US 12,293,331 B2
Using context to update product models
Yair Adato, Kfar Shmuel (IL); Aviv Eisenschtat, Ramat Hasharon (IL); Dolev Pomeranz, Hod Hasharon (IL); Ziv Mhabary, Tel Aviv (IL); Daniel Shimon Cohen, Hoboken, NJ (US); and Osnat Yanushevsky, Ramat Hasharon (IL)
Assigned to Trax Technology Solutions Pte Ltd., Singapore (SG)
Filed by Trax Technology Solutions Pte Ltd., Singapore (SG)
Filed on Apr. 5, 2024, as Appl. No. 18/627,562.
Application 18/627,562 is a continuation of application No. 18/212,350, filed on Jun. 21, 2023, granted, now 11,978,016.
Application 18/212,350 is a continuation of application No. 17/739,373, filed on May 9, 2022, granted, now 11,727,353, issued on Aug. 15, 2023.
Application 17/739,373 is a continuation of application No. 17/082,938, filed on Oct. 28, 2020, granted, now 11,354,916, issued on Jun. 7, 2022.
Application 17/082,938 is a continuation of application No. 16/578,595, filed on Sep. 23, 2019, granted, now 10,846,512, issued on Nov. 24, 2020.
Application 16/578,595 is a continuation of application No. 16/353,499, filed on Mar. 14, 2019, granted, now 10,452,924, issued on Oct. 22, 2019.
Application 16/353,499 is a continuation of application No. PCT/US2019/013054, filed on Jan. 10, 2019.
Claims priority of provisional application 62/695,469, filed on Jul. 9, 2018.
Claims priority of provisional application 62/681,718, filed on Jun. 7, 2018.
Claims priority of provisional application 62/615,512, filed on Jan. 10, 2018.
Prior Publication US 2024/0257050 A1, Aug. 1, 2024
Int. Cl. G06K 9/00 (2022.01); G06F 16/23 (2019.01); G06F 16/28 (2019.01); G06F 16/55 (2019.01); G06F 16/583 (2019.01); G06F 16/903 (2019.01); G06F 17/18 (2006.01); G06F 18/2115 (2023.01); G06Q 10/0631 (2023.01); G06Q 10/0633 (2023.01); G06Q 10/08 (2023.01); G06Q 10/087 (2023.01); G06Q 10/0875 (2023.01); G06Q 20/20 (2012.01); G06Q 30/0242 (2023.01); G06Q 30/0601 (2023.01); G06T 7/00 (2017.01); G06T 7/13 (2017.01); G06T 7/20 (2017.01); G06T 7/521 (2017.01); G06T 7/55 (2017.01); G06T 7/70 (2017.01); G06T 7/73 (2017.01); G06V 20/00 (2022.01); G06V 20/10 (2022.01); G06V 20/20 (2022.01); G06V 20/52 (2022.01); G06V 20/62 (2022.01); G06V 20/64 (2022.01); G06V 40/10 (2022.01); G08B 21/18 (2006.01); H04N 23/51 (2023.01); H04N 23/54 (2023.01); H04N 23/611 (2023.01); H04N 23/66 (2023.01); H04N 23/80 (2023.01); H04N 23/90 (2023.01); G06Q 30/0201 (2023.01); G06V 20/68 (2022.01); G06V 30/10 (2022.01)
CPC G06Q 10/087 (2013.01) [G06F 16/23 (2019.01); G06F 16/235 (2019.01); G06F 16/288 (2019.01); G06F 16/55 (2019.01); G06F 16/583 (2019.01); G06F 16/5846 (2019.01); G06F 16/90335 (2019.01); G06F 17/18 (2013.01); G06F 18/2115 (2023.01); G06Q 10/06311 (2013.01); G06Q 10/063112 (2013.01); G06Q 10/06316 (2013.01); G06Q 10/0633 (2013.01); G06Q 10/08 (2013.01); G06Q 10/0875 (2013.01); G06Q 20/203 (2013.01); G06Q 30/0246 (2013.01); G06Q 30/0629 (2013.01); G06Q 30/0639 (2013.01); G06Q 30/0643 (2013.01); G06T 7/0002 (2013.01); G06T 7/13 (2017.01); G06T 7/20 (2013.01); G06T 7/521 (2017.01); G06T 7/55 (2017.01); G06T 7/70 (2017.01); G06T 7/75 (2017.01); G06V 20/00 (2022.01); G06V 20/10 (2022.01); G06V 20/20 (2022.01); G06V 20/52 (2022.01); G06V 20/62 (2022.01); G06V 20/64 (2022.01); G06V 40/10 (2022.01); G08B 21/18 (2013.01); G08B 21/182 (2013.01); H04N 23/51 (2023.01); H04N 23/54 (2023.01); H04N 23/611 (2023.01); H04N 23/66 (2023.01); H04N 23/80 (2023.01); H04N 23/90 (2023.01); G06Q 30/0201 (2013.01); G06T 2207/30196 (2013.01); G06T 2207/30232 (2013.01); G06T 2207/30242 (2013.01); G06V 20/68 (2022.01); G06V 30/10 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A system for processing images captured in a retail store, the system comprising:
at least one processor configured to:
access a database storing a group of product models, each relating to at least one product in the retail store;
receive at least one image depicting at least part of at least one store shelf having a plurality of products of a same type displayed thereon;
analyze the received at least one image and determine a first candidate type of the plurality of products based on the group of product models and the image analysis;
determine a first confidence level associated with the determined first candidate type of the plurality of products;
when the first confidence level associated with the first candidate type is below a confidence threshold, determine a second candidate type of the plurality of products using contextual information;
determine a second confidence level associated with the determined second candidate type of the plurality of products; and
when the second confidence level associated with the second candidate type is above the confidence threshold, initiate an action to update the group of product models stored in the database.