Presentation Generation Tuned to Audience Expertise and Length
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Solution Overview
Problem
Conventional document processing models fail to consider the expertise level of the target audience and output length when generating presentation documents, resulting in a lack of control over content creation and decreased user experience.
Innovation Solution
A document processing apparatus that utilizes a language generation model trained with reinforcement learning and a reward function to generate topic descriptions based on user characteristics, such as expertise level and topic length preference, and a clustering model to align content with user needs, ensuring personalized and accurate document generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional document processing models are used to generate presentation documents, then the generation process is simple and fast, but the models fail to consider user characteristics resulting in poor adaptability and decreased user experience
Solution Approach 1:
The patent applies dynamics by making the document generation process adaptive and flexible through reinforcement learning. The language generation model dynamically adjusts its output based on user characteristics (expertise level, topic length preference) that are fed into the reward function. This allows the system to evolve from a static conventional model to a dynamic one that continuously optimizes presentation generation based on user feedback and preferences.
Solution Approach 2:
The patent changes key parameters of the generation process by introducing user characteristics as input variables. The expertise level and topic length preference parameters are incorporated into the reward function, allowing the model to adjust its generation behavior. This parameter change enables the system to produce differentiated outputs tailored to different user needs while maintaining a unified model architecture.
2Measurement precision
If reinforcement learning with reward function is used to train the language generation model, then the generation accuracy and user experience are improved, but the training complexity and computational resources increase
Solution Approach 1:
The patent implements feedback through the reward function that guides the reinforcement learning process. The reward function incorporates user characteristics (expertise level, topic length preference) and provides feedback signals to the language generation model during training. This feedback mechanism enables the model to learn optimal generation strategies that align with user preferences, improving generation accuracy while maintaining tractable training through structured reward design.
3Adaptability or versatility
If the language generation model is trained to consider user characteristics, then the content personalization is improved, but the control over content creation becomes more complex
Solution Approach 1:
The patent applies universality by designing a unified language generation model that handles multiple user characteristics simultaneously. The single model architecture can process different expertise levels, topic length preferences, and document types through the same reinforcement learning framework. This multi-functional approach allows content personalization across various dimensions without requiring separate specialized models for each user characteristic.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for natural language processing include obtaining a source document and a user characteristic that indicates a complexity preference of a user. A topic description is generated, using a language generation model, based on the source document and the user characteristic. The language generation model is trained based on an objective function that measures a complexity of the topic description.