Smart Contact Lens Video Capture Automation
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Solution Overview
Problem
Current video recording devices rely on manual settings and lack the ability to automatically determine when to start and stop recording based on contextual information, leading to inefficient and potentially inappropriate capture of video content.
Innovation Solution
A smart contact lens equipped with sensors and a camera that uses machine learning and contextual identifiers to automatically commence and stop recording, classifying video content and determining appropriate storage locations based on user interest, location, and predefined or learned rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual video recording settings are used, then user control over recording is maintained, but the system cannot automatically determine when to start and stop recording based on contextual information
Solution Approach 1:
The contact lens system performs self-service by automatically detecting contextual identifiers (such as faces, objects, locations) and autonomously determining when to start and stop video recording without requiring manual user intervention. The system uses machine learning algorithms to analyze sensor data and make recording decisions independently.
Solution Approach 2:
The patent replaces manual mechanical control (physical buttons, switches) with automated electronic systems including sensors, processors, and machine learning algorithms that detect contextual information and automatically control recording operations based on detected scenarios.
2Reliability
If continuous video recording is performed, then all events are captured, but storage space is wasted and privacy concerns arise
Solution Approach 1:
Instead of continuous recording, the system applies partial action by recording only during specific periods when contextual identifiers indicate relevant events are occurring. The machine learning model determines optimal recording windows based on detected patterns, capturing necessary events while avoiding unnecessary storage consumption.
Solution Approach 2:
The system uses feedback from sensor data analysis to dynamically control recording operations. The machine learning algorithm continuously monitors contextual information and provides feedback signals that trigger recording start/stop commands, ensuring recording occurs only when relevant events are detected.
3Productivity
If automated contextual detection is implemented, then recording efficiency is improved, but processing requirements and computational load increase
Solution Approach 1:
The system performs preliminary action by pre-processing sensor data and pre-training machine learning models to enable efficient real-time contextual detection. Contextual identifiers and detection rules are established in advance, allowing the system to quickly evaluate incoming data without intensive real-time computation.
Solution Approach 2:
The patent introduces an intermediary layer (machine learning model) that sits between raw sensor data and recording control decisions. This intermediary processes and simplifies complex sensor inputs into meaningful contextual identifiers, reducing the computational burden on the main processing system.
4Adaptability or versatility
If video streams are classified into categories, then storage organization is improved, but additional processing time is required
Solution Approach 1:
The system applies local quality by classifying video streams into different categories (e.g., personal, professional, sensitive) based on their specific contextual characteristics. Each category receives appropriate storage handling and access controls, with classification decisions made based on local analysis of detected contextual identifiers rather than uniform processing of all streams.
Data Source
AI summary
Embodiments of the present invention provide a method, computer program product and system for dynamic video capture through a contact lens based on dynamic contextual identification. Initially, a set of identifying information and a video stream from a contact lens is received. A determination is made as to whether to capture the video stream, based on the received information. The determining to capture the video stream is based on, at least one of, the user interest level exceeding a threshold and detecting a contextual identifier within the received the video stream from a contact lens. Responsive to determining to capture the video stream, the video stream is classified into a category and saving based on the classification category of the video stream.