US 12,236,320 B2
Passive heightened need prediction
Peter Joseph Ross, Dublin (IE); and Damian Kelly, Kildare (IE)
Assigned to Optum Services (Ireland) Limited, Dublin (IE)
Filed by Optum Services (Ireland) Limited, Dublin (IE)
Filed on May 5, 2020, as Appl. No. 16/866,844.
Prior Publication US 2021/0350271 A1, Nov. 11, 2021
Int. Cl. G06N 20/00 (2019.01); G06F 18/214 (2023.01); G06N 5/04 (2023.01)
CPC G06N 20/00 (2019.01) [G06F 18/214 (2023.01); G06N 5/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving, by one or more processors, incoming monitoring data associated with a user profile;
generating, by the one or more processors, anomaly-reduced incoming monitoring data by removing values within the incoming monitoring data that are estimated to be mis-recorded based at least in part on a distribution of the incoming monitoring data;
generating, by the one or more processors, an incoming monitoring window based at least in part on the anomaly-reduced incoming monitoring data, wherein the incoming monitoring window comprises a subset of the incoming monitoring data;
accessing, by the one or more processors, a heightened need prediction machine learning model that is trained to determine heightened need prediction for an input incoming monitoring window based at least in part on a heightened need ground-truth that (i) comprises one or more heightened need conditions that are determined based at least in part on training monitoring data that is associated with the user profile and (ii) represents whether an end user associated with the user profile engaged in one or more need-response actions that are (a) associated with one or more target addictive activities or substances and (b) responsive to the one or more heightened need conditions;
generating, by the one or more processors using the heightened need prediction machine learning model, a heightened need prediction for the incoming monitoring window, wherein the heightened need prediction indicates that the incoming monitoring window is associated with a need that is beyond a threshold level of need that is associated with the one or more target addictive activities or substances; and
based at least in part on the heightened need prediction and using an optimal action determination machine learning model that is configured to generate one or more need reduction likelihood values or one or more need reduction speed values for one or more candidate need-reducing actions, initiating, by the one or more processors, performance of a responsive action from the one or more candidate need-reducing actions based at least in part on the one or more need reduction likelihood values or the one or more need reduction speed values, wherein the responsive action comprises communicating with one or more client devices associated with the user profile.