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. 29, 2025

Filed:

Aug. 31, 2022
Applicant:

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

Inventors:

Daniel Lee Mace, Bellevue, WA (US);

William Blum, Bellevue, WA (US);

Jeremias Eichelbaum, Vancouver, CA;

Amir Rubin, Vancouver, CA;

Edir V. Garcia Lazo, Seattle, WA (US);

Nihal Irmak Pakis, Vancouver, CA;

Yogesh K. Roy, Redmond, WA (US);

Jugal Parikh, Sammamish, WA (US);

Peter A. Bryan, Seattle, WA (US);

Benjamin Elliott Nick, Bellevue, WA (US);

Ram Shankar Siva Kumar, Bothell, WA (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 40/47 (2020.01); G06F 16/242 (2019.01); G06F 21/55 (2013.01);
U.S. Cl.
CPC ...
G06F 21/554 (2013.01); G06F 16/2423 (2019.01); G06F 40/47 (2020.01);
Abstract

A computer-implemented method of generating a security language query from a user input query includes receiving, at a computer system, an input security hunting user query indicating a user intention; selecting, using a trained machine learning model and based on the input security hunting query, an example user security hunting query and corresponding example security language query; generating, using the trained machine learning model, query metadata from the input security hunting query; generating a prompt, the prompt comprising: the input security hunting user query; the selected example user security hunting query and the corresponding example security language query; and the generated query metadata; inputting the prompt to a large language model; receiving a security language query from the large language model corresponding to the input security hunting query reflective of the user intention.


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