DYNAMIC PROTOTYPE LEARNING FRAMEWORK FOR NON-HOMOPHILOUS GRAPHS

    公开(公告)号:US20240311658A1

    公开(公告)日:2024-09-19

    申请号:US18184407

    申请日:2023-03-15

    Applicant: PAYPAL, INC.

    Inventor: Yanfei Dong

    Abstract: Methods and systems are presented for providing a framework for analyzing graphs that exhibit non-homophilous behavior. Under the framework, a structural analysis and a feature-based analysis will be performed on a sequence of graphs. When performing the feature-based analysis, various features are extracted from each node in the sequence of graphs, and clusters of nodes are identified from each graph based on the features. A set of evolving prototypes is generated to represent evolving characteristics of the clusters of nodes, and a set of persistent prototypes is generated to represent persistent characteristics of the clusters of nodes. Information derived from the structural analysis of the graphs, the set of evolving prototypes, and the set of persistent prototypes are embedded within the nodes of the graphs. The embedded information is then used to classify the nodes.

    Transforming natural language request into enterprise analytics query using fine-tuned machine learning model

    公开(公告)号:US12032564B1

    公开(公告)日:2024-07-09

    申请号:US18149263

    申请日:2023-01-03

    Applicant: SAP SE

    CPC classification number: G06F16/243 G06F16/2455 G06N5/027

    Abstract: A system for enterprise analytics may include a Machine Learning (“ML”) model data store containing at least one generic ML model and a fine-tuning data store containing prior natural language user requests and associated enterprise database queries generated by analysts. The system may also include an enterprise data store containing enterprise business data. A transformation framework may retrieve the generic ML model and fine-tune the model using the prior user requests and associated enterprise database queries to create a fine-tuned ML model. The framework may then receive a new natural language request from a user and use the fine-tuned ML model and new natural language request to automatically create a new enterprise analytics query. The new enterprise analytics query may then be executed to fetch enterprise analytics data from the enterprise data store. In some embodiments, an analytics chart may be automatically created and provided to the user.

    TRANSFORMING NATURAL LANGUAGE REQUEST INTO ENTERPRISE ANALYTICS QUERY USING FINE-TUNED MACHINE LEARNING MODEL

    公开(公告)号:US20240220489A1

    公开(公告)日:2024-07-04

    申请号:US18149263

    申请日:2023-01-03

    Applicant: SAP SE

    CPC classification number: G06F16/243 G06F16/2455 G06N5/027

    Abstract: A system for enterprise analytics may include a Machine Learning (“ML”) model data store containing at least one generic ML model and a fine-tuning data store containing prior natural language user requests and associated enterprise database queries generated by analysts. The system may also include an enterprise data store containing enterprise business data. A transformation framework may retrieve the generic ML model and fine-tune the model using the prior user requests and associated enterprise database queries to create a fine-tuned ML model. The framework may then receive a new natural language request from a user and use the fine-tuned ML model and new natural language request to automatically create a new enterprise analytics query. The new enterprise analytics query may then be executed to fetch enterprise analytics data from the enterprise data store. In some embodiments, an analytics chart may be automatically created and provided to the user.

    Data Messaging Quality Check Tool
    4.
    发明公开

    公开(公告)号:US20240121617A1

    公开(公告)日:2024-04-11

    申请号:US17963948

    申请日:2022-10-11

    CPC classification number: H04W12/80 G06N5/025 G06N5/027

    Abstract: A data message quality check system that performs deep packet inspection on data messages after they have been constructed and sent towards a final receiver. The system comprises a plurality of mediation devices configured to generate data messages in accordance with a predefined format and to embed contextual information in a header of the data messages; and a data message quality check system configured to receive the data messages from the mediation devices, configured to perform deep packet inspection (DPI) on each of the data messages, configured to determine whether the data messages satisfy quality criteria, configured to transmit data messages that satisfy quality criteria to the final receiver, and quarantining data messages that do not satisfy quality criteria.

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