Hearing Device Fitting Agent With User Feedback And Environment Models
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Solution Overview
Problem
Traditional hearing device fitting methods fail to account for user preferences and environmental factors, leading to a tedious and inefficient process.
Innovation Solution
A fitting agent that initializes user and environment models, obtains data to determine settings, presents test settings to users, and updates models based on user feedback to optimize hearing device parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional rule-based fitting methods (NAL-NL1 or NAL-NL2) are used, then hearing device parameters can be configured based on audiograms, but user preferences and environmental factors are not taken into account
Solution Approach 1:
The patent implements feedback loops where user responses to presented settings are collected and used to update the user model. The system continuously adapts by incorporating user feedback, allowing the hearing device parameters to be tuned according to actual user preferences and environmental conditions rather than relying solely on predetermined rules.
Solution Approach 2:
The system dynamically changes parameters of the user model and environment model based on collected data and feedback. By updating model parameters iteratively, the system adapts to individual user preferences and environmental variations, transforming a static rule-based approach into a dynamic adaptive system.
2Adaptability or versatility
If user preference learning is implemented, then personalized fitting can be achieved, but memory requirements increase due to storing historical user feedback
Solution Approach 1:
The patent applies local quality by maintaining different models for different environments (user model and environment model). Instead of storing all historical feedback uniformly, the system organizes data locally according to environment types, allowing personalized adaptation while managing memory usage through structured organization of preference data.
3Measurement precision
If comprehensive user feedback collection is performed, then accurate preference learning can be achieved, but the fitting process becomes more tedious and time-consuming
Solution Approach 1:
The system performs preliminary actions by initializing user models and environment models before the actual fitting process. By preparing the computational framework in advance and organizing the feedback collection process, the system reduces the time required during actual fitting while maintaining measurement accuracy through structured data collection.
Solution Approach 2:
The patent implements dynamic adaptation where the system learns and updates user preferences continuously during normal operation rather than requiring extensive upfront calibration. The fitting process becomes an ongoing dynamic adjustment based on real-time feedback, reducing initial time investment while maintaining precision.
Data Source
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AI summary
A fitting agent for a hearing device system comprising a hearing device worn by a hearing device user is disclosed, wherein the fitting agent comprises one or more processors configured to initialize a user model; initialize an environment model; obtain environment data indicative of a present environment; determine, using the environment model, an environment state based on the environment data; obtain a test setting comprising a primary test setting and a secondary test setting for the hearing device based on the environment state; present the test setting to the hearing device user; obtain a user input of a preferred test setting indicative of a preference for either the primary test setting or the secondary test setting; update the user model for provision of an updated user model based on the preferred test setting and the environment state; and update the environment model for provision of an updated environment model based on the preferred test setting and one of the user model and the updated user model.