Digital Channel User-Action Prediction with Precomputed Knowledge Graphs
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Solution Overview
Problem
Traditional systems struggle to track and respond to user actions in real time, especially when users make rapid decisions, leading to missed opportunities for engagement and security risks, and are often resource-intensive and inaccurate due to the inability to differentiate between human and non-human behavior.
Innovation Solution
A device that tracks user actions and predicts next actions by converting domains into knowledge graphs, calculating probabilities of input traversals, and taking proactive measures to enhance user engagement and security, such as providing real-time feedback and authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional systems track user actions in real time, then user engagement can be improved, but computing resources are consumed excessively
Solution Approach 1:
The system pre-converts domain elements into a knowledge graph structure with nodes and edges beforehand, so that when user actions occur, the tracking and prediction can proceed efficiently without intensive real-time computation. This preliminary structuring enables faster query and prediction operations.
Solution Approach 2:
The patent replaces traditional computational tracking methods with a physics-inspired approach using virtual particles and knowledge graph traversal. Instead of heavy computational analysis of user behavior, the system uses simulated particle movement through the knowledge graph to predict next actions, reducing computing resource requirements.
2Ease of operation
If traditional systems respond to user actions, then user experience can be enhanced, but response time is delayed
Solution Approach 1:
The system performs preliminary conversion of domain elements into a knowledge graph structure, pre-establishing nodes and edges that represent user journey paths. This allows the system to instantly query and predict user next actions without delayed computational analysis, enabling real-time responsive interactions.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary layer between user actions and system responses. This knowledge graph acts as a pre-computed map that enables instant prediction of user next actions, eliminating the delay caused by real-time analytical processing while maintaining accurate user experience insights.
3Reliability
If traditional systems monitor user behavior, then security can be improved, but accuracy is reduced due to inability to differentiate human and non-human behavior
Solution Approach 1:
The system pre-converts domain elements into a knowledge graph with structured nodes and edges representing legitimate user journey paths. By comparing actual user actions against this pre-established knowledge graph structure, the system can accurately differentiate between human users following expected paths and non-human entities exhibiting anomalous traversal patterns, thereby improving both security and measurement precision.
Data Source
AI summary
A device may identify a domain associated with a user, and may identify domain elements in the domain and convert the domain to a knowledge graph with nodes and edges. The device may receive one or more movements of an input associated with the domain, and may predict positions of the input within the knowledge graph based on the one or more movements. The device may identify closest nodes to the positions, and may determine particular domain elements that correspond to the closest nodes. The device may calculate probabilities that the input will traverse the particular domain elements, and may perform one or more actions based on the probabilities that the input will traverse the particular domain elements.