Intelligent Agent System for Personalized Autonomous Decisioning
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Solution Overview
Problem
Current computing systems lack personalized, autonomous decision-making capabilities and the ability to aggregate information from multiple configurable channels, limiting their functionality as virtual assistants.
Innovation Solution
An intelligent agent system that includes a user interface for configuring access rights, an aggregation module for gathering information from various channels, a machine learning module for analysis, and a decision processing module for executing autonomous decisions based on learned user behavior and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic response systems are used, then system simplicity is maintained, but personalization capability is lost
Solution Approach 1:
The system is divided into distinct functional modules: an information aggregation module that collects data from multiple channels, a machine learning module that processes the aggregated data, and a response generation module that executes autonomous decisions. This segmentation allows personalization capabilities to be added without overwhelming system complexity, as each module handles specific tasks independently.
Solution Approach 2:
The system performs preliminary actions by continuously aggregating information from multiple channels and pre-processing data through machine learning algorithms before actual user interactions occur. This enables the system to be ready with personalized responses in advance, reducing the complexity of real-time decision-making while maintaining high adaptability.
2Measurement precision
If information aggregation from multiple channels is implemented, then decision-making accuracy is improved, but information processing complexity increases
Solution Approach 1:
The patent introduces an intermediary aggregation module that sits between multiple information channels and the machine learning processing unit. This intermediary consolidates and standardizes data from diverse sources (social media, browsing data, financial information, location data, calendar information) before passing it to the analysis engine, thereby improving decision accuracy while managing processing complexity through structured intermediate representation.
3Productivity
If autonomous decision-making is enabled, then user service efficiency is improved, but control complexity increases
Solution Approach 1:
The system implements self-service capabilities through autonomous decision-making modules that can independently execute actions based on learned user preferences and aggregated information. The machine learning component continuously improves its decision-making autonomy by learning from user feedback and observed behaviors, enabling the system to handle routine tasks without human intervention while maintaining manageable control complexity through iterative learning rather than complex hard-coded rules.
Data Source
AI summary
A computing system aggregates information from a plurality of information channels associated with a computing device and a user of the computing device. A user configures the access for the computing system to specific information channels at a user interface. Based on a knowledge base and by machine learning techniques, the computing system analyzes the aggregated information to identify information relevant to an intelligent action for execution on behalf of the user. The computing system identifies the intelligent action in the context of the user's preferences and permissions granted to the computing system. The computing system initiates execution of the intelligent action based on a confidence level derived from analysis of information contained the knowledge base and historical decisioning information. The computing system receives feedback for an executed action and incorporates the feedback in the knowledge base for future decisioning based on aggregated information.