Machine learning-powered framework to transform overloaded text documents
US11423207B1 · kind B1 · utility
Assignee
Inventor
Key dates
| Filing date | Jun 23, 2021 |
| Grant date | Aug 23, 2022 |
| Priority date | — |
| Expiry date | Jun 23, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V30/422
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for providing a machine learning-powered framework to transform overloaded text documents is provided. The system generates a plurality of candidate templates offline. During runtime, the system accesses a text document and analyzes the text document to identify segmentation data. The segmentation data can indicate a plurality of segments derived from the text document. The system then accesses a plurality of candidate templates, whereby each candidate template comprises a plurality of pages having a different background element that shares a common theme. The plurality of candidate templates are ranked based on at least the segmentation data. The network then generates multiple presentation pages for each of a predetermined number of top ranked candidate templates by incorporating each of the plurality of segments into a corresponding page of the plurality of pages for each of the top ranked candidate templates. The multiple presentation pages are presented for each of the top ranked candidate templates as a recommendation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.