Machine learning-based automated narrative text scoring including emotion arc characterization
US12271694B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 23, 2021 |
| Grant date | Apr 8, 2025 |
| Priority date | — |
| Expiry date | Dec 30, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Quality of a narrative is characterized by receiving data that includes a narrative text. This narrative text is then tokenized and events are extracted from the tokenized words. The extraction can use, in parallel, two or more different extraction techniques. The extracted events are then extracted so that a waveform can be generated based on the aggregated extracted events that characterizes a plurality of emotional arcs within the narrative text. Subsequently, a plurality of waveform elements are extracted from the waveform. The narrative quality (or other quality) of the narrative text is then scored based on the extracted plurality of waveform elements and using a machine learning model trained to correlate emotional arc waveforms with narrative quality scores. Related apparatus, systems, techniques and articles are also described.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.