Content Optimizer for Predicting Audience Interest and Comprehension

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Solution Overview

Problem

Existing automated read and reply systems and content optimization techniques struggle to effectively predict human interest and comprehension in user content, often relying on statistical models that fail to understand the underlying content or audience engagement.

Innovation Solution

The Content Optimizer employs machine-learned relevancy and comprehension models to predict the level of interest and understanding of human audiences for segments of user content, and automatically generates suggested formatting changes to enhance engagement and comprehension.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If statistical or probabilistic modeling is used to generate automated replies, then the system can automatically respond to incoming emails or messages, but the system fails to truly understand human interest and comprehension in the content

Engineering Contradiction:

Improveautomated reply generationVSAvoidprediction of human interest and comprehension

Core Design Contradiction:

Extent of automationVSMeasurement precision

Solution Approach 1:

The patent replaces statistical/probabilistic modeling with machine learning models that can actually predict human interest and comprehension. The system uses trained machine learning models to analyze content segments and predict audience engagement, substituting the mechanical statistical approach with a more intelligent system that understands human cognitive patterns.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an intermediary layer between the automated reply system and the content analysis. This intermediary consists of machine learning models that serve as mediators to interpret content meaning and predict human interest, rather than directly using statistical correlations.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Ease of manufacture

If pixel-based image analysis is used to predict visual attention, then the system can analyze content without understanding underlying meaning, but the predictions fail to account for human comprehension of content

Engineering Contradiction:

Improvesimplicity of visual attention modelingVSAvoidaccuracy of human interest prediction

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent replaces pixel-based image analysis with machine learning-based content analysis. Instead of treating content as mere visual patterns, the system uses trained models to understand the semantic meaning and predict human interest based on comprehension, substituting the mechanical pixel analysis with intelligent content understanding.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automated formatting suggestions are generated based on statistical models, then the system can suggest corrections to text documents, but the suggestions fail to optimize for actual human audience engagement

Engineering Contradiction:

Improveautomated content optimizationVSAvoidprediction of audience interest levels

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent replaces statistical modeling with machine learning models that predict actual audience interest and comprehension. The system uses trained machine learning models to analyze content segments and provide formatting suggestions that are optimized for human engagement, rather than relying on statistical correlations.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent implements a feedback mechanism where the machine learning models continuously learn from audience responses and engagement data. The system uses this feedback to refine its predictions of human interest and comprehension, improving the accuracy of its content optimization suggestions over time.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12242936B2Automated comprehension and interest-based optimization of content

Publication Date: 2025.03.04 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

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.