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    ROBUST ENTITY MATCHING USING MACHINE LEARNING MODELS TRAINED ON HISTORICAL CUSTOMER DATA
    1.
    发明公开
    ROBUST ENTITY MATCHING USING MACHINE LEARNING MODELS TRAINED ON HISTORICAL CUSTOMER DATA 审中-公开

    公开(公告)号：US20240185091A1

    公开(公告)日：2024-06-06

    申请号：US18074574

    申请日：2022-12-05

    Applicant: SAP SE

    Inventor： Stefan Klaus Baur ,  Matthias Frank ,  Hoang-Vu Nguyen

    IPC: G06N5/022 ,  G06Q30/0201

    CPC classification number: G06N5/022 ,  G06Q30/0201

    Abstract: Disclosed herein are system, method, and computer program product embodiments for dropping or replacing data from datasets and training ML models to avoid overfitting in training data. An embodiment operates by generating a first set of data, wherein the first set of data may include a first plurality of entities. The first set of data may be modified by processing the first set of data, which results in a second set of data. The second set of data may include a second plurality of entities. The second set of data may be extracted to be used in a machine learning (ML) process based at least in part on at least one ML model. The second set of data may be trained on at least one ML model. A third set of data may be predicted based on the at least one ML model. The third set of data may include a third plurality of entities. The first, second, and third plurality of entities may be classified by a class.

    Efficient search for combinations of matching entities given constraints
    3.
    发明授权
    Efficient search for combinations of matching entities given constraints 有权

    公开(公告)号：US11687575B1

    公开(公告)日：2023-06-27

    申请号：US17647477

    申请日：2022-01-10

    Applicant: SAP SE

    Inventor： Hoang-Vu Nguyen ,  Rajesh Vellore Arumugam ,  Matthias Frank ,  Stefan Klaus Baur

    IPC: G06F16/33 ,  G06F16/35 ,  G06F16/31

    CPC classification number: G06F16/3347 ,  G06F16/325 ,  G06F16/3346 ,  G06F16/35

    Abstract: Methods, systems, and computer-readable storage media for receiving a set of inference results generated by a ML model, the inference results including a set of query entities and a set of target entities, each query entity having one or more target entities matched thereto by the ML model, processing the set of inference results to generate a set of matched sub-sets of target entities by executing a search over target entities in the set of target entities based on constraints, for each problem in a set of problems, providing the problem as a tuple including an index value representative of a target entity in the set of target entities and a value associated with the query entity, the value including a constraint relative to the query entity, and executing at least one task in response to one or more matched sub-sets in the set of matched sub-sets.

    GLOBAL ENTITY MATCHING MODEL WITH CONTINUOUS PERFORMANCE ENHANCEMENT USING LARGE LANGUAGE MODELS
    4.
    发明申请
    GLOBAL ENTITY MATCHING MODEL WITH CONTINUOUS PERFORMANCE ENHANCEMENT USING LARGE LANGUAGE MODELS 有权

    公开(公告)号：US20250117663A1

    公开(公告)日：2025-04-10

    申请号：US18480635

    申请日：2023-10-04

    Applicant: SAP SE

    Inventor： Rajesh Vellore Arumugam ,  Donglin Ruan ,  Matthias Frank ,  Yi Quan Zhou

    IPC: G06N3/0895

    Abstract: Methods, systems, and computer-readable storage media for training a global matching ML model using a set of enterprise data associated with a set of enterprises, receiving a subset of enterprise data associated with an enterprise that is absent from the set of enterprises, fine tuning the global matching ML model using the subset of enterprise data to provide a fine-tuned matching ML model. deploying the fine-tuned matching ML model for inference, receiving feedback to one or more inference results generated by the fine-tuned matching ML model, receiving synthetic data from a LLM system in response to at least a portion of the feedback, and fine tuning one or more of the global matching ML model and the fine-tuned ML model using the synthetic data.

    EFFICIENT SEARCH FOR COMBINATIONS OF MATCHING ENTITIES GIVEN CONSTRAINTS
    7.
    发明公开
    EFFICIENT SEARCH FOR COMBINATIONS OF MATCHING ENTITIES GIVEN CONSTRAINTS 审中-公开

    公开(公告)号：US20230222147A1

    公开(公告)日：2023-07-13

    申请号：US17647477

    申请日：2022-01-10

    Applicant: SAP SE

    Inventor： Hoang-Vu Nguyen ,  Rajesh Vellore Arumugam ,  Matthias Frank ,  Stefan Klaus Baur

    IPC: G06F16/33 ,  G06F16/31 ,  G06F16/35

    CPC classification number: G06F16/3347 ,  G06F16/3346 ,  G06F16/325 ,  G06F16/35

    Abstract: Methods, systems, and computer-readable storage media for receiving a set of inference results generated by a ML model, the inference results including a set of query entities and a set of target entities, each query entity having one or more target entities matched thereto by the ML model, processing the set of inference results to generate a set of matched sub-sets of target entities by executing a search over target entities in the set of target entities based on constraints, for each problem in a set of problems, providing the problem as a tuple including an index value representative of a target entity in the set of target entities and a value associated with the query entity, the value including a constraint relative to the query entity, and executing at least one task in response to one or more matched sub-sets in the set of matched sub-sets.

    LARGE LANGUAGE MODELS FOR EXTRACTING CONVERSATIONAL-STYLE EXPLANATIONS FOR ENTITY MATCHES
    8.
    发明申请
    LARGE LANGUAGE MODELS FOR EXTRACTING CONVERSATIONAL-STYLE EXPLANATIONS FOR ENTITY MATCHES 有权

    公开(公告)号：US20250077773A1

    公开(公告)日：2025-03-06

    申请号：US18358225

    申请日：2023-07-25

    Applicant: SAP SE

    Inventor： Rajesh Vellore Arumugam ,  Anantharaman Ravi ,  Matthias Frank ,  Sundeep Gullapudi ,  Yi Quan Zhou

    IPC: G06F40/284 ,  G06F16/248 ,  G06F40/40

    Abstract: Methods, systems, and computer-readable storage media for receiving, by an entity matching ML model, a query and target pair including a query entity and a target entity, providing, by the entity matching ML model, a query-target prediction by processing the query entity and the target entity, the query-target prediction indicating a match type between the query entity and the target entity, generating a prompt by populating a prompt template with at least a portion of the query-target prediction, inputting the prompt into a large language model (LLM), and receiving, from the LLM, an explanation that is responsive to the prompt and that describes one or more reasons for the query-target prediction output by the entity matching ML model.

    NUMERIC EMBEDDINGS FOR ENTITY-MATCHING
    9.
    发明申请
    NUMERIC EMBEDDINGS FOR ENTITY-MATCHING 有权

    公开(公告)号：US20220391414A1

    公开(公告)日：2022-12-08

    申请号：US17375720

    申请日：2021-07-14

    Applicant: SAP SE

    Inventor： Stefan Klaus Baur ,  Matthias Frank ,  Hoang-Vu Nguyen

    IPC: G06F16/28 ,  G06F16/21 ,  G06N3/04

    Abstract: Pairwise entity matching systems and methods are disclosed herein. A deep learning model may be used to match entities from separate data tables. Entities may be preprocessed to fuse textual and numeric data early in the neural network architecture. Numeric data may be represented as a vector of a geometrically progressing function. By fusing textual and numeric data, including dates, early in the neural network architecture the neural network may better learn the relationships between the numeric and textual data. Once preprocessed, the paired entities may be scored and matched using a neural network.

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