US 12,153,879 B2
Syntactic and semantic autocorrect learning
Elizabeth Daly, Dublin (IE); Oznur Alkan, Clonsilla (IE); Anup Kalia, White Plains, NY (US); Jin Xiao, White Plains, NY (US); Bei Chen, Blanchardstown (IE); and Rahul Nair, Dublin (IE)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Apr. 19, 2022, as Appl. No. 17/659,823.
Prior Publication US 2023/0334241 A1, Oct. 19, 2023
Int. Cl. G06F 40/166 (2020.01); G06F 16/335 (2019.01); G06F 40/211 (2020.01); G06F 40/232 (2020.01); G06N 5/022 (2023.01)
CPC G06F 40/232 (2020.01) [G06F 16/337 (2019.01); G06F 40/166 (2020.01); G06F 40/211 (2020.01); G06N 5/022 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for learning and correcting errors in text input fields to an artificial intelligence (AI) system, the computer-implemented method, comprising:
receiving, by the AI system from a target system, an input text;
executing, by the AI system, a text processing operation on the input text by applying at least one of a plurality of transformers from a library of transformers to the input text to generate transformed text;
outputting, by the AI system, at least one proposed correction to the input text according to an analysis of the transformed text resultant from the text processing operation; and
using feedback data, associated with the at least one proposed correction, received from a user of the target system to iteratively learn, by the AI system, which of the plurality of transformers need be applied on future input text to accurately generate future proposed corrections on the future input text.