US 12,142,029 B2
Attributing generated visual content to training examples
Yair Adato, Kfar Ben Nun (IL); Ran Achituv, Kefar Sava (IL); Eyal Gutflaish, Beer Sheva (IL); and Dvir Yerushalmi, Kfar Saba (IL)
Assigned to BRIA ARTIFICIAL INTELLIGENCE LTD, Tel Aviv (IL)
Filed by BRIA ARTIFICIAL INTELLIGENCE LTD, Tel Aviv (IL)
Filed on Nov. 14, 2022, as Appl. No. 17/986,347.
Application 17/986,347 is a continuation of application No. PCT/IL2022/051189, filed on Nov. 9, 2022.
Claims priority of provisional application 63/279,111, filed on Nov. 14, 2021.
Prior Publication US 2024/0153039 A1, May 9, 2024
Int. Cl. G06V 10/772 (2022.01); G06F 18/214 (2023.01); G06F 18/24 (2023.01); G06T 3/40 (2006.01); G06T 5/50 (2006.01); G06T 7/00 (2017.01); G06T 11/00 (2006.01); G06T 19/00 (2011.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01)
CPC G06V 10/764 (2022.01) [G06F 18/214 (2023.01); G06F 18/24 (2023.01); G06T 3/40 (2013.01); G06T 5/50 (2013.01); G06T 7/0002 (2013.01); G06T 11/001 (2013.01); G06T 19/006 (2013.01); G06V 10/772 (2022.01); G06V 10/774 (2022.01); G06T 2207/20081 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A non-transitory computer readable medium containing instructions for causing at least one processor to perform operations for attributing generated visual content to training examples, the operations comprising:
receiving a first visual content generated using a generative model, the generative model is a result of training a machine learning algorithm using a plurality of training examples, each training example of the plurality of training examples is associated with a visual content;
determining one or more properties of the first visual content;
for each training example of the plurality of training examples, analyzing the visual content associated with the training example to determine one or more properties of the visual content associated with the training example;
using the one or more properties of the first visual content and the properties of the visual contents associated with the plurality of training examples to attribute the first visual content to a first subgroup of at least one but not all of the plurality of training examples;
determining that the at least one visual content associated with the training examples of the first subgroup is associated with a first at least one source; and
receiving a second visual content generated using the generative model;
determining one or more properties of the second visual content;
using the one or more properties of the second visual content and the properties of the visual contents associated with the plurality of training examples to attribute the second visual content to a second subgroup of at least one but not all of the plurality of training examples, the second subgroup includes at least one training example not included in the first subgroup;
determining that the at least one visual content associated with the training examples of the second subgroup is associated with a second at least one source, the second at least one source includes one or more sources not included in the first at least one source;
based on the second at least one source, forgoing usage of the second visual content; and
initiating usage of the first visual content.