US 12,423,469 B2
Systems and methods for dataset verification in a zero-trust computing environment
Mary Elizabeth Chalk, Austin, TX (US); and Robert Derward Rogers, Oakland, CA (US)
Assigned to BeeKeeperAI, Inc., Austin, TX (US)
Filed by BeeKeeperAI, Inc., Austin, TX (US)
Filed on Feb. 14, 2023, as Appl. No. 18/169,111.
Application 18/169,111 is a continuation of application No. 18/168,560, filed on Feb. 13, 2023.
Claims priority of provisional application 63/313,774, filed on Feb. 25, 2022.
Prior Publication US 2023/0315902 A1, Oct. 5, 2023
Int. Cl. G06F 16/22 (2019.01); G06F 16/2458 (2019.01); G06F 21/60 (2013.01); G06F 21/62 (2013.01); G16H 50/70 (2018.01)
CPC G06F 21/6245 (2013.01) [G06F 16/2237 (2019.01); G06F 16/2458 (2019.01); G06F 16/2462 (2019.01); G06F 21/602 (2013.01); G16H 50/70 (2018.01); G06F 2221/2115 (2013.01)] 10 Claims
OG exemplary drawing
 
1. A computerized method for dataset verification in a zero-trust computing environment, the method comprising:
receiving a sample dataset comprising records;
generating a sample vector for the entire sample dataset normalized by the total number or records in the sample dataset;
selecting a subset of data from the sample dataset;
generating a matrix from the subset of data;
divide the matrix into a series of vectors;
generating an example vector, wherein the example vector is generated by summing the series of vectors and normalizing the sum by the number of records in the subset of data;
calculating a difference between the sample vector-set and the example vector;
when the difference is below a threshold, applying a machine learning algorithm to the subset of data; and
when the difference is above a threshold, rejecting the subset of data.