US 12,277,232 B2
Systems and methods for identifying data processing activities based on data discovery results
Jonathan Blake Brannon, Smyrna, GA (US); Kevin Jones, Atlanta, GA (US); Saravanan Pitchaimani, Atlanta, GA (US); Dylan D. Patton-Kuhl, Atlanta, GA (US); Ramana Malladi, Atlanta, GA (US); and Subramanian Viswanathan, San Ramon, CA (US)
Assigned to OneTrust, LLC, Atlanta, GA (US)
Filed by OneTrust, LLC, Atlanta, GA (US)
Filed on Jan. 24, 2024, as Appl. No. 18/421,484.
Application 18/421,484 is a continuation of application No. 18/183,435, filed on Mar. 14, 2023, granted, now 11,921,865.
Application 18/183,435 is a continuation of application No. 17/828,953, filed on May 31, 2022, granted, now 11,615,192, issued on Mar. 28, 2023.
Application 17/828,953 is a continuation of application No. 17/520,272, filed on Nov. 5, 2021, granted, now 11,397,819, issued on Jul. 26, 2022.
Claims priority of provisional application 63/110,557, filed on Nov. 6, 2020.
Prior Publication US 2024/0160747 A1, May 16, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 21/57 (2013.01); G06F 21/60 (2013.01); G06F 21/62 (2013.01); G06N 20/00 (2019.01); G06V 30/24 (2022.01)
CPC G06F 21/577 (2013.01) [G06F 21/602 (2013.01); G06F 21/6245 (2013.01); G06N 20/00 (2019.01); G06V 30/248 (2022.01); G06F 2221/033 (2013.01); G06V 30/2528 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
identifying, by computing hardware, a plurality of data assets associated with a computing system;
scanning, by the computing hardware, the plurality of data assets to detect a subset of data assets in the plurality of data assets associated with target data by generating, using a first machine-learning model, first predictions for the data assets of the plurality of data assets that indicate a likelihood of being associated with the target data;
identifying a data processing activity that is associated with handling the target data for the computing system by generating, by the computing hardware using a second machine-learning model, second predictions for pairs of data assets of the subset of data assets that indicate a likelihood that the target data flows between a pair of data assets; and
causing, by the computing hardware, a performance of an action based on identifying the data processing activity is associated with handling the target data for the computing system.