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    METHODS AND SYSTEMS FOR GENERATING TASK AGNOSTIC REPRESENTATIONS
    1.
    发明申请
    METHODS AND SYSTEMS FOR GENERATING TASK AGNOSTIC REPRESENTATIONS 有权

    公开(公告)号：US20250068910A1

    公开(公告)日：2025-02-27

    申请号：US18455518

    申请日：2023-08-24

    Applicant: MASTERCARD INTERNATIONAL INCORPORATED

    Inventor： Aakarsh Malhotra ,  Akshay Sethi ,  Sonia Gupta ,  Siddhartha Asthana

    IPC: G06N3/084 ,  G06N3/0475

    Abstract: Methods and server systems for generating task-agnostic representations for nodes in bipartite graph are described herein. Method performed by server system includes accessing bipartite graph including first set of nodes and second set of nodes. Herein, set of edges exist between first and second set of nodes. Method includes performing for each node of first and second set of nodes: identifying a natural neighbor node, the natural neighbor node being a two-hop neighbor node from the each node, Then, generating temporary representation for one-hop neighbor node based on set of features corresponding to the one-hop neighbor node Then, generating temporary neighbor node based on temporary representation for the one-hop neighbor node. Then, generating augmented neighborhood based on the natural node and the temporary neighbor node, and then determining via machine learning model, task-agnostic representation for the each node based on augmented neighborhood.

    SYSTEM AND METHOD FOR GENERATING A NEW COMPUTER GAME UTILIZING MACHINE LEARNING
    3.
    发明申请
    SYSTEM AND METHOD FOR GENERATING A NEW COMPUTER GAME UTILIZING MACHINE LEARNING 有权

    公开(公告)号：US20250065233A1

    公开(公告)日：2025-02-27

    申请号：US18809618

    申请日：2024-08-20

    Applicant: PLAYO LTD

    Inventor： Ilan GRACIER ,  Yaron LEIFENBERG

    IPC: A63F13/69 ,  A63F13/79 ,  G06N3/0475

    Abstract: A system and method for generating a new computer game including: training a machine learning model to generate positions and properties of second in-game units over time in the second computer game, by providing the machine learning model data descriptive of positions and properties of a plurality of first in-game units over time in a first computer game; and generating the second computer game by deriving a computerized restrictive environment and dispersing the second in-game units in the second computer game based on the generated positions and properties of the second in-game units over time in the second computer game.

    DATA GENERATION
    4.
    发明申请
    DATA GENERATION 有权

    公开(公告)号：US20250061311A1

    公开(公告)日：2025-02-20

    申请号：US18746532

    申请日：2024-06-18

    Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

    Inventor： Zeyang LEI ,  Siqi BAO ,  Hua WU ,  Haifeng WANG

    IPC: G06N3/0475 ,  G06N3/08

    Abstract: A data generation method is provided. The data generation method includes: generating first answer data based on first question data from a user; determining, in response to receiving negative feedback from the user for the first answer data, a first reflection result for the first answer data based on the first answer data and the negative feedback, wherein the first reflection result indicates a diagnosis reason why feedback from the user for the first answer data is negative; and generating second answer data for the first question data based on the first question data and the first reflection result.

    SYSTEM AND METHOD FOR DESIGNING CURING PROCESSES USING GENERATIVE AI
    5.
    发明申请
    SYSTEM AND METHOD FOR DESIGNING CURING PROCESSES USING GENERATIVE AI 有权

    公开(公告)号：US20250053794A1

    公开(公告)日：2025-02-13

    申请号：US18902142

    申请日：2024-09-30

    Applicant: Quantiphi, Inc.

    Inventor： Dagnachew Birru ,  Anirudh Deodhar ,  Milad Ramezankhani ,  Muneeswaran I ,  Saisubramaniam Gopalakrishnan ,  Goutham Vignesh Saravanan

    IPC: G06N3/0475

    Abstract: A method and system for designing curing processes is disclosed. The method includes obtaining design requirements using a requirement gathering agent, determining potential ranges of recipes and operating conditions with an operations specialist agent, identifying suitable material options and assessing corresponding properties with a material specialist agent, defining dimensions and shape aspects of the design using a design specialist agent, extracting and reasoning relevant information with a knowledge processing and retrieval agent, formulating a final requirement specification with an experiment enabler agent, generating a first set of experiments based on the final requirement specifications using an experiment designer, performing, by a predictive model design agent implementing a prediction model, real-time predictions for dynamic conditions, updating, by a predictive model optimizer agent implementing a continual learning framework, the prediction model in real-time; and optimizing and updating, by a process optimizer agent, the prediction model iteratively based on feedback from experiments and simulations.

    Generating styles for neural style transfer in three-dimensional shapes
    6.
    发明授权
    Generating styles for neural style transfer in three-dimensional shapes 有权

    公开(公告)号：US12223611B2

    公开(公告)日：2025-02-11

    申请号：US18149609

    申请日：2023-01-03

    Applicant: AUTODESK, INC.

    Inventor： Hooman Shayani ,  Marco Fumero ,  Aditya Sanghi

    IPC: G06T19/20 ,  G06N3/0455 ,  G06N3/0475 ,  G06N3/08 ,  G06N3/092 ,  G06T17/00 ,  G06T17/10

    Abstract: 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.

    GAN IMAGE GENERATION FROM FEATURE REGULARIZATION
    7.
    发明申请
    GAN IMAGE GENERATION FROM FEATURE REGULARIZATION 有权

    公开(公告)号：US20250037431A1

    公开(公告)日：2025-01-30

    申请号：US18357621

    申请日：2023-07-24

    Applicant: ADOBE INC.

    Inventor： Min Jin Chong ,  Krishna Kumar Singh ,  Yijun Li ,  Jingwan Lu

    IPC: G06V10/774 ,  G06N3/045 ,  G06N3/0475

    Abstract: Systems and methods for training a Generative Adversarial Network (GAN) using feature regularization are described herein. Embodiments are configured to generate a candidate image using a generator network of a GAN, classify the candidate image as real or generated using a discriminator network of the GAN, and train the GAN to generate realistic images based on the classifying of the candidate image. The training process includes regularizing a gradient with respect to features extracted using a discriminator network of the GAN.

    Multi-Node Influence Based Artificial Intelligence Topology With Influence Data
    10.
    发明申请
    Multi-Node Influence Based Artificial Intelligence Topology With Influence Data 有权

    公开(公告)号：US20250021797A1

    公开(公告)日：2025-01-16

    申请号：US18767225

    申请日：2024-07-09

    Applicant: Fantagic Holdings LLC

    Inventor： James Duane Bennett ,  Bindu Rama Rao

    IPC: G06N3/045 ,  G06N3/0475

    Abstract: A multi-node artificial intelligence topology adapts to service many different overall purposes. Support processing nodes, discriminative AI elements, generative AI elements along with input, output and communication circuitry along with other outside interactions provide the nodal basis for the overall topology. Therewithin, outputs of several nodes drive a single node which uses influence balancing to optimize its own output. Influence is delivered in feed forward and feed back manner. Segmented processing is provided where sections of an overall output goal is processed through the topology in segments, e.g., chapter by chapter of a novel, episode by episode, a full topology processing using internal cross node influence followed by a second full topology processing using both internal cross node and cross segment influence. Pseudo random templating providing constraints used to progress through segments to control an output flow. AI elements can be fully software, use acceleration circuitry, and employ neural network circuitry such as analog and digital versions thereof. Topologies also adapt between local and remote processing locations on a node by node basis, where, for example, some AI elements or nodes operate in the cloud, while other AI elements operate on a particular user's device or other user devices located remotely. Topologies adapt in real time to move nodes to away from a user's device to a cloud counterpart and vice versa as circumstances change.

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