Customized Multimedia Therapy Sessions With AI Speech Evaluation
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Solution Overview
Problem
Existing solutions for interactive multimedia content customization are inadequate for on-demand customization and lack objective evaluation of user performance, particularly in treating speech disorders, leading to inconsistent results and reduced patient engagement.
Innovation Solution
A method and system utilizing machine learning models, including prompt generation and language models, to generate customized questions and expected answers based on multimedia content transcripts, analyze user performance, and provide feedback for treating speech disorders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a person manually provides an interactive experience, then user engagement is improved, but labor cost and scalability worsen
Solution Approach 1:
The system enables self-service by using AI models to automatically generate questions, evaluate user responses, and provide feedback without human intervention. The speech disorder treatment system autonomously creates personalized therapy sessions based on user profile data and automatically assesses performance, eliminating the need for manual therapist involvement in each interaction while maintaining engagement.
Solution Approach 2:
The patent replaces the mechanical system of manual human interaction with an automated AI-based system. Language models generate questions and evaluate responses, while speech recognition and analysis algorithms assess user performance objectively. This substitution maintains the interactive experience while eliminating labor costs and improving scalability.
2Ease of manufacture
If predetermined interactive content is used, then implementation simplicity is improved, but customization capability worsens
Solution Approach 1:
The system transitions from static predetermined content to dynamic generated content. Questions are not fixed but generated in real-time based on user responses and performance. The difficulty level, question types, and feedback are dynamically adjusted according to the user's evolving skill level and treatment progress, maintaining simplicity while enabling full customization.
Solution Approach 2:
The system changes parameters such as question difficulty, topic selection, and interaction type based on user profile data and performance metrics. By dynamically adjusting these parameters, the system provides personalized therapy sessions for each user while using the same underlying AI models, achieving customization without increasing implementation complexity.
3Reliability
If manual evaluation of user performance is used, then feedback quality is improved, but objectivity and consistency worsen
Solution Approach 1:
The system implements automated feedback loops where user responses are immediately evaluated by AI models and speech analysis algorithms. Performance metrics are objectively measured and fed back to adjust subsequent questions and treatment plans. This automated feedback system ensures consistency and objectivity while maintaining high quality through continuous performance tracking and adaptation.
4Adaptability or versatility
If on-demand customization is implemented, then user preference matching is improved, but system complexity worsens
Solution Approach 1:
The system uses universal AI models that can handle multiple functions: generating questions, evaluating responses, analyzing speech patterns, and providing feedback. These multi-functional models serve all customization needs without requiring separate specialized systems, reducing overall complexity while enabling comprehensive on-demand personalization based on user preferences and treatment goals.
Data Source
AI summary
A system and method for providing customized interactive multimedia sessions using machine learning. A method includes obtaining a transcript for multimedia content. The transcript is analyzed using a machine learning architecture in order to generate questions and corresponding expected answers for the multimedia content. The questions are provided to a user device. Responses to the questions may be received and analyzed in order to analyze user performance. Some techniques described include methods for treating speech disorders using customized interactive multimedia sessions.