US 12,488,192 B2
Generating recommendations by using communicative discourse trees of conversations
Boris Galitsky, San Jose, CA (US)
Assigned to Oracle International Corporation, Redwood Shores, CA (US)
Filed by Oracle International Corporation, Redwood Shores, CA (US)
Filed on Jan. 19, 2023, as Appl. No. 18/156,697.
Application 18/156,697 is a continuation of application No. 17/021,835, filed on Sep. 15, 2020, granted, now 11,599,731.
Claims priority of provisional application 62/909,350, filed on Oct. 2, 2019.
Prior Publication US 2023/0153540 A1, May 18, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 40/35 (2020.01); G06F 16/242 (2019.01); G06F 40/253 (2020.01); G06F 40/295 (2020.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01)
CPC G06F 40/35 (2020.01) [G06F 16/242 (2019.01); G06F 40/253 (2020.01); G06F 40/295 (2020.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method for providing a recommendation in conversational form, the method comprising:
determining a first sentiment score for a first utterance;
determining a second sentiment score for a second utterance, wherein the first sentiment score and the second sentiment score individually indicate an emotion indicated by a respective utterance, and wherein determining the first sentiment score and the second sentiment score comprises:
creating a communicative discourse tree from text comprising an utterance, wherein the communicative discourse tree comprises a discourse tree with elementary discourse units that are annotated with verb signatures;
providing the communicative discourse tree to a machine-learning model, the machine-learning model being trained to identify emotions based on input communicative discourse trees for which emotion associations are known; and
receiving a sentiment score from the machine-learning model;
identifying that a difference between the first sentiment score and the second sentiment score is greater than a threshold;
extracting a noun phrase from the second utterance;
identifying, in an entity database, a text fragment that comprises an entity that corresponds to the noun phrase;
verifying that the text fragment addresses a claim of the second utterance;
forming a third utterance that comprises the text fragment; and
outputting the third utterance to a user device.