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:
Nov. 25, 2025

Filed:

Nov. 20, 2024
Applicant:

Mckinsey & Company, Inc., New York, NY (US);

Inventors:

Neema Kechettira Uthappa, Phoenix, AZ (US);

Jennifer L. Gilbert, New York, NY (US);

Steven F. Reed, New York, NY (US);

A. R. Nandakumar Reddy, New York, NY (US);

Subhajit Basu, New York, NY (US);

Srinivasa Rao Maddili, New York, NY (US);

Kunal Chakrabarty, New York, NY (US);

Mujen Inanc, New York, NY (US);

Assignee:

MCKINSEY & COMPANY, INC., New York, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/33 (2025.01); G06F 16/332 (2019.01); G06F 16/3329 (2025.01); G06F 40/30 (2020.01); H04L 51/02 (2022.01); G06F 16/334 (2025.01); G06F 40/211 (2020.01); G10L 15/18 (2013.01);
U.S. Cl.
CPC ...
G06F 16/3329 (2019.01); G06F 16/3325 (2019.01); G06F 40/30 (2020.01); H04L 51/02 (2013.01); G06F 16/3347 (2019.01); G06F 40/211 (2020.01); G10L 15/1815 (2013.01); G10L 15/1822 (2013.01);
Abstract

Systems and methods for improved data curation and integration are disclosed. An example system includes one or more processors; and a memory storing instructions that cause the system to: receive an initial query, query an external database to obtain a set of keywords, topics, and other queries pertinent to the initial query, and embed articles into a database. The system may also append the keywords, topics, and other queries to the initial query to obtain an external data query and embedded articles to the initial query to obtain an internal data query; and generate (i) an external output by applying a large language model (LLM) to the external data query and (ii) an internal output by applying the LLM to the internal data query. The system may also combine the internal output with the external output to obtain a combined output and cause the combined output to be displayed.


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