Hearing Aid Self-Adjustment Algorithm for Memory-Constrained Adaptation
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Solution Overview
Problem
Current hearing aid settings are inefficiently adjusted and validated, requiring frequent visits to an acoustician, with significant memory space needed for data storage and complex algorithms, often resulting in suboptimal learning and incorrect modifications that compromise correct operation in most auditory situations.
Innovation Solution
A method for self-adjustment of hearing aid settings using a predefined algorithm that registers current user-selected settings at specific intervals, combining them with existing settings to generate new basic settings, minimizing memory requirements and allowing automatic adaptation to individual needs without the need for extensive user input storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user inputs and settings are stored extensively for learning and optimization, then the hearing aid can adapt to individual preferences, but memory space requirements increase significantly
Solution Approach 1:
The patent extracts only the essential elements needed for adaptation: current user-selected settings and basic hearing aid settings. By taking out only what is necessary rather than storing all possible data, the system achieves individualization without excessive memory requirements.
Solution Approach 2:
The system performs preliminary combination of current user settings with basic settings using a predefined algorithm to generate new basic settings before they are needed. This pre-processing allows the hearing aid to be ready for adaptation without requiring extensive real-time computation or storage of historical data.
2Adaptability or versatility
If complex algorithms are used for data management and setting optimization, then adaptation quality improves, but device complexity increases
Solution Approach 1:
The hearing aid system serves itself by automatically combining current user settings with basic settings using a predefined algorithm. This self-service approach allows the device to adapt to individual preferences without requiring complex external processing or manual intervention, thereby reducing overall system complexity while maintaining optimization quality.
3Adaptability or versatility
If manual adjustments by users are captured and stored for learning, then the hearing aid can learn user preferences, but the learning process becomes slower and less responsive
Solution Approach 1:
The system continuously combines current user-selected settings with basic settings to generate updated basic settings. This continuous process ensures that the hearing aid learns user preferences in real-time as they occur, rather than processing data in batches or requiring extensive accumulation of user inputs, thereby maintaining fast learning speed while achieving comprehensive preference adaptation.
Data Source
AI summary
For modifying or adapting at least one basic hearing aid setting such as volume, low/high frequency balance etc., it is suggested that at particular time intervals at least one current user-selected setting entered on the hearing aid is registered and combined or linked with the existing basic setting in or on the hearing aid by means of a predefined algorithm in order to arrive at a new basic setting and to store same in or on the hearing aid. The current user setting is acquired for instance in sound-specific fashion, i.e. with reference to a specific registered sound category or at least one hearing-related ambient parameter, and is linked with the corresponding, associated sound-specific basic setting in order to arrive at the new sound-specific basic setting.