US 12,405,844 B2
Systems and methods for synthetic database query generation
Jeremy Goodsitt, Champaign, IL (US); Austin Walters, Savoy, IL (US); Vincent Pham, Champaign, IL (US); and Fardin Abdi Taghi Abad, Champaign, IL (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by CAPITAL ONE SERVICES, LLC, McLean, VA (US)
Filed on Oct. 28, 2022, as Appl. No. 18/050,694.
Application 18/050,694 is a continuation of application No. 16/298,463, filed on Mar. 11, 2019, granted, now 11,513,869.
Claims priority of provisional application 62/694,968, filed on Jul. 6, 2018.
Prior Publication US 2023/0073695 A1, Mar. 9, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 20/00 (2019.01); G06F 8/71 (2018.01); G06F 9/54 (2006.01); G06F 11/3604 (2025.01); G06F 11/362 (2025.01); G06F 16/22 (2019.01); G06F 16/242 (2019.01); G06F 16/2455 (2019.01); G06F 16/248 (2019.01); G06F 16/25 (2019.01); G06F 16/28 (2019.01); G06F 16/335 (2019.01); G06F 16/903 (2019.01); G06F 16/9032 (2019.01); G06F 16/9038 (2019.01); G06F 16/906 (2019.01); G06F 16/93 (2019.01); G06F 17/15 (2006.01); G06F 17/16 (2006.01); G06F 17/18 (2006.01); G06F 18/20 (2023.01); G06F 18/21 (2023.01); G06F 18/2115 (2023.01); G06F 18/213 (2023.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/23 (2023.01); G06F 18/24 (2023.01); G06F 18/2411 (2023.01); G06F 18/2415 (2023.01); G06F 18/40 (2023.01); G06F 21/55 (2013.01); G06F 21/60 (2013.01); G06F 21/62 (2013.01); G06F 30/20 (2020.01); G06F 40/117 (2020.01); G06F 40/166 (2020.01); G06F 40/20 (2020.01); G06N 3/04 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/06 (2006.01); G06N 3/08 (2023.01); G06N 3/088 (2023.01); G06N 3/094 (2023.01); G06N 5/00 (2023.01); G06N 5/02 (2023.01); G06N 5/04 (2023.01); G06N 7/00 (2023.01); G06N 7/01 (2023.01); G06Q 10/04 (2023.01); G06T 7/194 (2017.01); G06T 7/246 (2017.01); G06T 7/254 (2017.01); G06T 11/00 (2006.01); G06V 10/70 (2022.01); G06V 10/98 (2022.01); G06V 30/194 (2022.01); G06V 30/196 (2022.01); H04L 9/40 (2022.01); H04L 67/00 (2022.01); H04L 67/306 (2022.01); H04N 21/234 (2011.01); H04N 21/81 (2011.01)
CPC G06F 9/541 (2013.01) [G06F 8/71 (2013.01); G06F 9/54 (2013.01); G06F 9/547 (2013.01); G06F 11/3608 (2013.01); G06F 11/3628 (2013.01); G06F 11/3636 (2013.01); G06F 16/2237 (2019.01); G06F 16/2264 (2019.01); G06F 16/2423 (2019.01); G06F 16/24568 (2019.01); G06F 16/248 (2019.01); G06F 16/254 (2019.01); G06F 16/258 (2019.01); G06F 16/283 (2019.01); G06F 16/285 (2019.01); G06F 16/288 (2019.01); G06F 16/335 (2019.01); G06F 16/90332 (2019.01); G06F 16/90335 (2019.01); G06F 16/9038 (2019.01); G06F 16/906 (2019.01); G06F 16/93 (2019.01); G06F 17/15 (2013.01); G06F 17/16 (2013.01); G06F 17/18 (2013.01); G06F 18/2115 (2023.01); G06F 18/213 (2023.01); G06F 18/214 (2023.01); G06F 18/2148 (2023.01); G06F 18/217 (2023.01); G06F 18/2193 (2023.01); G06F 18/22 (2023.01); G06F 18/23 (2023.01); G06F 18/24 (2023.01); G06F 18/2411 (2023.01); G06F 18/2415 (2023.01); G06F 18/285 (2023.01); G06F 18/40 (2023.01); G06F 21/552 (2013.01); G06F 21/60 (2013.01); G06F 21/6245 (2013.01); G06F 21/6254 (2013.01); G06F 30/20 (2020.01); G06F 40/117 (2020.01); G06F 40/166 (2020.01); G06F 40/20 (2020.01); G06N 3/04 (2013.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/06 (2013.01); G06N 3/08 (2013.01); G06N 3/088 (2013.01); G06N 3/094 (2023.01); G06N 5/00 (2013.01); G06N 5/02 (2013.01); G06N 5/04 (2013.01); G06N 7/00 (2013.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06Q 10/04 (2013.01); G06T 7/194 (2017.01); G06T 7/246 (2017.01); G06T 7/248 (2017.01); G06T 7/254 (2017.01); G06T 11/001 (2013.01); G06V 10/768 (2022.01); G06V 10/993 (2022.01); G06V 30/194 (2022.01); G06V 30/1985 (2022.01); H04L 63/1416 (2013.01); H04L 63/1491 (2013.01); H04L 67/306 (2013.01); H04L 67/34 (2013.01); H04N 21/23412 (2013.01); H04N 21/8153 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for generating synthetic results based on a query input, comprising:
one or more processors and memories storing instructions that, when executed by the one or more processors, cause operations comprising:
in response to a user interaction, with a user interface, involving a query input, inputting the query input into a natural language processing model to derive a type of the query input;
routing, via a network, based on the type of the query input, the query input to a trained model of a plurality of models, wherein the trained model is trained to generate synthetic values for a selected subclass within a class and not for other subclasses within the class; and
based on the routing of the query input to the trained model, inputting the query input to the trained model to generate a subclass-specific synthetic dataset that satisfies a statistical similarity criterion associated with both the synthetic dataset and a reference dataset, wherein the statistical similarity criterion is one or more of a statistical correlation score between the synthetic dataset and the reference dataset, a data similarity score between the synthetic dataset and the reference dataset, or a data quality score for the synthetic dataset.