US 12,423,612 B2
Methods, mediums, and systems for generating causal inference structure between concepts having predictive capabilities
Omar Florez Choque, Oakland, CA (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Apr. 9, 2021, as Appl. No. 17/226,619.
Application 17/226,619 is a continuation of application No. 16/704,567, filed on Dec. 5, 2019, granted, now 10,977,580.
Prior Publication US 2021/0232972 A1, Jul. 29, 2021
Int. Cl. G06N 20/00 (2019.01); G06F 16/901 (2019.01); G06N 3/08 (2023.01); G10L 15/18 (2013.01); G10L 15/22 (2006.01); G06Q 30/016 (2023.01)
CPC G06N 20/00 (2019.01) [G06F 16/9024 (2019.01); G06N 3/08 (2013.01); G10L 15/1815 (2013.01); G10L 15/22 (2013.01); G06Q 30/016 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus, comprising:
a processor; and
a memory comprising instructions that when executed by the processor cause the processor to:
analyze textual input data to determine a causal relationship between a first word in the textual input data and a second word in the textual input data based on semantics of the textual input data, wherein determining the causal relationship comprises mapping the first word to a first concept using an unsupervised artificial neural network by learning a first vector representation for the first word; mapping the second word to a second concept using the unsupervised artificial neural network by learning a second vector representation for the second word; generating a hypothesis comprising the first concept and the second concept, the hypothesis representing a prediction that the first concept is dependent upon the second concept; defining a probability distribution describing a likelihood of observing the first concept given the second concept; and assigning a coefficient to the second concept based on an amount of contribution of the second concept to the first concept, the coefficient for the second concept comprising a vector in a semantic space;
determine a weight for a connection in a causal inference structure, the connection between the first and second words in the textual input data based on the causal relationship determined between the first and second words; and
generate the causal inference structure comprising the first word, the second word, and the connection with the weight between the first and second words.