Persona-Based AI Answer Generation With Source Attribution
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Solution Overview
Problem
Generative AI systems lack transparency in attributing content creators, leading to impersonal experiences and undervaluation of content creators, resulting in limited diversity and quality of responses.
Innovation Solution
An AI-based content generation system that identifies and attributes persona-based answers from expert users, providing diverse perspectives and incentivizing content creation through recognition and compensation mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative AI systems are trained on large datasets from multiple sources, then the system's ability to generate human-like text is improved, but the transparency and acknowledgment of content creators deteriorates
Solution Approach 1:
The patent segments the training data into distinct contributions from different content creators, allowing the system to track and attribute specific information back to its sources. This segmentation enables the AI to maintain generation capability while preserving attribution information that would otherwise be lost in aggregated datasets.
Solution Approach 2:
The system implements feedback mechanisms that allow content creators to provide corrections, updates, and contextual information about their contributions. This feedback loop ensures that attribution information remains accurate and up-to-date, preventing the loss of creator acknowledgment while maintaining the system's generative capabilities.
2Productivity
If the generative AI system focuses on generating standardized responses for efficiency, then the response generation speed is improved, but the diversity of perspectives and opinions deteriorates
Solution Approach 1:
The patent introduces dynamic response generation where the system adapts its behavior based on the specific query and available content creator contributions. Rather than using fixed standardized responses, the system dynamically selects and combines perspectives from different creators, maintaining diversity while achieving efficiency through automated selection processes.
Solution Approach 2:
The system applies local quality by tailoring responses to specific contexts and user needs, selecting different content creators and perspectives based on the particular query. This allows standardized efficiency in the selection process while maintaining local customization and diversity in the actual responses provided.
3Productivity
If content creators are treated as interchangeable components in training data, then the data processing efficiency is improved, but the recognition and compensation of creators deteriorates
Solution Approach 1:
The patent creates digital copies or identifiers for each content creator's contributions, allowing the system to process data efficiently while maintaining distinct recognition of each creator. These copies enable automated processing and attribution tracking, resolving the contradiction between processing efficiency and creator recognition.
Solution Approach 2:
The system introduces intermediary metadata structures that mediate between the raw content data and the creator identification system. This intermediary layer allows efficient data processing while preserving creator information, acting as a bridge that maintains both productivity and recognition capabilities.
4Adaptability or versatility
If the AI system provides mean answers aggregating multiple perspectives, then the response generality is improved, but the nuanced perspectives and depth of engagement deteriorates
Solution Approach 1:
The patent adds a dimensional layer to responses by including not only the aggregated mean answer but also individual perspective contributions from different content creators. This dimensional expansion allows users to access both general summaries and nuanced individual viewpoints, preventing the loss of perspective depth while maintaining response generality.
Data Source
AI summary
An AI-based content generation method and system for generating persona-based question-answers based on personas and diversity of answers, is disclosed. The AI-based content generation method includes: obtaining inputs from electronic devices of first users; identifying second users based on criteria associated with inputs, by an AI model; generating information associated with the second users based on criteria associated with the inputs; generating confidence scores for the information associated with the second users based on attributes of the second users using the AI model; ranking the second users based on the generated confidence scores using the AI model; generating personas associated with optimized second user based on ranking of the second users using the AI model; automatically generating subsequent questions for the first users; and providing an output of generated personas associated with the optimized second user, with the persona-based answers, and the subsequent questions, to the first users.