US 12,191,004 B2
Machine learning system with two encoder towers for semantic matching
Sudipto Mukherjee, Seattle, WA (US); Liang Du, Redmond, WA (US); Ke Jiang, Bellevue, WA (US); and Robin Abraham, Redmond, WA (US)
Assigned to MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, WA (US)
Filed by MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, WA (US)
Filed on Jun. 27, 2022, as Appl. No. 17/850,763.
Prior Publication US 2023/0420085 A1, Dec. 28, 2023
Int. Cl. G16C 20/70 (2019.01); G06N 3/04 (2023.01); G16C 20/10 (2019.01)
CPC G16C 20/70 (2019.02) [G06N 3/04 (2013.01); G16C 20/10 (2019.02)] 20 Claims
OG exemplary drawing
 
1. A machine learning system for identifying one or more candidate chemical reaction procedures from a chemical reaction sketch, the system comprising:
a processor;
a memory comprising computer-readable instructions executable by the processor;
a datastore comprising a corpus of chemical reaction procedures;
an interface configured to receive the chemical reaction sketch from a user computing device;
a reaction encoder configured to create a reaction embedding of the chemical reaction sketch, the reaction encoder comprising a reaction transformer network, a reaction pooling layer, a reaction normalization layer, and a reaction neural network;
a procedure encoder configured to create procedure embeddings of the chemical reaction procedures in the corpus of chemical reaction procedures, the procedure encoder comprising a procedure transformer network, a procedure pooling layer, a procedure normalization layer, and a procedure neural network;
a similarity-assessing mechanism configured to determine a similarity between the reaction embedding and the procedure embeddings in a shared embedding space; and
an output mechanism configured to provide to the interface a predetermined number of candidate chemical reaction procedures from the corpus of chemical reaction procedures, the candidate chemical reaction procedures corresponding to procedure embeddings identified by the similarity-assessing mechanism as having the highest similarity to the reaction embedding.