Context Graph Rule Engine for Automated Financial Advice
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Solution Overview
Problem
High net worth individuals often lack personalized financial and health advice due to the absence of affordable human advisors, and existing websites require human intervention to provide tailored guidance.
Innovation Solution
A system that collects user interaction data to generate customized messages using machine-learning techniques, creating a context graph that applies rules to suggest actions, engages users in dialog, and updates the graph based on responses to refine messaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a human advisor is used to provide personalized financial and health advice, then the quality and personalization of advice is improved, but the cost increases making it unacceptable to the average employee or investor
Solution Approach 1:
The patent creates a digital copy of the human advisor's expertise through an automated system that uses machine learning models, context graphs, and rule-based engines to replicate personalized advice-giving capabilities without requiring actual human advisors, thereby eliminating the cost barrier while maintaining personalization quality
Solution Approach 2:
The patent replaces the mechanical system of human advisors with an automated computational system comprising machine learning algorithms, context graph processing, and rule-based messaging engines that can deliver personalized financial and health advice at scale without human intervention, thus reducing cost while preserving personalization
2Extent of automation
If machine-learning techniques are applied to analyze user interaction data, then the automation and personalization of advice is improved, but the system complexity increases
Solution Approach 1:
The patent segments the complex automated advice system into distinct functional modules including user interaction event data collection, context graph construction and updating, rule-based messaging engine, and machine learning model integration, allowing each component to be developed, maintained, and scaled independently thereby managing overall system complexity
Solution Approach 2:
The patent introduces a context graph as an intermediary data structure that mediates between raw user interaction data and the rule-based messaging system, serving as a structured representation of user context that simplifies the processing pipeline and enables more manageable automation without requiring direct complex interactions between all system components
Data Source
AI summary
One embodiment of the present invention provides a system for generating a message. During operation, the system receives user interaction event data. The user interaction event data describes explicit or implicit interactions of a user with a web application and/or mobile application. Next, the system modifies a graph describing the user's current context associated with the user based on an analysis of the user interaction event data, as interpreted by the system learning from previous processing of user interaction event data. The context graph includes information about the user's state, behavior, and interests, and some or all portions of the context graph may be shared between users. The system determines a set of rules associated with a group of users that includes the user, and then applies the determined set of rules to any context graph associated with the user to generate the message.