Voice Interface Memory Impairment Detection via Utterance Pattern Analysis
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Solution Overview
Problem
Current voice-based computer-human interaction systems using AI struggle to accurately distinguish between routine forgetfulness and memory impairment, which is a long-term or permanent decline in an individual's ability to store and retrieve information due to diseases or conditions like Alzheimer's, making it difficult to provide appropriate assistance or interventions.
Innovation Solution
A system that captures human utterances using a voice interface, generates a corpus of utterances based on natural language processing, recognizes patterns using machine learning models, and contextual information from IoT devices to identify changes in memory functioning, classifying whether these changes are due to memory impairment or temporary conditions, and generates ameliorative action strategies to assist users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If voice-based interaction systems use AI to monitor user memory, then the ability to detect memory changes is improved, but the difficulty of distinguishing between routine forgetfulness and memory impairment increases
Solution Approach 1:
The system segments the analysis by creating separate processing pathways: one for detecting memory changes through pattern recognition in utterance corpora, and another for classifying the nature of these changes using contextual information from multiple sources. This segmentation allows the system to handle the complexity of differentiation by breaking it into manageable analytical stages.
Solution Approach 2:
The system introduces contextual information as an intermediary element that mediates between raw utterance data and final classification. By incorporating contextual data from sensing devices and analyzing patterns in the corpus separately before integration, the system creates an intermediate analytical layer that helps distinguish between routine forgetfulness and memory impairment more accurately.
2Measurement precision
If the system analyzes comprehensive contextual information from multiple sources, then the accuracy of classifying memory impairment is improved, but the device complexity increases
Solution Approach 1:
The voice-based interaction system performs multiple functions: it serves as both a natural language processing engine for routine user interactions and a memory monitoring system for health assessment. By making the system universal, it can handle diverse input types (utterances, sensor data, contextual information) and perform multiple analytical tasks without requiring separate dedicated systems for each function.
Solution Approach 2:
The system merges multiple information sources (utterance corpora, contextual data from sensing devices, pattern recognition results) into a unified analysis framework. By combining these diverse data streams and processing them through integrated machine learning models, the system achieves accurate memory impairment classification while managing complexity through unified architecture rather than separate systems.
3Reliability
If the system generates targeted ameliorative actions based on detailed analysis, then the effectiveness of assistance is improved, but the loss of time for processing and response generation increases
Solution Approach 1:
The system performs preliminary analysis by continuously building and analyzing the utterance corpus in the background, recognizing patterns and preparing classification models before memory impairment events occur. This preliminary action allows the system to have pre-processed information ready, reducing the time needed to generate effective ameliorative actions when actually needed.
Solution Approach 2:
The system implements feedback mechanisms where the results of memory impairment classification and ameliorative action effectiveness are fed back into the pattern recognition models. This continuous feedback loop allows the system to learn from past interactions and improve its response generation speed and accuracy over time, reducing processing time while maintaining or improving assistance effectiveness.
Data Source
AI summary
Memory deterioration detection and evaluation includes capturing human utterances with a voice interface and generating, for a user, a human utterances corpus that comprises human utterances selected from the plurality of human utterances based on meanings of the human utterances as determined by natural language processing by a computer processor. Based on data generated in response to signals sensed by one or more sensing devices operatively coupled with the computer processor, contextual information corresponding to one or more human utterances of the corpus is determined. Patterns among the corpus of human utterances are recognized based on pattern recognition performed by the computer processor using one or more machine learning models. Based on the pattern recognition a change in memory functioning of the user is identified. The identified change is classified, based on the contextual information, as to whether the change is likely due to memory impairment of the user.