US 12,406,213 B2
System and method for generating financing structures using clustering
Saman Baghestani, Plano, TX (US); Rohan Shah, Frisco, TX (US); and Nicholas E. Dolle, Mckinney, TX (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Jan. 3, 2023, as Appl. No. 18/092,500.
Application 18/092,500 is a continuation of application No. 15/931,095, filed on May 13, 2020, granted, now 11,544,727.
Prior Publication US 2023/0136862 A1, May 4, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 10/04 (2023.01); G06F 18/232 (2023.01); G06F 18/23213 (2023.01); G06N 20/00 (2019.01); G06Q 30/0201 (2023.01)
CPC G06Q 10/04 (2013.01) [G06F 18/232 (2023.01); G06F 18/23213 (2023.01); G06N 20/00 (2019.01); G06Q 30/0201 (2013.01); G06Q 30/0206 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A computer implemented method for increasing accuracy of weighing a plurality of attributes in a decision model, and revealing which dimension is most impactful in the decision model, the computer implemented method comprising:
concurrently executing, by a plurality of hypervisors, multiple instances of a plurality of operating systems to provide hardware virtualization;
performing block virtualization, by a virtualized storage component of the hardware virtualization, that separates logical storage from physical storage to allow attributes of a data store to be accessed without regard to a heterogeneous structure;
receiving, at a profile engine, the attributes accessed from the data store;
performing file virtualization, using a virtualized storage component of the hardware virtualization, to eliminate dependencies between the accessed attributes at a file-level and a location where files are physically stored;
generating, by the profile engine, a multi-dimensional graph of a plurality of n-dimensions where each dimension of the n-dimensions corresponds with a respective attribute of the attributes;
generating, by the profile engine, a bell curve along the n-dimensions of the multi-dimensional graph;
determining, by the profile engine, which ones of the attributes impact the decision model more than a threshold amount according to a comparison of the attributes against the generated bell curve at a specified value for each of the attributes;
identifying, by unsupervised machine learning, clusters of variable K to each of the attributes processed by K-means clustering that assigns each of the clusters to each of a plurality of K groups based on feature weights;
positioning, by the profile engine on the multi-dimensional graph, the identified clusters, along a first axis comprising the n-dimensions and, along a second axis representing the feature weights, based on how much weight to give to each of the attributes along each of the n-dimensions;
computing, by the profile engine, a distribution of each graph of the multi-dimensional graph along the first and second axes indicating how statistically reliable each of the n-dimensions are in determining predictable patterns of characterization for each of the identified clusters in response to respective combinations of the n-dimensions;
recalibrating, by the profile engine, the feature weights of the attributes for the decision model, as the profile engine receives more data for the attributes according to a continuous decrease in variance between observed and expected outcomes of the decision model to improve accuracy over time of the weighing of each of the attributes within each of the n-dimensions; and
generating, by the profile engine, along dimension lines of the multi-dimensional graph, a plurality of maximum nodes connected to a plurality of edges of a maximum value for each dimension of the n-dimensions along the dimension lines, indicating which particular dimension of the n-dimensions is most impactful in the decision model, according to said particular dimension having a higher value of its node up to the maximum nodes.