US 11,669,755 B2
Detecting cognitive biases in interactions with analytics data
Atanu R Sinha, Bangalore (IN); Tanay Asija, Noida (IN); Sunny Dhamnani, Bangalore (IN); Raja Kumar Dubey, Durangi Khurd (IN); Navita Goyal, Bengaluru (IN); Kaarthik Raja Meenakshi Viswanathan, Nambiyur (IN); and Georgios Theocharous, San Jose, CA (US)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Jul. 6, 2020, as Appl. No. 16/921,202.
Prior Publication US 2022/0004898 A1, Jan. 6, 2022
Int. Cl. G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06F 9/451 (2018.01)
CPC G06N 5/04 (2013.01) [G06F 9/451 (2018.02); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A non-transitory computer-readable medium storing instructions thereon that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
identifying, from a digital behavior log corresponding to a user, a set of digital action sequences corresponding to a set of sessions for a task executed by the user, each digital action sequence comprising a task-identifying digital action, a first subset of digital actions that chronologically precedes the task-identifying digital action, and a second subset of digital actions that chronologically follows the task-identifying digital action;
generating, utilizing a machine learning model, session weights indicating a predicted influence of the set of sessions for the task on a future session for the task; and
providing, for display on a graphical user interface, a visual indication of an action-selection bias of the user for the task based on the session weights.