Medical Apparatus Speech Recognition Adaptation
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Solution Overview
Problem
Medical devices, such as injection devices for diabetes management, often have limited speech recognition capabilities, leading to frustration and low intuitivity for users due to restricted recognizable instructions, which can result in inaccurate interactions and discomfort, especially for inexperienced or older patients.
Innovation Solution
A medical apparatus with a processor that performs interaction processes and determines operable instructions based on user input, using operational data and modification information to improve speech recognition accuracy and adapt to user-specific preferences, allowing for flexible and intuitive user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If speech recognition is limited to a defined set of recognizable instructions, then the device complexity is reduced, but the ease of operation deteriorates due to low intuitivity and user frustration
Solution Approach 1:
The speech recognition system transitions from a static, predefined instruction set to a dynamic system that continuously learns and adapts to user speech patterns. The processor analyzes operational data from each interaction and modifies the recognition model accordingly, allowing the system to evolve and improve its understanding of user commands over time without requiring manual reconfiguration.
Solution Approach 2:
The system performs self-improvement by automatically analyzing operational data and modifying its own speech recognition capabilities. The processor uses feedback from user interactions to update the operational data storage, enabling the system to learn from actual usage patterns and enhance its speech recognition accuracy without external intervention or reprogramming.
2Manufacturing precision
If a predefined set of speech instructions is used, then the manufacturing precision of the recognition system is improved, but the adaptability deteriorates when users provide variations or deviations from standard instructions
Solution Approach 1:
The system changes its recognition parameters dynamically based on operational data. Instead of using fixed thresholds and predefined patterns, the processor adjusts the speech recognition parameters by analyzing actual user input variations, allowing it to adapt to different speaking styles, accents, and phrasings while maintaining accurate instruction recognition.
Solution Approach 2:
The system incorporates feedback loops where operational data from each speech interaction is analyzed and used to modify future recognition attempts. The processor receives feedback from successful or unsuccessful recognitions and uses this information to refine its speech patterns, improving its ability to accurately recognize varied user instructions over time.
3Ease of manufacture
If speech recognition is restricted to standard instructions, then the ease of manufacture is improved, but the reliability deteriorates due to low recognition accuracy for non-standard speech
Solution Approach 1:
The system performs preliminary analysis of operational data during each interaction to prepare modification information before the next speech recognition attempt. By continuously processing and storing operational data in advance, the system builds a foundation of learned patterns that improves the reliability of subsequent speech recognition without requiring complex manufacturing processes.
Data Source
AI summary
It is inter alia disclosed to perform at least one of operating an interaction process with a user of the medical apparatus and determining, based on a representation of at least one instruction given by the user, at least one instruction operable by the medical apparatus. Therein, the at least one of the operating and the determining at least partially depends on operational data. It is further disclosed to receive modification information for modifying at least a part of the operational data, wherein the modification information is at least partially determined based on an analysis of a representation of at least one instruction given by the user.