Cross-document intelligent authoring and processing, with arbitration for semantically-annotated documents
US11960832B2 · kind B2 · utility
Assignee
Inventors
- Andrew Paul Begun
- Steven DeRose
- Taqi Jaffri
- Luis Marti Orosa
- Michael B. Palmer
- Jean Paoli
- Christina Pavlopoulou
- Elena Pricoiu
- Swagatika Sarangi
- Marcin Sawicki
- Manar Shehadeh
- Michael Taron
- Bhaven Toprani
- Zubin Rustom Wadia
- David Watson
- Eric White
- Joshua Yongshin Fan
- Kush Gupta
- Andrew Minh Hoang
- Zhanlin Liu
- Jerome George Paliakkara
- Zhaofeng Wu
- Yue Zhang
- Xiaoquan Zhou
Key dates
| Filing date | Apr 20, 2022 |
| Grant date | Apr 16, 2024 |
| Priority date | — |
| Expiry date | Apr 20, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F40/205
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.