Invention Grant
- Patent Title: Dimensional reduction of complex vectors in artificially intelligent solutions to compare similarity of natural language text
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Application No.: US17089658Application Date: 2020-11-04
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Publication No.: US11409752B1Publication Date: 2022-08-09
- Inventor: Javed Qadrud-Din , Ryan Walker , Ravi Soni , Marcin Gajek , Gabriel Pack , Akhil Rangaraj
- Applicant: Casetext, Inc.
- Applicant Address: US CA San Francisco
- Assignee: Casetext, Inc.
- Current Assignee: Casetext, Inc.
- Current Assignee Address: US CA San Francisco
- Agency: Maynard Cooper & Gale, P.C.
- Agent Jon E. Holland, Esq.
- Main IPC: G06F7/00
- IPC: G06F7/00 ; G06F16/2457 ; G06N20/00 ; G06F40/126 ; G06F40/279 ; G06F16/242

Abstract:
A web-based tool performs records matching in response to a freeform text input, to find highly contextually-related sentences in a corpus of records. Each sentence in the corpus is converted into a full-size vector representation, and each vector's angle within space is measured. Each full-size vector is compressed to a smaller vector and a loss function is used to preserve for each vector the angle within the lower-dimensional space that existed for the higher-dimensional vector. Full-size and reduced vector representations are generated from the freeform text input. The reduced-size vector of the input is compared to those of the corpus of text to identify, in real-time, a set of vector nearest neighbors that includes, with high accuracy, representations of all records in the corpus similar to the input. Full-size vectors for the nearest neighbors are in turn retrieved and compared to the input, and ranked results are generated.
Information query