Explainable unsupervised vector representation of multi-section documents
US12190252B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 5, 2021 |
| Grant date | Jan 7, 2025 |
| Priority date | — |
| Expiry date | Aug 12, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/23
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating an inferred document representation for a multi-section document using a machine learning model. In accordance with one embodiment, a method is provided that includes: identifying a document corpus comprising the multi-section document and other multi-section documents; for each section of the document that is associated with a section type identifier: identifying a section batch that comprises common-type sections across the document corpus; and processing the section batch using the machine learning model to generate per-type section clusters for the section type identifier that comprise an inferred per-type section cluster for the current section; generating the inferred document representation based at least in part on each inferred per-type section cluster for a section of the document; and performing a prediction-based action based at least in part on the representation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.