Hearing Aid Automatic Self-Modification Algorithm
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Solution Overview
Problem
Current hearing aids require frequent adjustments by an acoustician and have high memory requirements for storing user data, leading to inefficient optimization and potential incorrect learning of user preferences, often focusing on negative listening situations rather than overall user needs.
Innovation Solution
A method that records current user settings at predetermined intervals, combining them with existing settings using a specific algorithm to generate new basic settings, reducing memory needs by storing only the effect of user inputs as updated values for sound classes, allowing for automatic adaptation to individual needs without extensive data logging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user settings are recorded only at user input events, then learning occurs only for problematic situations, but correct settings for common situations are not recorded and may be incorrectly adjusted
Solution Approach 1:
The patent implements periodic recording of user settings at predetermined time intervals regardless of whether changes occur. This periodic action ensures that both problematic situations (when users adjust settings) and common correct situations (when users leave settings unchanged) are captured in the learning database, preventing loss of valuable correct setting information while maintaining learning accuracy.
2Measurement precision
If detailed user input data and sound environment descriptions are stored for every situation, then learning precision improves, but memory requirements become excessively high
Solution Approach 1:
The patent extracts and stores only the essential elements needed for learning - specifically the user setting values and their temporal patterns - rather than storing complete detailed sound environment descriptions for every situation. This extraction approach maintains learning precision by capturing the critical setting information while dramatically reducing memory requirements by eliminating redundant environmental detail storage.
Solution Approach 2:
Instead of storing sound environment data and deriving settings from it, the patent inverts the approach by directly recording the user settings themselves as the primary learning data. This inversion simplifies the data structure and reduces memory needs while preserving the essential learning information about user preferences.
3Ease of operation
If hearing aid settings are manually adjusted by audiologist, then initial fitting is performed, but frequent re-visits are required for optimization
Solution Approach 1:
The patent implements self-service functionality where the hearing aid automatically learns from user settings and autonomously generates optimized fitting parameters without requiring audiologist intervention. The device performs its own fitting optimization by analyzing recorded user preferences and automatically applying learned settings, eliminating the need for frequent re-visits while maintaining ease of operation.
Solution Approach 2:
The patent establishes a feedback loop where user settings are continuously recorded, analyzed, and used to generate optimized parameters that are then applied back to the hearing aid. This closed-loop feedback system enables automatic continuous optimization of the fitting, replacing the manual iterative process of repeated audiologist visits with an automated self-improving system.
4Speed
If modifications are applied immediately upon user input, then responsiveness is improved, but random or incorrect inputs may negatively affect learning
Solution Approach 1:
The patent implements preliminary action by recording all user settings in advance during a learning phase before applying any modifications. User inputs are accumulated and stored as learning data during predetermined time intervals, and only after sufficient data collection does the system generate and apply optimized settings. This preliminary recording approach filters out random or incorrect inputs through statistical analysis, ensuring only meaningful patterns trigger actual modifications, thus maintaining both responsiveness and learning quality.
Data Source
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Figure 3
AI summary
The method involves detecting current normal user settings existing in a hearing aid in time referenced intervals. The base settings existing in or at the hearing aid are combined and/or linked based on a given algorithm in order to determine new base settings. The sound specific modified new base settings are stored in a memory (9) at the hearing aid, where the old base settings are cleared from the memory if necessary. Independent claims are also included for the following: (1) a hearing aid for implementing a method for modifying and/or adjusting hearing aid base settings such as volume and depth/height balance (2) a method for influencing an algorithm for combination and/or linking of base settings existing in or at the hearing aid.