Contextual Memory Trainer for Cognitive Assistance
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Solution Overview
Problem
Existing memory aid devices for patients with impaired memory due to conditions like Alzheimer's and Parkinson's often rely on recreating environmental and physiological data to trigger recollections, which can be emotionally taxing and may trigger adverse responses, while solely relying on facial recognition is mentally demanding and delays memory recall.
Innovation Solution
A system that includes an input data receiver, a contextual memory trainer, and a cognition analyzer to capture and preprocess video and audio data, prioritize and annotate unknown aspects based on spatiotemporal data, and train temporal models to provide contextual outputs for mental memory assistance, reducing training time and enhancing the relevance of memory recall.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If environmental and physiological data are recreated to trigger recollections, then memory recall is assisted, but emotional distress and adverse responses are triggered
Solution Approach 1:
The patent extracts only the essential and beneficial elements from past experiences (positive emotions, key memories) while excluding harmful elements (negative emotions, traumatic events). This selective extraction allows the system to trigger recollections without reproducing the full emotional context that could cause distress.
Solution Approach 2:
The system applies different processing qualities to different types of data: positive memories are preserved and enhanced, neutral memories are maintained as-is, and negative memories are filtered or modified. This local quality approach ensures that only beneficial emotional content is used to assist recall.
2Measurement precision
If facial recognition is used to prompt patient recall, then person identification is achieved, but mental burden increases and recall time is delayed
Solution Approach 1:
The system merges multiple types of information (facial recognition results, stored memory data, contextual information) to create a comprehensive recall prompt. This combination provides richer context that accelerates recall while maintaining identification accuracy.
Solution Approach 2:
The system performs preliminary processing of facial recognition data and preps recall prompts in advance. By preparing contextual information beforehand, the system reduces the time required for actual recall during live interactions.
3Reliability
If comprehensive data annotation is performed for memory training, then recall relevance is improved, but training time increases
Solution Approach 1:
The system performs annotation selectively on the most critical and frequently accessed memory elements rather than comprehensively annotating all data. This partial action approach maintains high recall relevance for important memories while significantly reducing overall training time.
Solution Approach 2:
The system employs automated algorithms and AI models to perform data annotation independently, reducing the need for manual annotation efforts. This self-service capability maintains annotation quality while minimizing the time investment required.
Data Source
AI summary
A system for providing cognition assistance including a contextual memory trainer, which receives preprocessed data including facial data, scene data, and activity data related to a video in association with temporal data and geographical location data of the camera that captured the video, where the scene data, the activity data, the geographical location data, and the temporal data collectively define spatiotemporal data. The trainer identifies an unknown aspect in the preprocessed data based on historical data and determines a predefined priority factor therefor. The priority factor includes one of a frequency of occurrence within a set period and relative proximity of the unknown aspect to the camera, a known face, place, or scene. The unknown aspect is prioritized for annotation based on a value of the priority factor exceeding a predefined threshold value, based on which facial data is associated with the spatiotemporal data to provide contextual annotated data.