Hearing Device Fitting Agent with Environment Model

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Solution Overview

Problem

Current methods for fitting and tuning hearing device parameters are cumbersome and do not adequately account for user preferences, especially in varying environments.

Innovation Solution

A fitting agent that initializes user and environment models, allowing for the determination of optimal hearing device settings based on user preferences and environmental conditions, thereby eliminating the need for extensive user feedback and historical data storage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional fitting methods using audiograms and compensation rules are used, then the fitting process is standardized, but user preferences and environmental factors are not taken into account

Engineering Contradiction:

Improveadaptability to user preferencesVSAvoidcomplexity of fitting process

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system enables self-service by allowing the hearing device to automatically learn and adapt to user preferences through continuous feedback from usage data and environmental sensors, eliminating the need for manual professional fitting adjustments while maintaining high adaptability to individual user preferences

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements continuous feedback loops where user interactions, usage patterns, and environmental data are constantly monitored and fed back to the machine learning model, which automatically updates hearing device parameters to optimize performance according to evolving user preferences without requiring professional intervention

Inventive Principle:

Principle #23Feedback

2Measurement precision

If extensive historical user feedback is stored to improve model accuracy, then user preference modeling is more precise, but memory requirements increase

Engineering Contradiction:

Improveprecision of user preference modelingVSAvoidmemory storage requirements

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The system extracts only the essential features and patterns from user feedback data that are necessary for model updating, storing only compressed representations of preference patterns rather than raw historical data, thereby maintaining modeling precision while minimizing memory storage requirements

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system transforms extensive historical feedback data into condensed parameter representations that capture the essence of user preferences in a compact form, allowing the model to learn from accumulated experience without proportionally increasing storage requirements through efficient parameter encoding

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If manual fitting and tuning by healthcare professionals is performed, then personalized user preferences can be addressed, but the process is time-consuming and tedious

Engineering Contradiction:

Improveease of parameter configurationVSAvoidtime required for fitting process

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements continuous learning and adaptation that operates continuously in the background without interrupting normal device usage, allowing preference modeling to accumulate and improve over time without requiring dedicated fitting sessions or professional intervention

Inventive Principle:

Principle #20Continuity of useful action

4Adaptability or versatility

If environment-specific user preference functions are implemented, then listening experience is optimized for different environments, but model complexity increases

Engineering Contradiction:

Improveadaptability to different environmentsVSAvoidcomplexity of preference functions

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the environment space into distinct categories (e.g., quiet, noisy, outdoor, indoor) and learns separate preference functions for each segment, allowing the model to capture environment-specific user preferences without creating an intractably complex monolithic model by dividing the problem into manageable segments

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20250088812A1Fitting agent for a hearing device with environment model

Publication Date: 2025.03.13 GN HEARING AS

AI summary

A fitting agent for a hearing device system comprising a hearing device includes one or more processors configured to initialize a user model and an environment model, the user model comprising a plurality of user preference functions and associated user response distribution; obtain environment data; determine a first initial environment probability of a first environment and a second initial environment probability of a second environment based on the environment data and the environment model, obtain a test setting comprising a primary test setting and a secondary test setting for the hearing device based on the first initial environment probability and the second initial environment probability; provide the test setting; obtain a user input of a preferred test setting indicative of a preference for either the primary test setting or the secondary test setting; and update the user model based on the preferred test setting and the environment data.