US 12,470,503 B2
Customized message suggestion with user embedding vectors
Kelsey Taylor Ball, Jersey City, NJ (US); Tao Lei, Jersey City, NJ (US); Christopher David Fox, Mastic, NY (US); and Joseph Ellsworth Hackman, New York, NY (US)
Assigned to ASAPP, INC., New York, NY (US)
Filed by ASAPP, INC., New York, NY (US)
Filed on Jul. 1, 2022, as Appl. No. 17/856,752.
Application 17/856,752 is a continuation of application No. 16/663,872, filed on Oct. 25, 2019, granted, now 11,425,064.
Prior Publication US 2022/0337538 A1, Oct. 20, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 40/00 (2020.01); G06F 40/56 (2020.01); G06N 3/049 (2023.01); H04L 51/046 (2022.01)
CPC H04L 51/046 (2013.01) [G06F 40/56 (2020.01); G06N 3/049 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
obtaining a user embedding vector corresponding to a user, wherein the user embedding vector represents the user in a first vector space;
receiving text of a conversation with the user;
computing a conversation feature vector using the text of the conversation and a first neural network, wherein the conversation feature vector represents the conversation in a second vector space;
obtaining a set of designated messages, wherein each designated message is associated with a corresponding designated message feature vector;
computing a first context score for a first designated message of the set of designated messages by processing the user embedding vector, the conversation feature vector, and a first designated message feature vector with a second neural network;
selecting the first designated message using the first context score; and
presenting the first designated message as a suggested message to the user.
 
9. A system, comprising:
at least one server computer comprising at least one processor and at least one memory, the at least one server computer configured to:
obtain a user embedding vector corresponding to a user, wherein the user embedding vector represents the user in a first vector space;
receive text of a conversation with the user;
compute a conversation feature vector using the text of the conversation and a first neural network, wherein the conversation feature vector represents the conversation in a second vector space;
obtain a set of designated messages, wherein each designated message is associated with a corresponding designated message feature vector;
compute a first context score for a first designated message of the set of designated messages by processing the user embedding vector, the conversation feature vector, and a first designated message feature vector with a second neural network;
select the first designated message using the first context score; and
present the first designated message as a suggested message to the user.