Jinna Kim

20210390616, Dec 16, 2021

Jun 10, 2020

16/897394

- Charlotte NC, US
Elena Kvochko - New York NY, US
Albena N. Fairchild - Spruce Pine NC, US
Jinna Kim - Charlotte NC, US

G06Q 40/02
G06F 21/62
G06Q 50/00

Aspects of the disclosure relate to using machine learning to modify account privacy settings. A computing platform may identify initial account settings for an individual of a plurality of individuals. Using a settings optimization model, the computing platform may identify account data and third party data for the individual. Using the settings optimization model, the computing platform may identify discrepancies between the initial account settings for the first individual and account settings for a subset of the plurality of individuals having common characteristics with the individual. Based on the discrepancies, the computing platform may identify settings modifications for the individual, and may determine that a modification of the settings modifications applies to a detected interaction of the individual. The computing platform may direct an enterprise data source to perform the modification, which may cause the enterprise data source to modify the initial account settings based on the modification.

20210304088, Sep 30, 2021

Mar 24, 2020

16/827825

- Charlotte NC, US
Elena Kvochko - New York NY, US
Jinna Kim - Charlotte NC, US
Christine D. Black - Belfast ME, US
Monika Kapur - Jacksonville FL, US

G06Q 10/06
G06F 9/50
G06N 20/00

Systems for using cognitive automation techniques to generate resource recommendations are provided. In some examples, current resource data may be received. The current resource data may include current staffing data, skills associated with current staff, and the like. In some examples, environmental condition data may also be received. The environmental condition data may include current weather information, predicted weather information, power outage information, and the like. The resource data and environmental data may be analyzed using cognitive automation techniques to identify and/or generate one or more resource recommendations. The resource recommendations may include adjustments to staffing, modification of locations at which processes are performed, and the like. The identified or generated recommendations may be transmitted to one or more computing devices. In some examples, feedback data may be received based on the recommendations and the cognitive automation model may be updated and/or validated based on the feedback data.

20210295223, Sep 23, 2021

Mar 20, 2020

16/824879

- Charlotte NC, US
Maria Carolina Barraza - New York NY, US
Richard S. Scot - Huntersville NC, US
Elena Kvochko - New York NY, US
Monika Kapur - Jacksonville FL, US
Jinna Kim - Charlotte NC, US
Michael Ogrinz - Easton CT, US
George Albero - Charlotte NC, US
Siten Sanghvi - Westfield NJ, US

G06Q 10/06
G06Q 30/02
G06Q 30/06
G06Q 30/00
G06Q 10/10
G06F 11/30
G06F 11/34
G06F 16/245
G06N 20/00

Aspects of the disclosure relate to a cognitive automation based vendor compliance system. A computing platform may monitor, via an enterprise network, use of a software product hosted by an enterprise computing infrastructure. Subsequently, the computing platform may determine a product usage score, where the product usage score is indicative of a number network devices using the software product. Then, the computing platform may determine, for the software product, a product performance score, where the product performance score is indicative of one or more performance issues associated with the software product. Then, the computing platform may compare, for the software product, at least one of: (1) the product usage score to a product usage threshold, and (2) the product performance score to a product performance threshold. Subsequently, the computing platform may recommend, based on the comparing, one or more actions associated with the software product.