US 12,307,208 B2
Prompt-based attribution of generated media contents
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); and Vered Horesh-Yaniv, Tel Aviv (IL)
Assigned to BRIA ARTIFICIAL INTELLIGENCE LTD., Tel Aviv (IL)
Filed by BRIA ARTIFICIAL INTELLIGENCE LTD., Tel Aviv (IL)
Filed on Feb. 16, 2024, as Appl. No. 18/444,120.
Application 18/444,120 is a continuation of application No. 18/387,657, filed on Nov. 7, 2023, granted, now 11,934,792.
Application 18/387,657 is a continuation of application No. PCT/IL2023/051132, filed on Nov. 5, 2023.
Claims priority of provisional application 63/525,754, filed on Jul. 10, 2023.
Claims priority of provisional application 63/444,805, filed on Feb. 10, 2023.
Prior Publication US 2024/0273300 A1, Aug. 15, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. 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)
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)] 20 Claims
OG exemplary drawing
 
1. A non-transitory computer readable medium storing a software program comprising data and computer implementable instructions that when executed by at least one processor cause the at least one processor to perform operations for prompt-based attribution of generated media contents to training examples, the operations comprising:
receiving a first media content generated using a generative model in response to a first textual input in a natural language, the generative model is a result of training a machine learning model using a plurality of training examples, each training example of the plurality of training examples includes a respective textual content in the natural language and a respective media content;
determining one or more properties of the first textual input;
using the one or more properties of the first textual input to attribute the first media content to a first subgroup of at least one but not all of the plurality of training examples;
determining that the training examples of the first subgroup are associated with a first at least one source; and
for each source of the first at least one source, updating a respective data-record associated with the source based on the attribution.