US 12,293,403 B2
Classification of query text to generate relevant query results
Vikram Srinivasan, Karnataka (IN); Srinivasan Seshadri, Los Altos, CA (US); and Richard Wang, Sunnyvale, CA (US)
Assigned to Target Brands, Inc., Minneapolis, MN (US)
Filed by Target Brands, Inc., Minneapolis, MN (US)
Filed on Jun. 2, 2021, as Appl. No. 17/337,183.
Application 17/337,183 is a continuation of application No. 16/366,949, filed on Mar. 27, 2019, granted, now 11,055,765.
Prior Publication US 2021/0287277 A1, Sep. 16, 2021
Int. Cl. G06Q 30/00 (2023.01); G06F 16/953 (2019.01); G06F 17/15 (2006.01); G06F 18/24 (2023.01); G06F 40/30 (2020.01); G06N 3/04 (2023.01); G06N 3/0464 (2023.01); G06N 20/00 (2019.01); G06Q 30/0601 (2023.01)
CPC G06Q 30/0641 (2013.01) [G06F 16/953 (2019.01); G06F 17/15 (2013.01); G06F 18/24 (2023.01); G06F 40/30 (2020.01); G06N 3/04 (2013.01); G06N 3/0464 (2023.01); G06N 20/00 (2019.01); G06Q 30/0625 (2013.01); G06Q 30/0633 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method of classifying text, the method comprising:
identifying a plurality of classes;
receiving user interaction data associated with a particular text;
associating the particular text with at least one of the plurality of classes based on the user interaction data;
generating text-to-class data based on the association;
providing the text-to-class data as training data to an N-gram convolutional neural network; and
in response to receiving new text for classification, providing the new text as input to the N-gram convolutional neural network to generate a subset of classes from the plurality of classes for classifying the new text, the N-gram convolutional neural network building a plurality of N-grams comprised of multiple N-gram levels to analyze the new text using the multiple N-gram levels in conjunction with the training data to output the subset of classes.