Personalized Content Summaries With Multimodal Context Feedback

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Solution Overview

Problem

Existing content summarization technologies lack context awareness, rely excessively on algorithms lacking human insight, struggle with integrating multiple data types, fail to adapt to changing user needs, and lack mechanisms for adaptive feedback, leading to stagnant performance.

Innovation Solution

A context-aware system integrating Generative AI with collaborative intelligence, employing an Intelligent Selection Process to generate personalized summaries by considering user profiles, social contexts, and adaptive feedback mechanisms, while seamlessly integrating various media types.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If traditional rule-based systems or machine learning algorithms are used for content summarization, then the summarization process can be automated, but the summaries lack context awareness and nuanced understanding

Engineering Contradiction:

Improveautomation of summarizationVSAvoidcontext awareness and nuanced understanding

Core Design Contradiction:

Extent of automationVSLoss of information

Solution Approach 1:

The patent combines multiple AI models (GPT-4 for generation, BERT for understanding) with collaborative filtering and human feedback mechanisms into an integrated system. This merging allows automated summarization to incorporate contextual awareness and nuanced understanding by synthesizing outputs from multiple specialized components rather than relying on a single algorithmic approach

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system implements feedback loops where user interactions, social context signals, and model outputs are continuously fed back to refine future summarizations. This allows the automated system to learn from and adapt to contextual patterns, improving its understanding over time while maintaining automation

Inventive Principle:

Principle #23Feedback

2Productivity

If algorithms are used to analyze and summarize content, then processing efficiency is improved, but the summaries lack human insight and collaborative intelligence

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidhuman insight and collaborative intelligence

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent introduces social context analysis and collaborative filtering components as intermediaries between raw content and final summaries. These intermediaries process social signals, user preferences, and community feedback to infuse human insight into the automated summarization process without significantly reducing processing efficiency

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system creates composite summaries by integrating outputs from multiple AI models (generative and analytical), collaborative filtering results, and human feedback. This composite approach combines the efficiency of algorithms with the depth of human insight from diverse sources

Inventive Principle:

Principle #40Composite materials

3Measurement precision

If personalization engines use extensive data collection to tailor content, then personalization accuracy is improved, but data privacy concerns increase

Engineering Contradiction:

Improvepersonalization accuracyVSAvoiddata privacy concerns

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The system applies different data processing approaches to different user contexts. Sensitive personal data is processed with higher privacy protection (local quality approach), while less sensitive contextual information is used for personalization. This allows accurate personalization where appropriate while minimizing privacy intrusion

Inventive Principle:

Principle #3Local quality

4Device complexity

If static personalization systems are used, then system simplicity is maintained, but the systems fail to adapt to changing user needs or social dynamics

Engineering Contradiction:

Improvesystem simplicityVSAvoidadaptation to changing user needs

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The system implements dynamic personalization where user profiles, social context weights, and summarization parameters automatically adjust based on changing user behavior, preferences, and social dynamics. This allows the system to adapt to evolving needs while maintaining a relatively simple underlying architecture through automated parameter adjustment

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20260044551A1Method and system for generating a personalized summary of content

Publication Date: 2026.02.12 PROPHETSTOR DATA SERVICES

AI summary

The present invention provides a method for generating a personalized summary of content and a system thereof. The method includes the steps of: storing a user profile of a user and/or a user-annotated content; retrieving multimodal contents from various online sources based on the user profile; storing the user-annotated content obtained from the user subsystem, and the multimodal contents obtained from the data retrieval module; selecting pertinent material from the multimodal contents and/or the user-annotated content; and autonomously generating a personalized content summary based on the selected pertinent material by a generative artificial intelligence (AI) module.