Chatbot Content Curation With Generative AI Feedback Loops

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Solution Overview

Problem

Existing social media platforms lack effective methods to curate and recommend content in a way that eliminates bias and enhances user engagement, while also providing personalized recommendations and feedback mechanisms.

Innovation Solution

Implementing a content curation system using generative AI to classify, summarize, and recommend content by topic through a chatbot, which collects user responses to refine future recommendations and provides reports to creators.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If content is recommended using traditional social media algorithms, then content delivery is provided, but bias in content recommendation persists and user engagement is not effectively enhanced

Engineering Contradiction:

Improvecontent recommendation personalizationVSAvoidcontent recommendation bias

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system implements a feedback mechanism where user responses to recommended content are collected and used to refine future recommendations. The chatbot engages users in conversation about the recommended content, gathering their opinions and preferences, which then feed back into the recommendation algorithm to improve personalization and reduce bias over time.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system changes the parameters of content recommendation by using generative AI to create summarized content and engage in natural conversation with users, rather than relying on traditional algorithmic sorting and filtering. This transforms the recommendation approach from static algorithmic selection to dynamic AI-generated content delivery with conversational feedback.

Inventive Principle:

Principle #35Parameter changes

2Ease of operation

If a chatbot is used for content curation, then user engagement is enhanced through conversation, but system complexity increases

Engineering Contradiction:

Improveuser interaction with contentVSAvoidcontent curation system

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The chatbot serves as an intermediary between the content curation system and the user. It handles the complex tasks of content generation, summarization, and recommendation conversation, shielding the user from system complexity while providing natural, easy-to-use interaction. The chatbot mediates between the AI content generation engine and user preferences.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Manufacturing precision

If generative AI is used to create recommended content, then content quality and personalization improve, but processing time and computational resources increase

Engineering Contradiction:

Improvecontent qualityVSAvoidcontent generation time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system extracts only the essential elements from original content to create concise summaries through generative AI, rather than generating entirely new content. This extraction approach maintains content quality and personalization while significantly reducing processing time and computational resources compared to full content generation.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20250260656A1Method, computer device, and non-transitory computer-readable recording medium to curate content on various topics using chatbot

Publication Date: 2025.08.14 LINE PLUS

AI summary

Disclosed are a method, a computer device, and a non-transitory computer-readable recording medium for curing content on various topics through a chatbot. A content curation method may include creating recommended content for at least one topic using original content produced on at least one platform; providing the recommended content to a user using a chatbot for content curation; collecting a user response to the recommended content provided by the user to the chatbot; and reflecting the user response in at least one of a user's personalization recommendation and a report related to the recommended content.