US 12,008,065 B2
Utilizing machine-learning models to generate identifier embeddings and determine digital connections between digital content items
Jongmin Baek, Foster City, CA (US); Jiarui Ding, Foster City, CA (US); Ermo Wei, San Bruno, CA (US); and Scott McCrae, Mill Valley, CA (US)
Assigned to Dropbox, Inc., San Francisco, CA (US)
Filed by Dropbox, Inc., San Francisco, CA (US)
Filed on Jan. 12, 2023, as Appl. No. 18/153,960.
Application 18/153,960 is a continuation of application No. 17/131,488, filed on Dec. 22, 2020, granted, now 11,568,018.
Prior Publication US 2023/0169139 A1, Jun. 1, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/958 (2019.01); G06F 16/14 (2019.01); G06F 40/284 (2020.01); G06F 40/30 (2020.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/084 (2023.01); G06N 20/00 (2019.01); G06N 5/02 (2023.01)
CPC G06F 16/958 (2019.01) [G06F 16/14 (2019.01); G06F 40/284 (2020.01); G06F 40/30 (2020.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/084 (2013.01); G06N 20/00 (2019.01); G06N 5/02 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
at least one processor; and
at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
identify a plurality of identifiers corresponding to a plurality of digital content items;
generate a plurality of identifier embeddings corresponding to the plurality of identifiers by utilizing one or more embedding machine-learning models;
generate digital similarity predictions between the plurality of digital content items by processing the plurality of identifier embeddings utilizing a content management model; and
determine a digital connection between a subset of digital content items of the plurality of digital content items based on the digital similarity predictions.