US 12,242,555 B2
Combined wide and deep machine learning models for automated database element processing systems, methods and apparatuses
Bing Song, La Canada, CA (US); Jeffrey Michael Balbien, Los Angeles, CA (US); Hao Lu, Los Angeles, CA (US); Phillip Yang, Los Angeles, CA (US); and Patrick Soon-Shiong, Los Angeles, CA (US)
Assigned to NantMedia Holdings, LLC, El Segundo, CA (US)
Filed by NantMedia Holdings, LLC, El Segundo, CA (US)
Filed on Aug. 16, 2024, as Appl. No. 18/807,634.
Application 18/807,634 is a continuation of application No. 17/643,881, filed on Dec. 13, 2021, granted, now 12,105,765.
Claims priority of provisional application 63/125,570, filed on Dec. 15, 2020.
Prior Publication US 2024/0411827 A1, Dec. 12, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/9535 (2019.01); G06F 40/284 (2020.01); G06F 40/40 (2020.01); G06N 3/045 (2023.01)
CPC G06F 16/9535 (2019.01) [G06F 40/284 (2020.01); G06F 40/40 (2020.01); G06N 3/045 (2023.01)] 22 Claims
OG exemplary drawing
 
1. A computer-based recommendation system comprising:
at least one computer readable non-transitory memory storing computer-executable instructions including at least one trained wide machine learning model configured to generate a wide ranked article output and at least one trained deep machine learning model configured to generate a deep ranked article output; and
at least one processor coupled with the at least one computer readable non-transitory memory and that performs the following operations upon execution of the computer-executable instructions:
generating a set of inputs specific to a subscriber and derived from article access records associated with the subscriber;
obtaining a set of current articles published according to specified time period criteria;
creating a feature vector input according to the set of inputs specific to the subscriber and the set of current articles;
generating, via the at least one trained wide machine learning model, a wide ranked article list from a wide model subset of the current articles generated based on the feature vector input and based on a subscriber click history, and having a highest correlation with the article access records;
generating, via the at least one trained deep machine learning model, a deep ranked article list from a deep model subset of the current articles generated based on the feature vector input and based on the subscriber click history, and having a highest correlation with the article access records;
generating a ranked article recommendation output by merging articles from the wide ranked article list and the deep ranked article list; and
displaying the ranked article recommendation output on a user device of the subscriber.