Intelligent Personal Agent Platform for Autonomous Activity Monitoring
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Solution Overview
Problem
Current systems for monitoring and analyzing user data from various sources, including sensors and machines, lack the ability to autonomously determine responsive actions, learn from data, and provide tailored feedback or predictions, limiting their effectiveness in real-time monitoring and decision-making.
Innovation Solution
An intelligent personal agent platform that collects and processes data from multiple sources, using software-based agents to analyze user information, determine responsive actions, and learn for improved analysis, enabling personalized advice, coaching, and automated tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If simple monitoring systems are used to track user data, then data collection is straightforward, but the system cannot autonomously analyze data to determine responsive actions
Solution Approach 1:
The system is divided into distinct functional modules: data collection module, data storage module, data analysis module with machine learning models, and action execution module. This segmentation allows complex autonomous analysis to be achieved through coordinated simpler components, resolving the contradiction between automation extent and device complexity.
Solution Approach 2:
An intelligent personal agent platform serves as an intermediary between raw data collection and responsive action execution. The platform includes software-based agents that autonomously analyze data, determine actions, and coordinate execution, enabling automation without requiring direct complex integration between all system components.
2Measurement precision
If comprehensive data from multiple sources is collected, then analysis accuracy improves, but real-time processing becomes more difficult
Solution Approach 1:
Data from multiple sources is pre-processed, validated, and stored in standardized formats before analysis. Machine learning models are pre-trained and prepared in advance. This preliminary preparation enables rapid real-time analysis when data arrives, resolving the contradiction between comprehensive data collection and processing speed.
Solution Approach 2:
Traditional rule-based data processing is replaced with machine learning models that automatically learn patterns and relationships from comprehensive data. The models use algorithms including neural networks, decision trees, and clustering techniques to efficiently analyze multi-source data in real-time, achieving both accuracy and speed.
3Ease of operation
If the system provides personalized feedback and predictions, then user assistance quality improves, but system complexity increases
Solution Approach 1:
The system generates personalized feedback and predictions tailored to each user's specific data patterns, needs, and context. Machine learning models create user-specific profiles and adapt analysis parameters locally for each user, providing high-quality personalized support without requiring the entire system architecture to be overly complex.
Solution Approach 2:
The intelligent personal agent platform autonomously performs data analysis, action determination, and feedback generation without requiring extensive manual configuration or intervention. The system learns from user interactions and automatically improves its personalization capabilities, reducing operational complexity while maintaining high support quality.
4Reliability
If the system learns from collected data to improve predictions, then analytical capability improves, but computational requirements increase
Solution Approach 1:
The system implements periodic batch learning where machine learning models are retrained and updated at scheduled intervals using accumulated data, rather than continuously learning from every data point in real-time. This periodic approach maintains high prediction accuracy while significantly reducing instantaneous computational energy consumption during operation.
Solution Approach 2:
The system dynamically adjusts model complexity and learning parameters based on available computational resources and data characteristics. Machine learning models use techniques such as dimensionality reduction, feature selection, and adaptive learning rates to maintain high prediction reliability while optimizing energy consumption during data processing.
Data Source
AI summary
The invention is directed to an intelligent personal agent platform that uses sensor data and other inputs to monitor and detect daily activities, to monitor adherence to goals or plans, and to provide in-context personalized advice, coaching, and support to a user. The platform integrates wearable and environmental sensors that gather real-time behavior data with one or more software-based intelligent personal agents that run on cloud-based or local servers to monitor and analyze the data and provide a response, such as a request to take a certain action. Users can interact with their personal agents wherever they are via a variety of interfaces depending on the communication devices and communication networks that are available to them (e.g. mobile devices, Smart TVs, wearable displays, heads-up displays in a car, touch interfaces, and spoken natural language).