US 12,093,300 B1
Enhancing accuracy of entity matching inference using large language models
Yi Quan Zhou, Singapore (SG); Rajesh Vellore Arumugam, Singapore (SG); Raja Sekhar Juluri, Singapore (SG); Xingce Bao, Singapore (SG); and Eshwin Sukhdeve, Singapore (SG)
Assigned to SAP SE, Walldorf (DE)
Filed by SAP SE, Walldorf (DE)
Filed on Sep. 8, 2023, as Appl. No. 18/463,519.
Int. Cl. G06F 7/00 (2006.01); G06F 16/33 (2019.01); G06F 16/332 (2019.01); G06F 16/35 (2019.01); G06F 40/174 (2020.01); G06F 40/186 (2020.01)
CPC G06F 16/35 (2019.01) [G06F 16/3329 (2019.01); G06F 16/3344 (2019.01); G06F 40/174 (2020.01); G06F 40/186 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for improved document matching using machine learning (ML) models in ML-based decision systems, the method being executed by one or more processors and comprising:
receiving a first document comprising structured data and unstructured data;
providing a first sub-document and a second sub-document, the first sub-document comprising the structured data of the first document, the second sub-document comprising the unstructured data of the first document;
generating a prompt using the second sub-document and a second document;
inputting the prompt to a large language model (LLM);
receiving a response from the LLM;
providing a calibrated first document by merging the response into the first sub-document; and
processing the calibrated first document and the second document using a ML model to provide a prediction, the prediction indicating a matching class between the first document and the second document.