Mobile Cognitive Analytics for Private Wearable Data Processing
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Solution Overview
Problem
Wearable health devices have limited sensors and data analysis capabilities, and transmitting user data to cloud servers exposes privacy risks and security vulnerabilities.
Innovation Solution
An adaptive cognitive analytics platform installed on a user's mobile device analyzes data from multiple sensors within a personal area network, allowing users to select analytics programs, and uses named data networking to secure and prioritize data transmission based on network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user data is transmitted to cloud servers for analysis, then data analysis capability is improved, but data security and privacy are compromised
Solution Approach 1:
Instead of transmitting data to the cloud for analysis, the patent inverts the approach by keeping data local on the user's device and transmitting only the analytics application code to the cloud. The actual data processing occurs locally through webAssembly technology, eliminating the security risk of data transmission while maintaining cloud-based analytical capabilities.
Solution Approach 2:
The patent introduces webAssembly as an intermediary layer between the cloud server and the user's device. The analytics code is compiled to webAssembly and executed locally on the device, acting as a secure mediator that enables cloud-based analytics functionality without exposing sensitive user data to external servers.
2Adaptability or versatility
If multiple sensors are added to wearable devices, then health analysis complexity is improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent creates a universal analytics platform that can handle multiple sensor types and health analysis scenarios through a single configurable system. The downloaded analytics applications provide multi-functionality, allowing the same device to perform various health analyses (sleep tracking, activity monitoring, heart rate analysis) without requiring dedicated hardware for each function.
Solution Approach 2:
The system dynamically adapts to different health analysis needs by downloading and executing different analytics applications based on user requirements. The analytics platform can be reconfigured on-demand to analyze data from different sensor combinations, providing dynamic versatility without permanent hardware complexity.
3Adaptability or versatility
If multiple sensors are added to wearable devices, then data stream diversity is improved, but bandwidth requirements and memory storage increase
Solution Approach 1:
The patent segments the data processing function from data storage. By executing analytics locally on the device through webAssembly, the system processes diverse sensor data streams locally without needing to transmit all raw data to the cloud. Only essential results or aggregated data need transmission, significantly reducing bandwidth requirements while maintaining the ability to analyze diverse sensor inputs.
4Adaptability or versatility
If analytics code is downloaded from cloud servers, then functionality adaptability is improved, but data transmission security risks increase
Solution Approach 1:
The patent downloads and executes a copy of the analytics application code locally on the user's device rather than executing code remotely. This copying approach allows the system to maintain up-to-date analytics functionality from the cloud while ensuring that actual data processing occurs in a secure local environment, eliminating the risk of remote code execution attacks on sensitive data.
Data Source
AI summary
Described are methods and systems to provide an adaptive cognitive analytics platform to a user such that the user's data remains secure while enabling functionality adaptive to the user's needs. In some embodiments, a cognitive analytics application can be installed on the user's mobile device to analyze a plurality of data streams generated by sensors worn by the user. The cognitive analytics application is configured to allow the user to select and download one or more analytics programs from a plurality of vetted analytics programs depending on the user's desired functionality. The cognitive analytics application can be configured to determine the types of analytics program usable by the user based on the data streams available at the user's mobile device. To secure user data and protect the user's data privacy, the cognitive analytics application can prevent the plurality of analytics programs from transmitting monitored physiological data.