公开(公告)号：US20230334070A1

公开(公告)日：2023-10-19

申请号：US17723586

申请日：2022-04-19

Applicant: SAP SE

Inventor： Sundeep Gullapudi ,  Rajesh Vellore Arumugam ,  Matthias Frank ,  Wei Xia

IPC: G06F16/31 ,  G06F16/332 ,  G06F16/33 ,  G06F40/284

CPC classification number: G06F16/322 ,  G06F16/332 ,  G06F16/3334 ,  G06F40/284

Abstract: Methods, systems, and computer-readable storage media for a ML system that reduces a number of target items from consideration as potential matches to a query item using token embeddings and a search tree.

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：US10783377B2

公开(公告)日：2020-09-22

申请号：US16218067

申请日：2018-12-12

Applicant: SAP SE

Inventor： Anoop Raveendra Katti ,  Shachar Klaiman ,  Marius Lehne ,  Sebastian Brarda ,  Johannes Hoehne ,  Matthias Frank ,  Lennart Van der Goten

IPC: G06K9/00 ,  G06F16/783 ,  G06N3/08

Abstract: Aspects of the present disclosure therefore involve systems and methods for identifying a set of visually similar scenes to a target scene selected or otherwise identified by a match analyst. A scene retrieval platform performs operations for: receiving an input that comprises an identification of a scene; retrieving a set of coordinates based on the scene identified by the input, where the set of coordinates identify positions of the entities depicted within the frames; generating a set of vector values based on the coordinates of the entities depicted within each of the frames; concatenating the set of vector values to generate a concatenated vector value that represents the scene; generating a visual representation of the concatenated vector value; and identifying one or more similar scenes to the scene identified by the input based on the visual representation of the concatenated vector value.