created assets and components. In addition or alternatively, an application can invoke an API to write data to the information model. An application can also invoke the API to query the information model, such as to read data from the information model.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable software for manufacturing project management, configuration and design; Downloadable software for production management and floor tracking, tracking and analyzing production flow, maintenance operations, workflows, scheduling, inventory management, and scheduling and managing production teams, machines and materials in industrial manufacturing; Downloadable software for connecting, operating, and managing networked devices in the internet of things, namely, devices used in industrial manufacturing; Software development tools for product design, manufacturing and managing networked devices in the internet of things, namely, devices used in industrial manufacturing; Downloadable software for simulation, visualization, collaboration, data management, communication with networked devices in the internet of things, manufacturing process management, optimization and design Non-downloadable, cloud-based software for manufacturing project management, configuration and design; Non-downloadable cloud-based software for production management and floor tracking, tracking and analyzing production flow, maintenance operations, workflows, scheduling, inventory management, and scheduling and managing production teams, machines and materials in industrial manufacturing; Non-downloadable cloud-based software for connecting, operating, and managing networked devices in the internet of things, namely devices used in industrial manufacturing; Software as a service (SAAS) services featuring cloud-connected software tools for product design, manufacturing and internet of things; Providing a web site featuring temporary use of non-downloadable software for simulation, visualization, collaboration, data management, internet of things communication management, manufacturing process management, optimization and design
81.
TECHNIQUES FOR ENABLING CONVERSATIONAL USER INTERFACES FOR SIMULATION APPLICATIONS VIA LANGUAGE MODELS
One embodiment of a method for determining user intent includes receiving user input that comprises first natural language text, performing one or more operations to map the user input to one or more classes of intents included in a plurality of classes of intents, and responsive to determining that the user input does not map to any class of intents, generating, via a first trained language model, second natural language text requesting additional user input.
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
One embodiment of a method for determining user intent includes receiving user input that comprises first natural language text, performing one or more operations to map the user input to one or more classes of intents included in a plurality of classes of intents, and responsive to determining that the user input does not map to any class of intents, generating, via a first trained language model, second natural language text requesting additional user input.
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
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
83.
TECHNIQUES FOR ENABLING CONVERSATIONAL USER INTERFACES FOR SIMULATION APPLICATIONS VIA LANGUAGE MODELS
One embodiment of a method for determining user intent includes receiving user input that comprises first natural language text, performing one or more operations to map the user input to one or more classes of intents included in a plurality of classes of intents, and responsive to determining that the user input does not map to any class of intents, generating, via a first trained language model, second natural language text requesting additional user input.
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
37 - Construction and mining; installation and repair services
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Business consultancy services in the building and construction industry, including progress claim management services, construction contract management and administration services, the provision of all these services via a telecommunications system or an on-line system, including via a global telecommunications network.
(2) Building and construction services including the provision of information relating to building construction, the provision of construction information, building and construction project management, advisory services in the field of building and construction and the provision of all these services via a telecommunications system or an on-line system, including via a global telecommunications network.
(3) Software as a service; website hosting, including hosting databases and hosting on-line calculators; internet portal services [designing or hosting] in the building and construction industry including, progress claims management, contract management and financial management; computer software programming and design services; computer software consultancy, computer software technical support services, and the online provision of web-based software (non-downloadable); and the provision of all these services via a telecommunications system or an on-line system, including via a global telecommunications network; providing non-downloadable computer software for server computers, desktop computers, portable and handheld electronic devices, laptop computers, tablet computers, global positioning system enabled devices, personal digital assistants, smartphones and mobile telephones, all such computer software accessible via a telecommunications system or an on-line system, including via a global telecommunications network; information technology consultancy services in the field of building and construction and the provision of this service via a telecommunications system or an on-line system, including via a global telecommunications network.
37 - Construction and mining; installation and repair services
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Business consultancy services in the building and construction industry, including progress claim management services, construction contract management and administration services, the provision of all these services via a telecommunications system or an on-line system, including via a global telecommunications network.
(2) Building and construction services including the provision of information relating to building construction, the provision of construction information, building and construction project management, advisory services in the field of building and construction and the provision of all these services via a telecommunications system or an on-line system, including via a global telecommunications network.
(3) Software as a service; website hosting, including hosting databases and hosting on-line calculators; internet portal services [designing or hosting] in the building and construction industry including, progress claims management, contract management and financial management; computer software programming and design services; computer software consultancy, computer software technical support services, and the online provision of web-based software (non-downloadable); and the provision of all these services via a telecommunications system or an on-line system, including via a global telecommunications network; providing non-downloadable computer software for server computers, desktop computers, portable and handheld electronic devices, laptop computers, tablet computers, global positioning system enabled devices, personal digital assistants, smartphones and mobile telephones, all such computer software accessible via a telecommunications system or an on-line system, including via a global telecommunications network; information technology consultancy services in the field of building and construction and the provision of this service via a telecommunications system or an on-line system, including via a global telecommunications network.
37 - Construction and mining; installation and repair services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Business consultancy services in the building and construction industry, including progress claim management services, construction contract management and administration services, the provision of all these services via a telecommunications system or an on-line system, including via a global telecommunications network Building and construction services including the provision of information relating to building construction, the provision of construction information, building and construction project management, advisory services in the field of building and construction and the provision of all these services via a telecommunications system or an on-line system, including via a global telecommunications network Software as a service; website hosting, including hosting databases and hosting on-line calculators; internet portal services [designing or hosting] in the building and construction industry including, progress claims management, contract management and financial management; computer software programming and design services; computer software consultancy, computer software technical support services, and the online provision of web-based software (non-downloadable); and the provision of all these services via a telecommunications system or an on-line system, including via a global telecommunications network; providing non-downloadable computer software for server computers, desktop computers, portable and handheld electronic devices, laptop computers, tablet computers, global positioning system enabled devices, personal digital assistants, smartphones and mobile telephones, all such computer software accessible via a telecommunications system or an on-line system, including via a global telecommunications network; information technology consultancy services in the field of building and construction and the provision of this service via a telecommunications system or an on-line system, including via a global telecommunications network
37 - Construction and mining; installation and repair services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Business consultancy services in the building and construction industry, including progress claim management services, construction contract management and administration services, the provision of all these services via a telecommunications system or an on-line system, including via a global telecommunications network Building and construction services including the provision of information relating to building construction, the provision of construction information, building and construction project management, advisory services in the field of building and construction and the provision of all these services via a telecommunications system or an on-line system, including via a global telecommunications network Software as a service; website hosting, including hosting databases and hosting on-line calculators; internet portal services [designing or hosting] in the building and construction industry including, progress claims management, contract management and financial management; computer software programming and design services; computer software consultancy, computer software technical support services, and the online provision of web-based software (non-downloadable); and the provision of all these services via a telecommunications system or an on-line system, including via a global telecommunications network; providing non-downloadable computer software for server computers, desktop computers, portable and handheld electronic devices, laptop computers, tablet computers, global positioning system enabled devices, personal digital assistants, smartphones and mobile telephones, all such computer software accessible via a telecommunications system or an on-line system, including via a global telecommunications network; information technology consultancy services in the field of building and construction and the provision of this service via a telecommunications system or an on-line system, including via a global telecommunications network
88.
Hybrid Reinforcement Learning (RL) to Control a Water Distribution Network
A method and system control a water distribution network. A database is maintained of prior states based on a residential water demand, a tank level, and an energy tariff. A current state of the water distribution network is determined. Rewards are determined and include a tank level constraint, an energy cost, and a toggle count. A query based model is used to determine a set of control points used to control a first prior state. An RL agent is trained based on the prior states and rewards. The RL agent determines a control setpoint (that changes the pump speed) that maintains the tank level, minimizes the energy cost, and complies with the toggle count. The RL agent determines time slots and selects one of the time slots. Hybrid setpoints are generated to control the water distribution network within the selected time slot.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
A method and system provide for searching a computer-aided design (CAD) drawing. A CAD drawing is obtained and includes vector based geometric entities. For each entity, primitives are extracted. A feature coordinate system is created for each of the entities using the primitives. The primitives are transformed from a world coordinate system to feature coordinates of a feature coordinate system. Geometry data of the transformed entities is encoded into index codes that are utilized in an index table as keys. A target geometric entity is identified and a target index code is determined and used to query the index table to identify instances of the target geometric entity in the CAD drawing.
During a sampling stage, a system enables a user to capture samples of 3D digital components within an immersive environment. The 3D digital component can include a 3D object that is rendered and displayed within the immersive environment. The 3D digital components can also include object-property components used to render a 3D object, such as texture, color scheme, animation, motion path, or physical parameters. The samples of the 3D digital components are stored to a sample-palette data structure (SPDS) that organizes the samples. During a remix stage, the system enables a user to apply a sample stored to the SPDS to modify a 3D object and/or an immersive environment. The user can add a sampled object to an immersive environment to modify the immersive environment. The user can apply one or more object-based samples to a 3D object to modify one or more object properties of the 3D object.
Methods, systems, and apparatus, including medium-encoded computer program products, including: receiving, by a shape modeling computer program, a selection of first geometry defined in a data structure used by the computer program to represent a three-dimensional model of an object and an indication of an amount of complexity reduction, The computer program produces a second geometry defined in the data structure based on the indication of the amount of indicated complexity reduction and taking into account local shape curvature for the first geometry, where the second geometry replaces the first geometry in representing the three-dimensional model of the object. The computer program provides the three-dimensional model of the object, with the second geometry included in the three-dimensional model, for use in manufacturing a physical structure corresponding to the object using one or more computer-controlled manufacturing systems, or for use in displaying the object on a display screen.
During a sampling stage, a system enables a user to capture samples of 3D digital components within an immersive environment. The 3D digital component can include a 3D object that is rendered and displayed within the immersive environment. The 3D digital components can also include object-property components used to render a 3D object, such as texture, color scheme, animation, motion path, or physical parameters. The samples of the 3D digital components are stored to a sample-palette data structure (SPDS) that organizes the samples. During a remix stage, the system enables a user to apply a sample stored to the SPDS to modify a 3D object and/or an immersive environment. The user can add a sampled object to an immersive environment to modify the immersive environment. The user can apply one or more object-based samples to a 3D object to modify one or more object properties of the 3D object.
A method and system provide the ability to estimate the vulnerability of a repairable infrastructure system. A survival curve is constructed for one or more assets. A rehabilitation plan is prescribed for one or more failure states of the repairable infrastructure system. A cost estimation model is constructed for costs associated with the repairs for each of the failure states. A planning basis is specified. A multiple probability simulation is conducted that estimates a potential restoration cost for a possible failure. The simulation is repeated to acquire a distribution of potential restoration costs. A vulnerability estimation is determined and provided based on the distribution.
H04L 41/0823 - Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
H04L 41/0826 - Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability for reduction of network costs
Providing a website featuring news and information in the field of computer software design and development; administration of a membership program that enables participants to share knowledge, best practices, expertise, and participate in exclusive meetings and events relating to software in the fields of computer aided design, graphics, construction management, architectural design and computer software usage; arranging, organizing and conducting meetups and events in the field of computer software design Providing online chatrooms and electronic bulletin boards for transmission of messages among users in the field of computer software design and development; computer services, namely providing online facilities for real-time interaction with others concerning design and development of software in the fields of computer aided design, graphics, construction management, architectural design and computer software usage; transmission of podcasts
95.
INTERACTIVE GENERATIVE DESIGN WITH SENSITIVITY ANALYSIS AND PROBABILITY VISUALIZATION FOR CATEGORICAL DESIGN VARIABLES
Techniques for interactive generative design with sensitivity analysis and probability visualization for categorical design variables include a computer-implemented method for evaluating an impact of categorical design variables on a design problem solution comprises receiving information regarding choices for one or more categorical design variables associated with each of a plurality of design members of a design problem, determining a respective sensitivity of an objective function to the choices for the one or more categorical design variables for each design member of the plurality of design members, determining a respective visual aspect for each design member based on the respective sensitivity, displaying, on a user interface, a graphical depiction of the plurality of design members, wherein each design member is displayed using the respective visual aspect, and displaying, on the user interface, a key for interpreting the respective visual aspects.
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.
INTERACTIVE GENERATIVE DESIGN WITH SENSITIVITY ANALYSIS AND PROBABILITY VISUALIZATION FOR CATEGORICAL DESIGN VARIABLES
Techniques for generative design include a computer-implemented method for solving a design problem comprising initializing values for one or more categorical and continuous design variables, and performing a design iteration by generating sample vectors for each of one or more categorical design variables based on the categorical design variable probabilities, solving one or more governing equations for the design problem based on values of the continuous design variables and the sample vectors, computing a value of one or more constraint functions and an objective function, computing first gradients of the objective function and the constraint functions with respect to each of the continuous design variables, computing second gradients of the objective function and the constraint functions with respect to the categorical design variable probabilities, and updating values for the continuous design variables based on the first gradients and values for the categorical design variable probabilities based on the second gradients.
Techniques for generative design include a computer-implemented method for solving a design problem comprising initializing values for one or more categorical design variable probabilities and one or more continuous design variables, and performing a design iteration by performing one or more iterations to update the categorial design variable probabilities by generating sample vectors for each of one or more categorical design variables based on the categorical design variable probabilities, computing first gradients of an objective function and one or more constraint functions with respect to the categorical design variable probabilities, and updating values for the categorical design variable probabilities based on the first gradients, then updating the sample vectors based on the updated categorical design variable probability values, computing second gradients of the objective and constraint functions with respect to each of the continuous design variables, and updating values for the continuous design variables based on the second gradients.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
98.
ITERATIVE SHAPE MODIFICATION PROVIDING MAXIMUM SUSTAINABLE LOADS DURING COMPUTER AIDED GENERATIVE DESIGN
Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures include: obtaining a design space for a modeled object and boundary conditions including a location where load is applied; checking whether, the boundary conditions include a specified direction for the load; assigning a direction for the load at the location when no specified direction is included; iteratively modifying a three dimensional shape in the design space in accordance with a physical response of the modeled object determined by a numerical simulation employing a linear analysis, where the iterative modification comprises determining a respective maximum sustainable load for each of two or more versions of the modified three dimensional shape; presenting to a user the two or more versions of the modeled object having different shapes; and receiving a user selection of one of the two or more versions of the modeled object.
G06F 30/20 - Design optimisation, verification or simulation
B29C 64/393 - Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
B33Y 50/02 - Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
A feedback mechanism that reports software issues between users of software applications and the developers of the software applications. The feedback mechanism generates feedback logs that capture moments of user frustration at the moment a user encounters issues with using a particular software application executing on a client device. The feedback mechanism is triggered to generate a feedback log by the user via a predetermined set of user inputs. Once generated, the feedback log captures an associated importance level, a user description, and/or context information (such as application and command activity information) for the particular software application and one or more other software applications that interacted with the particular software application executing on the client device. The feedback log can also capture multimedia content such as audio, images, and videos. The feedback log is then transmitted to a server of a developer of the particular software application.
A computer-implemented method includes obtaining an engineering model of a physical structure, the engineering model specifying structural elements distributed among different levels of the structure; obtaining a value of a cost metric for each of the structural elements; determining an accumulated value of the cost metric for at least one area of an architectural massing model of the structure, the at least one area being associated with a predetermined level of the different levels of the structure, the determining including calculating a direct contribution to the accumulated value of the cost metric, and calculating an indirect contribution to the accumulated value of the cost metric; and displaying the accumulated value of the cost metric for the at least one area of the architectural massing model of the structure.
