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    Efficient search for combinations of matching entities given constraints
    2.
    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.

    LARGE LANGUAGE MODELS FOR EXTRACTING CONVERSATIONAL-STYLE EXPLANATIONS FOR ENTITY MATCHES
    3.
    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.

    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.

    DEPLOYMENT OF MACHINE LEARNING MODELS USING LARGE LANGUAGE MODELS AND FEW-SHOT LEARNING
    9.
    发明申请
    DEPLOYMENT OF MACHINE LEARNING MODELS USING LARGE LANGUAGE MODELS AND FEW-SHOT LEARNING 有权

    公开(公告)号：US20250036974A1

    公开(公告)日：2025-01-30

    申请号：US18358245

    申请日：2023-07-25

    Applicant: SAP SE

    Inventor： Rajesh Vellore Arumugam ,  Anantharaman Ravi ,  Isaac New Yi Qing ,  Sundeep Gullapudi ,  Yi Quan Zhou

    IPC: G06N5/04 ,  G06N20/20

    Abstract: Methods, systems, and computer-readable storage media for providing, for a set of ML models, a set of training metrics determined using test data during a training phase, providing, for a production-use ML model, a set of inference metrics based on predictions generated by the production-use ML model, generating, by a prompt generator, a set of few-shot examples using the set of training metrics and the set of inference metrics, inputting, by the prompt generator, the set of few-shot examples to a LLM as prompts, transmitting, to the LLM a query, displaying, to a user, a recommendation that is received from the LLM and responsive to the query, receiving input from a user indicating a user-selected ML model responsive to the recommendation, and deploying a user-selected ML model to an inference runtime for production use.

    ENHANCED MODEL EXPLANATIONS USING DYNAMIC TOKENIZATION FOR ENTITY MATCHING MODELS
    10.
    发明公开
    ENHANCED MODEL EXPLANATIONS USING DYNAMIC TOKENIZATION FOR ENTITY MATCHING MODELS 审中-公开

    公开(公告)号：US20240177053A1

    公开(公告)日：2024-05-30

    申请号：US18070598

    申请日：2022-11-29

    Applicant: SAP SE

    Inventor： Sundeep Gullapudi ,  Rajesh Vellore Arumugam ,  Abhinandan Padhi

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: Methods, systems, and computer-readable storage media for receiving query data representative of query entities and target data representative of target entities, determining, by an attention ML model, a set of character-level embeddings, providing, by a sub-word-level tokenizer, a set of sub-word-level tokens, each sub-word-level token including a string of multiple characters, generating, by the attention ML model, a set of sub-word-level embeddings based on the set of sub-word-level tokens, providing, by the attention ML model, at least one attention matrix including attention scores, each attention score representative of a relative importance of a respective sub-word-level token in a predicted match, the predicted match including a match between a query entity and a target entity, and outputting an explanation based on the at least one attention matrix.

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