利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

2597 条记录 (耗时 0.03 秒)

    AUTOMATED EXTENSIBILITY FRAMEWORK
    22.
    发明公开
    AUTOMATED EXTENSIBILITY FRAMEWORK 审中-公开

    公开(公告)号：US20240202012A1

    公开(公告)日：2024-06-20

    申请号：US18065877

    申请日：2022-12-14

    申请人： SAP SE

    发明人： Garima Srivastava ,  Sushama Shadakshary

    IPC分类号： G06F9/445 ,  G06F8/36 ,  G06F8/41

    CPC分类号： G06F9/44526 ,  G06F8/36 ,  G06F8/427

    摘要： An automated extensibility framework is provided to automatically convert or migrate application extensions from a source system to be used on target applications in a target format. Extension artefacts of an extension of a source application on a source system are obtained. Each of the extension artefacts are parsed into a target format of a target platform based on a type of the corresponding extension artefact. Target applications of the target platform are identified based on an identifier of the source application. Extension simulations are compiled for each of the one or more target applications. Then, a user interface is provided which enables a user to select simulations from among extension simulations. Then the selected simulations are published to the target platform such that the corresponding extensions are implemented in corresponding target applications.

    NEURAL NETWORK MODEL DEFINITION CODE GENERATION AND OPTIMIZATION
    23.
    发明公开
    NEURAL NETWORK MODEL DEFINITION CODE GENERATION AND OPTIMIZATION 审中-公开

    公开(公告)号：US20240201957A1

    公开(公告)日：2024-06-20

    申请号：US18513232

    申请日：2023-11-17

    申请人： Micron Technology, Inc.

    发明人： Abhishek Chaurasia ,  Andre Xian Ming Chang

    IPC分类号： G06F8/34 ,  G06F8/36

    CPC分类号： G06F8/34 ,  G06F8/36

    摘要： A system providing neural network model definition code generation and optimization is disclosed. The system receives inputs to facilitate the generation of an artificial intelligence model, such as freehand drawings of a model, modules available in repositories, various forms of content, and other inputs. The system utilizes a neural network to analyze the inputs and generates blocks and connections to generate a graph for the artificial intelligence model. Properties of the model are selected, and the system locates modules, generates code for modules, or both, based on the blocks and connections from the graph and the properties. The system generates the model definition for the artificial intelligence model using the located modules and the generated code. Once the model definition is completed, the artificial intelligence model may be utilized to perform a task for which the artificial intelligence model has been created to perform.

    SYSTEM AND METHOD FOR OPTIMIZED GENERATION OF MICROSERVICES
    24.
    发明公开
    SYSTEM AND METHOD FOR OPTIMIZED GENERATION OF MICROSERVICES 审中-公开

    公开(公告)号：US20240192932A1

    公开(公告)日：2024-06-13

    申请号：US18112623

    申请日：2023-02-22

    申请人： Cognizant Technology Solutions India Pvt. Ltd.

    发明人： Parasu Pillai Iyappan Velammal ,  Manoj Chakri Shekaran ,  Vinitha Yogish PAI ,  Sivakumar Ellappan ,  Jeyashree Pandian Duraipandian ,  Pramod Subrayappa

    IPC分类号： G06F8/36 ,  G06F8/76

    CPC分类号： G06F8/36 ,  G06F8/76

    摘要： The present invention provides for a system and a method for generation of domain driven microservices-based architecture from application source codes. A control flow structure is created based on extracted technical rules from an application source code and metadata is extracted by parsing the control flow structure in real-time. A first data associated with a plurality of source entities and corresponding attributes is identified and a second data associated with a plurality of target entities based on an operation type is identified. A third data associated with a plurality of technical data in the application source code is identified based on an action performed by a user via an application corresponding to application source code and data trace between the first data, the second data and the third data is generated. Correlations are established to generate microservices code-based architecture for deployment on a target platform.

    Software Call Translations for On-Device Machine Learning Execution
    26.
    发明公开
    Software Call Translations for On-Device Machine Learning Execution 审中-公开

    公开(公告)号：US20240176603A1

    公开(公告)日：2024-05-30

    申请号：US18059613

    申请日：2022-11-29

    申请人： Meta Platforms, Inc.

    发明人： Roman Georg RAEDLE ,  Christopher Robert Harper KLAIBER ,  Yinglao LIU ,  Shiyong FANG ,  Pranav DESHPANDE ,  Hung Shek NGAN ,  Anoop Kumar SINHA

    IPC分类号： G06F8/51 ,  G06F8/36

    CPC分类号： G06F8/51 ,  G06F8/36

    摘要： Aspects of the present disclosure are directed to translating application calls for on-device machine learning execution. A translation layer supports on-device machine learning execution by translating JavaScript software application call data to achieve interoperability with on-device machine learning models. For example, JavaScript software applications interact with data, such as images, audio, video, and/or text, in a format or data type that is compatible with the application. On the other hand, machine learning models interact with data in a form conducive to mathematical operations, such as a data structure representation (e.g., tensor representation). Implementations translate data types and/or data files to provide compatible data to each of a native JavaScript software application and on-device machine learning models. The translation layer can translate JavaScript application calls to provide compatible data to the machine learning model(s), and output from the machine learning model(s) to provide compatible data to the JavaScript application.

    Layered functional and graphical user interface system
    29.
    发明授权
    Layered functional and graphical user interface system 有权

    公开(公告)号：US11972275B1

    公开(公告)日：2024-04-30

    申请号：US18103411

    申请日：2023-01-30

    申请人： Salesforce, Inc.

    发明人： Alan Weibel ,  Brandon Ferrua

    IPC分类号： G06F9/451 ,  G06F8/36

    CPC分类号： G06F9/451 ,  G06F8/36

    摘要： A computer-implemented method for providing user interface functionalities is disclosed. The method includes providing a base design system layer including web browser components providing platform-agnostic user interface functionalities, an application programming interface (API) that extends the base design system layer into a second design system layer providing platform-specific user interface functionalities, inheriting and extending extensible APIs from the second design system layer, building a third design system layer using the extensible APIs, providing product-specific user interface functionalities using the third design system layer, inheriting and extending extensible APIs from the second design system layer, building a fourth design system layer using the extensible APIs, and providing presentation-specific user interface functionalities using the fourth design system layer. The method also includes integrating the base design system layer, the second design system layer, the third design system layer and the fourth design system layer into a user interface framework.

    ARCHITECTURE FOR AUTOMATICALLY GENERATING COMPUTER-EXECUTABLE CODE FOR QUERYING NETWORKED RELATIONAL DATABASE MANAGEMENT SYSTEMS
    30.
    发明公开
    ARCHITECTURE FOR AUTOMATICALLY GENERATING COMPUTER-EXECUTABLE CODE FOR QUERYING NETWORKED RELATIONAL DATABASE MANAGEMENT SYSTEMS 审中-公开

    公开(公告)号：US20240126517A1

    公开(公告)日：2024-04-18

    申请号：US17966398

    申请日：2022-10-14

    申请人： Evernorth Strategic Development, Inc.

    发明人： Joshua P. Barrett

    IPC分类号： G06F8/36 ,  G06F8/38 ,  G06F16/903

    CPC分类号： G06F8/36 ,  G06F8/38 ,  G06F16/90335

    摘要： A system for automatically generating computer-executable code includes a user device including a communications interface, a code generation module, and a data store, and a storage device operatively coupled to the code generation module via a network and the communications interface. The storage device includes a relational database management system. The code generation module is configured to parse a selected feature from the feature library to determine a first helper function of the one or more helper functions and a selected template function of the one or more template functions, receive a first argument for the first helper function, generate a first output value by associating the first helper function with the first argument, generate precursor executable code by adding the first output value to the selected template function as a first argument of the selected template function, and execute the precursor executable code to generate bespoke code.