US 11,915,293 B2
Offering automobile recommendations from generic features learned from natural language inputs
Micah Price, Plano, TX (US); Stephen Wylie, Carrollton, TX (US); Habeeb Hooshmand, McKinney, TX (US); Jason Hoover, Grapevine, TX (US); Geoffrey Dagley, McKinney, TX (US); and Qiaochu Tang, The Colony, TX (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Nov. 10, 2020, as Appl. No. 17/094,088.
Application 17/094,088 is a division of application No. 16/254,504, filed on Jan. 22, 2019, granted, now 10,867,338.
Prior Publication US 2021/0056613 A1, Feb. 25, 2021
Int. Cl. G06Q 30/0601 (2023.01); G06F 16/9538 (2019.01); G06N 20/00 (2019.01); G06N 7/01 (2023.01)
CPC G06Q 30/0631 (2013.01) [G06F 16/9538 (2019.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving a first data set and a second data set from a corpus of one or more expert automobile reviews, the first data set and the second data set comprising generic text related to a plurality of automobile makes and models and specific text related to at least one feature of at least one of the plurality of automobile makes and models, wherein the generic text of the corpus of one or more expert automobile reviews is related to the specific text of the corpus of the one or more expert automobile reviews;
training, by at least one computer processor, a machine learning model (MLM) based on at least the first data set;
generating, by the MLM, a respective probability distribution for one or more specific automobile makes and models in relation to generic automobile text by analyzing a relationship between the generic text of the corpus of one or more expert automobile reviews and the specific text of the corpus of one or more expert automobile reviews;
receiving a third data set comprising a generic automobile text, the generic automobile text comprising a preference of an account; and
generating, by the MLM, a recommendation comprising a specific automobile make and model corresponding to the generic automobile text of the third data set, the recommendation based on the probability distribution for the specific automobile make and model.