The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 08, 2025

Filed:

Dec. 07, 2022
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Saurabh Sanjay Deshpande, Sammamish, WA (US);

Mina Mikhail, Bothell, WA (US);

Matthew Francis Hurst, Seattle, WA (US);

Riham Hassan Abdel-Moneim Mansour, Redmond, WA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2022.01); G06F 16/22 (2019.01); G06F 16/2453 (2019.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06F 16/2228 (2019.01); G06F 16/22 (2019.01); G06F 16/2272 (2019.01); G06F 16/24549 (2019.01); G06F 18/2148 (2023.01); G06N 20/00 (2019.01);
Abstract

The present disclosure relates to processing operations configured to uniquely utilize indexing of content to improve content retrieval processing, particularly when working with large data sets. The techniques described herein enables efficient content retrieval when working with large data sets such as those that may be associated with a plurality of tenants of a data storage application/service. Among other technical advantages, the present disclosure is applicable to train a classifier using relevant samples based on text search in tenant-specific scenarios, where accurate searching can be executed for content associated with one or more tenant accounts of an application/service concurrently in milliseconds even in instances where there may be millions of documents to be searched. As an example, exemplary data shards may be generated and managed for efficient and scalable content retrieval processing including training of a classifier (e.g., artificial intelligence classifier) and real-time (or near real-time) query processing.


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