US 12,153,640 B2
Machine-learning based document recommendation for online real-time communication system
Feifei Jiang, San Mateo, CA (US); Zachary Alexander, Berkeley, CA (US); Yuanxin Wang, Mountain View, CA (US); Yixin Mao, San Mateo, CA (US); Sitaram Asur, Newark, CA (US); Regunathan Radhakrishnan, Dublin, CA (US); and Aron Kale, Huntington Beach, CA (US)
Assigned to Salesforce, Inc., San Francisco, CA (US)
Filed by Salesforce, Inc., San Francisco, CA (US)
Filed on Dec. 12, 2022, as Appl. No. 18/079,857.
Prior Publication US 2024/0193213 A1, Jun. 13, 2024
Int. Cl. G06F 16/9535 (2019.01); G06F 16/9538 (2019.01)
CPC G06F 16/9535 (2019.01) [G06F 16/9538 (2019.01)] 18 Claims
OG exemplary drawing
 
1. A method comprising:
establishing a communication session between a human agent and a user, wherein the communication session is over an electrical medium and relates to a tenant of a plurality of tenants accessing computing services via a cloud platform;
transmitting one or more instructions to generate an interface on a client device associated with the human agent, wherein a first portion of the interface is configured to display an exchange of one or more messages between the human agent and the user for a conversation between the human agent and the user;
obtaining, at a first time, a set of utterances from a transcript of the conversation, wherein an utterance includes a sequence of words from the human agent to the user or from the user to the human agent during the conversation;
accessing a database to identify a plurality of articles specific to the tenant;
generating relevance scores between the conversation and the plurality of articles, wherein a relevance score between the conversation and an article is generated by (1) applying a query retrieval function that estimates a relevance of the article to the transcript and (2) determining an encoding of the transcript to a conversation embedding via a bi-directional encoder representation from transformer model;
selecting a subset of articles having relevance scores above a threshold value; and
presenting the selected subset of articles on a second portion of the interface.