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.

公开(公告)号：US11615120B2

公开(公告)日：2023-03-28

申请号：US17375720

申请日：2021-07-14

Applicant: SAP SE

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

IPC: G06F16/00 ,  G06F16/28 ,  G06N3/04 ,  G06F16/21

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.

公开(公告)号：US20220092405A1

公开(公告)日：2022-03-24

申请号：US17025845

申请日：2020-09-18

Applicant: SAP SE

Inventor： Matthias Frank ,  Hoang-Vu Nguyen ,  Stefan Klaus Baur ,  Alexey Streltsov ,  Jasmin Mankad ,  Cordula Guder ,  Konrad Schenk ,  Philipp Lukas Jamscikov ,  Rohit Kumar Gupta

IPC: G06N3/08 ,  G06N3/04 ,  G06F16/81

Abstract: In an example embodiment, a deep neural network may be utilized to determine matches between candidate pairs of entities, as well as confidence scores that reflect how certain the deep neural network is about the corresponding match. The deep neural network is also able to find these matches without requiring domain knowledge that would be required if features for a machine-learned model were handcrafted, which is a drawback of prior art machine-learned models used to match entities in multiple tables. Thus, the deep neural network improves on the functioning of prior art machine learned models designed to perform the same tasks. Specifically, the deep neural network learns the relationships of tabular fields and the patterns that define a match from historical data alone, making this approach generic and applicable independent of the context.

公开(公告)号：US20230325708A1

公开(公告)日：2023-10-12

申请号：US17718850

申请日：2022-04-12

Applicant: SAP SE

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

IPC: G06N20/00 ,  G06V10/74 ,  G06V10/766

CPC classification number: G06N20/00 ,  G06V10/761 ,  G06V10/766

Abstract: Computer-readable media, methods, and systems are disclosed for feature attribution in a machine learning model. Samples may be generated for a machine learning model based on a normalized probability distribution. The samples may be used to determine a weight for features and feature pairs for the machine learning model. The weights of the features and feature pairs may be used to determine which features are significant for predictions within the machine learning model.

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.

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.

公开(公告)号：US20240045890A1

公开(公告)日：2024-02-08

申请号：US17817388

申请日：2022-08-04

Applicant: SAP SE

Inventor： Hoang-Vu Nguyen ,  Li Rong Wang ,  Matthias Frank ,  Rajesh Vellore Arumugam ,  Stefan Klaus Baur ,  Sundeep Gullapudi

IPC: G06F16/28 ,  G06N20/00 ,  G06F16/2457 ,  G06K9/62

CPC classification number: G06F16/288 ,  G06N20/00 ,  G06F16/24578 ,  G06K9/6215

Abstract: Methods, systems, and computer-readable storage media for a machine learning (ML) system for matching a query entity to one or more target entities, the ML system that reducing a number of query-target entity pairs from consideration as potential matches during inference.

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.