US Patent:
20180357562, Dec 13, 2018
Inventors:
- Redmond WA, US
Jacob M. HOFMAN - New York NY, US
Assignee:
Microsoft Technology Licensing, LLC - Redmond WA
International Classification:
G06N 99/00
G06N 5/02
Abstract:
A “Content Optimizer” applies a machine-learned relevancy model to predict levels of interest for segments of arbitrary content. Arbitrary content includes, but is not limited to, any combination of documents including text, charts, images, speech, etc. Various automated reports and suggestions for “reformatting” segments to modify the predicted levels of interest may then be presented. Similarly, the Content Optimizer applies a machine-learned comprehension model to predict what a human audience is likely to understand (e.g., a “comprehension prediction”) from the arbitrary content. Various automated reports and suggestions for “reformatting” segments to modify the comprehension prediction may then be presented. In either case, user-selectable suggested “formatting” changes, if applied to corresponding content segments, are designed to modify either or both the predicted level of interest of one or more of the segments by either increasing or decreasing those predicted levels of interest, and the comprehension prediction relating to the arbitrary content.
