US 12,260,343 B2
Methods and apparatus for identifying concepts corresponding to input information
Nadiya Yampolska, Kitchener (CA); Mathew Whitney Wilson, Kitchener (CA); Andrew Russell, Duncan (CA); and Ihab Francis Ilyas, Waterloo (CA)
Assigned to Primal Fusion Inc., Kitchener (CA)
Filed by Primal Fusion Inc., Kitchener (CA)
Filed on Jul. 13, 2015, as Appl. No. 14/797,858.
Application 14/797,858 is a continuation of application No. 14/760,239, granted, now 10,755,179, previously published as PCT/CA2014/000016, filed on Jan. 13, 2014.
Claims priority of provisional application 61/751,571, filed on Jan. 11, 2013.
Claims priority of provisional application 61/751,594, filed on Jan. 11, 2013.
Claims priority of provisional application 61/751,623, filed on Jan. 11, 2013.
Claims priority of provisional application 61/751,659, filed on Jan. 11, 2013.
Prior Publication US 2015/0356418 A1, Dec. 10, 2015
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 5/022 (2023.01); G06F 16/33 (2019.01); G06F 16/3332 (2025.01); G06F 16/35 (2019.01); G06F 16/36 (2019.01); G06F 16/38 (2019.01); G06F 16/901 (2019.01); G06F 16/955 (2019.01); G06F 40/205 (2020.01); G06Q 30/02 (2023.01)
CPC G06N 5/022 (2013.01) [G06F 16/3334 (2019.01); G06F 16/35 (2019.01); G06F 16/367 (2019.01); G06F 16/38 (2019.01); G06F 16/9024 (2019.01); G06F 16/955 (2019.01); G06F 40/205 (2020.01); G06Q 30/02 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A method for using knowledge representation construction to augment user context information in a user request, the method comprising:
obtaining user context information associated with a user request, wherein the user context information comprises a first portion and a second portion different from the first portion;
identifying a first group of concepts in a knowledge representation (KR), wherein each concept in the first group of concepts covers a meaning of the user context information, wherein each concept in the first group of concepts is associated with a different meaning of the first portion, wherein each concept in the first group of concepts has a label associated therewith that is substantially the same as each label associated with the other concepts in the first group of concepts;
identifying a second group of concepts in the KR, wherein each concept in the second group of concepts is a semantic context for a respective concept in the first group of concepts;
wherein the identifying is performed at least in part by using at least one processor, and said identifying is based on a measure of dominance and a measure of semantic coherence exceeding a predetermined threshold, and
constructing a context intersection graph using the first group of concepts and the second group of concepts as nodes, and a measure of semantic coherence between the first group of concepts and the second group of concepts as edges, wherein calculating the measure of semantic coherence comprises:
identifying a first semantic context of the first group of concepts in the KR, wherein the first semantic context is defined as a number of concepts in the first group of concepts which are within a predetermined distance of said first concept in said KR; and
identifying a second semantic context of the second group of concepts in the KR, wherein the second semantic context is defined as a number of concepts in the second group of concepts which are within a predetermined distance of said second concept in said KR;
ranking concepts in the context intersection graph based on the measure of semantic coherence of concepts, wherein ranking includes:
calculating a score for each concept in the first group of concepts at least in part by using the context intersection graph; and
identifying the first concept as a concept in the first group of concepts having the highest score; and
augmenting the user request using the user context information and the ranked concepts.