US 12,249,117 B2
Machine learning architecture for peer-based image scoring
Elham Saraee, Medford, MA (US); Jehan Hamedi, Wellesley, MA (US); and Zachary Halloran, Franklin, MA (US)
Assigned to VIZIT LABS, INC., Boston, MA (US)
Filed by Vizit Labs, Inc., Boston, MA (US)
Filed on Oct. 10, 2024, as Appl. No. 18/912,407.
Application 16/537,426 is a division of application No. 15/727,044, filed on Oct. 6, 2017, granted, now 10,380,650.
Application 18/912,407 is a continuation in part of application No. 18/587,524, filed on Feb. 26, 2024, granted, now 12,020,471.
Application 18/587,524 is a continuation of application No. 18/466,465, filed on Sep. 13, 2023, granted, now 11,915,469.
Application 18/466,465 is a continuation of application No. 18/200,102, filed on May 22, 2023, granted, now 11,763,546.
Application 18/200,102 is a continuation in part of application No. 17/833,671, filed on Jun. 6, 2022, granted, now 11,804,028.
Application 17/833,671 is a continuation of application No. 17/548,341, filed on Dec. 10, 2021, granted, now 11,417,085.
Application 17/548,341 is a continuation in part of application No. 16/537,426, filed on Aug. 9, 2019, abandoned.
Claims priority of provisional application 63/348,984, filed on Jun. 3, 2022.
Claims priority of provisional application 62/537,428, filed on Jul. 26, 2017.
Prior Publication US 2025/0037420 A1, Jan. 30, 2025
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/438 (2019.01); G06N 3/045 (2023.01); G06V 10/40 (2022.01); G06V 10/74 (2022.01); G06V 10/82 (2022.01)
CPC G06V 10/761 (2022.01) [G06F 16/438 (2019.01); G06N 3/045 (2023.01); G06V 10/40 (2022.01); G06V 10/82 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A non-transitory, computer-readable medium including instructions which, when executed by one or more processors, cause the one or more processors to:
display, via an interactive graphic user interface (GUI), a first user interface including a task selection element;
in response to a selection of a benchmark creation task using the task selection element:
execute a target audience machine learning model using as input a plurality of images to generate performance scores for the plurality of images indicating a performance of the plurality of images for a target audience corresponding to the target audience machine learning model;
display, via the interactive GUI, a second user interface including the plurality of images ranked according to the performance scores; and
store the performance scores of the plurality of images with an indication of the target audience as a benchmark for the target audience.