Monitoring Device Programming via Signal Profile Machine Learning
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Solution Overview
Problem
Monitoring devices face inefficiencies due to frequent software and mechanical updates, leading to inconsistent user experiences and consumer frustration, as each update restarts the relationship between the user and the device, hindering effective customization.
Innovation Solution
A system and method that utilize a computing device to obtain user data from monitoring devices, calculate signal profiles through machine-learning processes, identify optimal scan frequencies, and generate device schemes to program the monitoring devices efficiently, ensuring consistent and tailored user experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If monitoring devices are frequently updated with software and mechanical changes, then device functionality and features are improved, but user experience consistency deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting user data and establishing customized monitoring parameters before updates occur. Machine learning models pre-process user behavior patterns and create baseline profiles that can withstand subsequent device updates, ensuring experience consistency is maintained despite functional changes.
Solution Approach 2:
The system implements continuous feedback loops where user responses to device updates are collected and fed back into the machine learning models. This feedback mechanism allows the system to adapt to changes while maintaining personalized monitoring schemes, resolving the contradiction between device evolution and experience consistency.
2Ease of operation
If customized monitoring schemes are created for each user, then user engagement is improved, but device complexity increases
Solution Approach 1:
The system enables self-service by allowing devices to automatically generate customized monitoring schemes through embedded machine learning algorithms. The device autonomously processes user data, identifies patterns, and creates personalized monitoring parameters without requiring complex manual programming, thus maintaining ease of operation while reducing apparent device complexity.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw user data and customized monitoring schemes. These models simplify the complexity by automatically translating diverse user inputs into coherent monitoring parameters, shielding users from the underlying computational complexity while delivering personalized experiences.
3Measurement precision
If machine learning processes are used to analyze user data, then monitoring precision is improved, but computational requirements increase
Solution Approach 1:
The system segments the machine learning process into distinct stages: initial data collection and model training occur during low-activity periods or in the cloud, while inference and pattern recognition are performed locally on the device during active monitoring. This segmentation reduces real-time computational energy requirements while maintaining high monitoring precision through distributed processing.
Data Source
AI summary
A system for programming a monitoring device includes a computing device configured to obtain a user datum of a plurality of user datums from a monitoring device, calculate a signal profile as a function of the user datum, identify a scan frequency correlated to the signal profile, wherein identifying further comprises receiving a frequency training set relating at least a first element of a vigor element to at least a first frequency requirement and using a frequency machine learning process, wherein the frequency machine learning process is configured using the signal training set, generate a device scheme as a function of the scan frequency, and program the monitoring device as a function of the device scheme.