US 11,776,036 B2
Generating and utilizing classification and query-specific models to generate digital responses to queries from client device
Tuan Manh Lai, San Jose, CA (US); Trung Bui, San Jose, CA (US); Sheng Li, San Jose, CA (US); Quan Hung Tran, Melbourne (AU); and Hung Bui, Sunnyvale, CA (US)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Apr. 19, 2018, as Appl. No. 15/957,556.
Prior Publication US 2019/0325068 A1, Oct. 24, 2019
Int. Cl. G06Q 30/0601 (2023.01); G06N 3/08 (2023.01); G06F 16/951 (2019.01); G06F 16/583 (2019.01); G06V 10/764 (2022.01)
CPC G06Q 30/0631 (2013.01) [G06F 16/583 (2019.01); G06F 16/951 (2019.01); G06N 3/08 (2013.01); G06V 10/764 (2022.01)] 7 Claims
OG exemplary drawing
 
1. A system comprising:
at least one processor; and
a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to:
identify a training query that corresponds to a first product specification of a training product, wherein the training product corresponds to a plurality of training product specifications;
train a neural ranking model comprising a bidirectional long short-term memory layer and a differential product concatenation layer to generate relevance scores between product specifications and digital queries by, for each training product specification of the plurality of training product specifications:
generating a training concatenated vector utilizing the differential product concatenation layer to concatenate a difference vector representing a difference between a training query vector and a training product specification vector together with a product vector representing a product of the training query vector and the training product specification vector;
generating a relevance score indicating a measure of correspondence between the training product specification and the training query by utilizing the bidirectional long short-term memory layer to process the training concatenated vector; and
comparing the relevance score to a ground truth score for the training product specification, the ground truth score indicating whether the training product specification is the first product specification that corresponds to the training query.