Smart Lens Memory Mapping via Biometric Attention Prediction
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Solution Overview
Problem
Smart contact lenses and augmented reality devices struggle to effectively capture and store video footage of a user's surroundings when the user is distracted or inattentive, leading to memory constraints and forgetfulness of important events.
Innovation Solution
A smart storage management mechanism is embedded in augmented reality devices, which uses machine learning and biometric parameters to predict user attentiveness, selectively record and store video footage, and prune data based on user preferences and behavior, employing LSTM-RNN models and PCA for efficient data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the smart contact lens continuously records video footage of the user's surroundings, then the memory retention of important events is improved, but the storage capacity is quickly exhausted and energy is wasted on recording unimportant moments
Solution Approach 1:
The system performs preliminary analysis of video data using machine learning models (LSTM-RNN and PCA) to identify and flag important events before storing them. This preliminary action distinguishes between significant moments requiring storage and routine moments that can be discarded, thereby improving memory retention of important events while conserving storage capacity.
2Reliability
If the smart contact lens records all video footage, then no important events are missed, but the device complexity and processing requirements increase significantly
Solution Approach 1:
The patent extracts and isolates only the critical components of video analysis using PCA (Principal Component Analysis) to identify key features that indicate important events. By taking out only the essential analysis functions rather than processing entire video streams, the system maintains event capture reliability while reducing processing complexity.
Solution Approach 2:
The system uses machine learning models (LSTM-RNN) that create simplified representations or copies of video data patterns to identify important events. Instead of analyzing every pixel and frame in detail, the models work with compressed feature representations, maintaining detection reliability while reducing computational complexity.
3Loss of information
If the smart contact lens uses machine learning models for attentiveness prediction, then the selective recording of important events is improved, but the energy consumption and computational load increase
Solution Approach 1:
The system applies partial action by using machine learning models selectively rather than continuously. The LSTM-RNN and PCA models are deployed to analyze attentiveness patterns and predict important events only when needed, rather than processing every moment. This partial application of computational resources improves information retention while managing energy consumption in smart contact lenses with limited power supply.
Data Source
AI summary
A method, computer system, and a computer program product for memory mapping is provided. The present invention may include identifying an augmented reality device and at least one Internet of Things (IoT) device which observes at least one biometric parameter. The present invention may include defining at least one user attention pattern based on the at least one biometric parameter. The present invention may include predicting an attentiveness of a user based on the at least one attention pattern. The present invention may include recording data from the augmented reality device, based on the attentiveness of the user dropping below a certain point. The present invention may include storing the recorded data.