Real-Time Content Adjustment via Sentiment Analysis

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Solution Overview

Problem

Existing methods fail to adapt natural language content in real-time based on viewer reactions, particularly in scenarios where a human presenter is unavailable or lacks the ability to adjust presentations dynamically to engage diverse audiences effectively.

Innovation Solution

A system utilizing sentiment analysis models to measure viewer reactions, generating content parameters, and combining these with content generation models to create adjusted content, such as animated or break content, to enhance engagement and adapt presentation formats dynamically.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a human presenter is used to adapt presentation content, then engagement with diverse audiences improves, but the system complexity and cost increase

Engineering Contradiction:

Improveadaptability to audience reactionsVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system enables automated content adaptation through sentiment analysis models and content generation models that operate without human intervention. The system measures viewer reactions, determines content generation parameters, and adjusts presentation content automatically, allowing the system to serve itself in the role traditionally filled by human presenters.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical system of human presenters with an automated computational system consisting of sentiment analysis models, content generation models, and parameter determination logic. This substitution eliminates the need for human physical presence while maintaining the adaptability function through algorithmic processing of audience feedback.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Device complexity

If automated content presentation is used, then system complexity decreases, but the ability to adapt to viewer reactions in real-time is lost

Engineering Contradiction:

Improvesystem complexityVSAvoidreal-time content adaptation

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The system implements a closed-loop feedback mechanism where viewer reactions are continuously measured using sentiment analysis models, the results are used to determine content generation parameters, and the presentation content is adjusted in real-time based on this feedback. This enables the automated system to respond dynamically to audience engagement levels.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system transitions from static pre-recorded content to dynamic adaptive content generation. The content generation models create or modify presentation materials in real-time based on current viewer reaction metrics, allowing the system to adapt its output dynamically rather than delivering fixed content.

Inventive Principle:

Principle #15Dynamics

3Productivity

If multiple content generation models are used to create varied content, then viewer engagement improves, but computational resources and processing time increase

Engineering Contradiction:

Improvecontent generation efficiencyVSAvoidcomputational resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system divides the content generation task into multiple specialized models, each responsible for generating specific types of content (e.g., text, images, videos). This segmentation allows parallel processing of different content types and enables selective activation of models based on the specific adaptation needs, optimizing resource utilization.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system adjusts content generation parameters based on sentiment analysis results to control the level and type of content modification. By dynamically changing parameters such as adaptation intensity, content type selection, and generation model activation, the system optimizes the balance between engagement improvement and computational resource consumption.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11770585B2Reaction based real time natural language content adjustment

Publication Date: 2023.09.26 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Using a sentiment analysis model, a reaction to a presentation of a first content is measured. Using the first content and the reaction, a content generation parameter is determined. Using a first content generation model and the content generation parameter, second content corresponding to the first content is generated. Using a second content generation model and the content generation parameter, third content corresponding to the first content is generated. The first content is adjusted by combining the first content with the second content and the third content, the adjusting resulting in an adjusted content.