Real-Time Sensor AI for Care Data Capture and PII Redaction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for capturing and managing care data in human service agencies are prone to human error, inaccurate documentation, and lack of individual-specific insights, while also failing to protect personal information privacy.
Innovation Solution
A system that uses machine learning to automatically capture sensor data, make data-driven predictions, and redact personally identifiable information, ensuring privacy and context-aware care delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data entry and documentation methods are used, then system simplicity is maintained, but data accuracy and reliability deteriorate due to human errors and inaccurate documentation
Solution Approach 1:
The patent replaces manual mechanical data entry processes with automated machine learning systems that process sensor data automatically. The ML model analyzes sensor inputs (video, audio, biometric data) and generates care data without human intervention, eliminating transcription errors and improving data accuracy while maintaining system simplicity from the user perspective
Solution Approach 2:
The system performs self-service by automatically capturing, analyzing, and documenting care data through machine learning algorithms. The ML model independently processes sensor data, makes predictions about care needs, and generates documentation without requiring manual input from caregivers, thereby improving reliability while reducing operational complexity
2Measurement precision
If more detailed individual information is collected to improve care quality, then care precision improves, but privacy protection challenges worsen
Solution Approach 1:
The patent extracts and processes only the necessary information from sensor data through machine learning algorithms. The system analyzes sensor inputs to generate care data while automatically filtering out personally identifiable information (PII) and sensitive details, retaining only the essential care-relevant insights. This extraction approach enables precise individual-specific care data collection while removing privacy risks associated with storing detailed personal information
Solution Approach 2:
The machine learning model serves as an intermediary between sensor data collection and care data storage. The ML system processes raw sensor data, extracts care-relevant information, and generates anonymized care data that lacks PII. This intermediary function enables precise measurement of individual care needs while protecting privacy by preventing direct storage of sensitive personal information
3Speed
If real-time sensor data is processed to enable immediate care decisions, then response speed improves, but data processing complexity worsens
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on historical care data and sensor patterns before actual use. The ML models are pre-configured with care protocols and decision trees, enabling them to process real-time sensor data instantly without requiring complex real-time analysis. This preliminary preparation allows rapid response while reducing the complexity of real-time processing
Data Source
AI summary
The present invention relates to computer security methods and systems, employing artificial intelligence and machine learning, for recording information in an electronic format relating to the individual under care, from a sensor, identifying the individual under care from the information, and redacting information associated with the individual under care before transmitting to the user.