公开(公告)号：US11755582B2

公开(公告)日：2023-09-12

申请号：US16862667

申请日：2020-04-30

Applicant: salesforce.com, inc.

Inventor： Arun Kumar Jagota ,  Ajitesh Jain ,  Rahul Mathias Madan ,  Shravani Madhavaram

IPC: G06F7/00 ,  G06F16/2455 ,  G06N20/00

CPC classification number: G06F16/24558 ,  G06F16/24564 ,  G06N20/00

Abstract: Adaptive field-level matching is described. A system identifies first elements in a field of a prospective record for a database, and second elements in the field of a candidate record, in the database, for matching the prospective record. The system identifies features corresponding to any of the first elements that are identical to any of the second elements, any of the first elements that are absent from the second elements, and any of the second elements that are absent from the first elements. A machine-learning model uses the features to determine a field match score for the candidate record's field. Another machine-learning model weighs the field match score and weighs another field match score for another field of the candidate record to determine a record match score for the candidate record. If the record match score satisfies a threshold, the system identifies the candidate record as matching the prospective record.

公开(公告)号：US20210232637A1

公开(公告)日：2021-07-29

申请号：US16775611

申请日：2020-01-29

Applicant: salesforce.com, inc.

Inventor： Arun Kumar Jagota ,  Ajitesh Jain ,  Rahul Mathias Madan ,  Shravani Madhavaram

IPC: G06F16/903 ,  G06N20/00 ,  G06F16/901

Abstract: Determine first count of first records storing first value in first field, second count of second records storing second value in second field, third count of third records storing third value in third field. Determine count threshold using first, second and third counts, dispersion measure based on dispersion of values stored in second field by first records and other dispersion measure based on other dispersion of values stored in third field by first records. Train machine-learning model to determine dispersion measure threshold based on dispersion and other dispersion measures. If first count is greater than count threshold, and dispersion measure is greater than dispersion measure threshold, create match index based on first and second fields. Receive prospective record storing first value in first field, second value in second field. Use match index to identify record storing first value in first field, second value in second field as matching prospective record.

公开(公告)号：US11372928B2

公开(公告)日：2022-06-28

申请号：US16775611

申请日：2020-01-29

Applicant: salesforce.com, inc.

Inventor： Arun Kumar Jagota ,  Ajitesh Jain ,  Rahul Mathias Madan ,  Shravani Madhavaram

IPC: G06F16/90 ,  G06N20/00 ,  G06F16/903 ,  G06F16/901

Abstract: Determine first count of first records storing first value in first field, second count of second records storing second value in second field, third count of third records storing third value in third field. Determine count threshold using first, second and third counts, dispersion measure based on dispersion of values stored in second field by first records and other dispersion measure based on other dispersion of values stored in third field by first records. Train machine-learning model to determine dispersion measure threshold based on dispersion and other dispersion measures. If first count is greater than count threshold, and dispersion measure is greater than dispersion measure threshold, create match index based on first and second fields. Receive prospective record storing first value in first field, second value in second field. Use match index to identify record storing first value in first field, second value in second field as matching prospective record.

公开(公告)号：US20210342353A1

公开(公告)日：2021-11-04

申请号：US16862667

申请日：2020-04-30

Applicant: salesforce.com, inc.

Inventor： Arun Kumar Jagota ,  Ajitesh Jain ,  Rahul Mathias Madan ,  Shravani Madhavaram

IPC: G06F16/2455 ,  G06N20/00

Abstract: Adaptive field-level matching is described. A system identifies first elements in a field of a prospective record for a database, and second elements in the field of a candidate record, in the database, for matching the prospective record. The system identifies features corresponding to any of the first elements that are identical to any of the second elements, any of the first elements that are absent from the second elements, and any of the second elements that are absent from the first elements. A machine-learning model uses the features to determine a field match score for the candidate record's field. Another machine-learning model weighs the field match score and weighs another field match score for another field of the candidate record to determine a record match score for the candidate record. If the record match score satisfies a threshold, the system identifies the candidate record as matching the prospective record.