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:
Feb. 25, 2025

Filed:

Jun. 22, 2023
Applicant:

Open Text SA Ulc, Halifax, CA;

Inventors:

Norddin Habti, Montreal, CA;

Steve Pettigrew, Montreal, CA;

Martin Brousseau, Mont-Saint-Hilaire, CA;

Lalith Subramanian, Menlo Park, CA (US);

Assignee:

OPEN TEXT SA ULC, Halifax, CA;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/27 (2019.01); G06F 9/54 (2006.01); G06F 16/22 (2019.01); G06F 16/2455 (2019.01); G06F 16/25 (2019.01); G06F 16/84 (2019.01); G06N 5/04 (2023.01);
U.S. Cl.
CPC ...
G06F 9/541 (2013.01); G06F 16/221 (2019.01); G06F 16/2456 (2019.01); G06F 16/252 (2019.01); G06F 16/27 (2019.01); G06F 16/84 (2019.01); G06N 5/04 (2013.01);
Abstract

Disclosed is a flexible and scalable artificial intelligence and analytics platform with advanced content analytics and content ingestion. Disparate contents can be ingested into a content analytics system of the platform through a content ingestion pipeline operated by a sophisticated text mining engine. Prior to persistence, editorial metadata can be extracted and semantic metadata inferred to gain insights across the disparate contents. The editorial metadata and the semantic metadata can be dynamically mapped, as the disparate contents are crawled from disparate sources, to an internal ingestion pipeline document conforming to a uniform mapping schema that specifies master metadata of interest. For persistence, the semantic metadata in the internal ingestion pipeline document can be mapped to metadata tables conforming to a single common data model of a central repository. In this way, ingested metadata can be leveraged across the platform, for instance, for trend analysis, mood detection, model building, etc.


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