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:
May. 20, 2025

Filed:

Feb. 16, 2024
Applicant:

Bria Artificial Intelligence Ltd., Tel Aviv, IL;

Inventors:

Yair Adato, Kfar Ben Nun, IL;

Michael Feinstein, Tel Aviv, IL;

Efrat Taig, Beer Sheva, IL;

Dvir Yerushalmi, Kfar Saba, IL;

Ori Liberman, Netanya, IL;

Vered Horesh-Yaniv, Tel Aviv, IL;

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/00 (2019.01); G06F 40/279 (2020.01); G06F 40/30 (2020.01); G06F 40/40 (2020.01); G06T 7/194 (2017.01); G06T 7/70 (2017.01); G06T 11/00 (2006.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G10L 15/06 (2013.01); G10L 15/18 (2013.01); H04N 5/272 (2006.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/279 (2020.01); G06F 40/40 (2020.01); G06T 7/194 (2017.01); G06T 7/70 (2017.01); G06T 11/001 (2013.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G10L 15/063 (2013.01); G10L 15/18 (2013.01); H04N 5/272 (2013.01); G06T 2207/30196 (2013.01); G10L 2015/0631 (2013.01);
Abstract

Systems, methods and non-transitory computer readable media for identifying prompts used for training of inference models are provided. In some examples, a specific textual prompt in a natural language may be received. Further, data based on at least one parameter of an inference model may be accessed. The inference model may be a result of training a machine learning model using a plurality of training examples. Each training example of the plurality of training examples may include a respective textual content and a respective media content. The data and the specific textual prompt may be analyzed to determine a likelihood that the specific textual prompt is included in at least one training example of the plurality of training examples. A digital signal indicative of the likelihood that the specific textual prompt is included in at least one training example of the plurality of training examples may be generated.


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