Adaptive Reading Interface With Personalized Definition Support
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Solution Overview
Problem
Existing electronic reading environments disrupt user flow with embedded dictionary features that obscure text or redirect users, requiring active engagement for definitions and offering only generic assistance.
Innovation Solution
An apparatus and method for adaptive content generation using a processor, memory, and natural language processing (NLP) to analyze user data, generate a linguistic profile, and modify a graphical user interface to provide contextually relevant definitions without disrupting the reading flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If embedded dictionary features are used to provide definitions, then users receive assistance with unfamiliar terms, but the reading flow is disrupted and user experience deteriorates
Solution Approach 1:
The patent introduces an intermediary system (machine learning model with linguistic profiles) that mediates between the user and the dictionary features. Instead of direct interaction with pop-up windows, the system automatically analyzes the text, identifies unfamiliar terms based on the user's linguistic profile, and provides definitions in a non-intrusive manner, thus maintaining reading flow while providing necessary assistance.
Solution Approach 2:
The system enables self-service by automatically detecting when a user may need definition assistance based on their linguistic profile and the context of the text. The machine learning model proactively provides definitions without requiring the user to actively search for or request them, allowing users to continue reading uninterrupted while receiving targeted assistance.
2Adaptability or versatility
If generic dictionary definitions are provided, then all users receive the same information, but personalization and user-specific comprehension needs are not addressed
Solution Approach 1:
The patent applies local quality by creating personalized linguistic profiles for each user that capture their specific vocabulary knowledge, reading level, and comprehension needs. The system then uses these profiles to selectively provide definitions tailored to each user's requirements, rather than applying a uniform approach to all users. This allows the definition assistance to be locally optimized for each user's specific needs.
Solution Approach 2:
The linguistic profiles are dynamic and adapt over time based on user interactions and performance. The system continuously refines its understanding of each user's vocabulary and comprehension level, adjusting the provision of definitions dynamically. This allows the system to become increasingly personalized and effective over time, adapting to each user's evolving needs.
3Ease of operation
If users must actively identify words needing assistance, then user control is maintained, but cognitive load increases and reading efficiency decreases
Solution Approach 1:
The system performs self-service by automatically identifying which words or phrases may need definition assistance based on the user's linguistic profile and the text context. This eliminates the need for users to actively scan and identify unfamiliar terms, significantly reducing cognitive load and time spent on comprehension tasks while maintaining the ability to control which definitions are provided.
Solution Approach 2:
The system performs preliminary action by pre-analyzing the text and identifying potential unfamiliar terms before the user encounters them. The machine learning model prepares personalized definition recommendations based on the user's linguistic profile, so that when a user does need assistance, the relevant definitions are already prepared and can be provided immediately without delay.
Data Source
AI summary
An apparatus for adaptive content generation includes a processor configured to receive input data, retrieve a user associated data file as a function of the input data, generate a linguistic profile as a function of the user associated data file and the at least one learning task by extracting one or more linguistic identifiers within the user associated data file, modify, using a natural language processing (NLP) model, the user associated data file to create a modified user associated data file, modify a graphical user interface comprising one or more display elements associated with the modified user associated data file, and transmit the graphical user interface to the remote device.