US 12,482,014 B2
Confidence levels in management and determination of user identity using identity graphs
Brenda Maas, Elk River, MN (US); Jill Lemmerman, Minneapolis, MN (US); Murthy Remella, Karnataka (IN); Abhishek Srivastava, Minneapolis, MN (US); Paritosh Desai, San Francisco, CA (US); Michael Whitsitt, Lakeville, MN (US); Devanathan Rajagopalan, Karnataka (IN); Kristina Taylor, Minneapolis, MN (US); Akhilesh Singh, Karnataka (IN); Shomit Goyal, Karnataka (IN); and Andrea Hitzman, Minneapolis, MN (US)
Assigned to Target Brands, Inc., Minneapolis, MN (US)
Filed by Target Brands, Inc., Minneapolis, MN (US)
Filed on Jan. 17, 2023, as Appl. No. 18/097,979.
Claims priority of application No. 202211070303 (IN), filed on Dec. 6, 2022.
Prior Publication US 2024/0185284 A1, Jun. 6, 2024
Int. Cl. G06Q 30/0204 (2023.01); G06Q 20/40 (2012.01); G06Q 30/0226 (2023.01); G06Q 30/0251 (2023.01); G06Q 30/0601 (2023.01); H04L 67/306 (2022.01)
CPC G06Q 30/0205 (2013.01) [G06Q 20/401 (2013.01); G06Q 20/4014 (2013.01); G06Q 30/0204 (2013.01); G06Q 30/0229 (2013.01); G06Q 30/0269 (2013.01); G06Q 30/0631 (2013.01); H04L 67/306 (2013.01)] 23 Claims
OG exemplary drawing
 
1. A method comprising:
receiving a request, from a requesting entity, for a user identity at an identity management platform associated with an enterprise, the identity management platform maintaining an identity graph of a plurality of users, wherein the user identity platform includes a random forest classifier model;
receiving a set of training data including transaction data, pairs of known correlated user profile nodes, and pairs of known non-correlated nodes;
generating a set of values based on the training data to define account to transaction correlations for each of a plurality of pairs of store and transaction data collections, wherein the set of values are one or more of a binary, integer, numerical, or string value and each collection represents a separate user profile node;
training the random forest classifier model with the set of values to enable the random forest classifier to identify a likely correlation across two accounts in response to receiving an identification of two nodes or two sets of transaction data, wherein the random forest classifier model is configured to generate a plurality of parallel probability analyses and includes an aggregation layer configured to output a probability score representing a normalized likelihood of similarity between two nodes;
identifying, in response to the request, using at least the trained random forest classifier model, a user cluster within the identity graph associated with a user identifiable via the request, the user cluster including one or more user profile nodes, wherein the user cluster includes a plurality of edge connections including at least one identity edge connection linking between two user profile nodes of the user cluster, each of the user profile nodes being associated with a user account established with the enterprise and having a node confidence associated therewith;
identifying, using at least the trained random forest classifier model, at least one of the one or more user profile nodes based on whether a cluster edge confidence associated with the one or more user profile nodes included within the user cluster meets a threshold confidence level, the threshold confidence level being based, at least in part, on the request, and the cluster edge confidence being based in part on the node confidence; and
transmitting, to the requesting entity, an identification of the at least one user profile node that meets the threshold confidence level.