Transaction Event Detection Through Multi-Dimensional Context Graphs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing interactive interfaces struggle with multi-tasking, providing contextually irrelevant solutions, and inefficiently managing user requests due to inaccurate context derivation and lack of data partitioning, leading to user dissatisfaction and wasted computational resources.
Innovation Solution
Implementing a multi-dimensional context management graph that organizes user data into manageable clusters, enabling concurrent handling of multiple user requests and providing relevant predictive and reactive solutions by correlating context clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional time-based context derivation techniques are used, then the system can derive context from recent locations or interactions, but the context predictions become inaccurate and solutions become irrelevant
Solution Approach 1:
The patent segments user data into distinct context clusters (location context, interaction context, transaction context, etc.) rather than treating all data uniformly. Each cluster is processed independently to derive accurate context predictions, resolving the contradiction between using sufficient data and maintaining prediction accuracy.
Solution Approach 2:
The patent transitions from traditional time-based single-dimensional context derivation to a multi-dimensional context graph that incorporates spatial, temporal, and semantic dimensions. This allows the system to derive context from multiple perspectives simultaneously, improving both accuracy and reliability of solutions.
2Productivity
If the system handles multiple user requests concurrently, then user productivity improves, but the system complexity increases
Solution Approach 1:
The patent segments the request management system into independent context cluster processors. Each context cluster (location, interaction, transaction) is handled by dedicated processing logic, allowing concurrent request handling without increasing overall system complexity. The segmentation enables parallel processing while maintaining manageable individual components.
Solution Approach 2:
The context graph serves as an intermediary data structure that mediates between multiple user requests and the resolution system. It organizes context clusters and their relationships, enabling concurrent request processing by providing a structured interface for context retrieval and correlation without direct complex interactions between request handlers.
3Loss of information
If user data is organized into manageable clusters, then context understanding improves, but data processing complexity increases
Solution Approach 1:
The patent segments user data into distinct context clusters (location context, interaction context, transaction context, device context, etc.) that can be independently processed and managed. This segmentation improves context understanding by organizing related information together while reducing processing complexity through modular handling of each cluster type.
Solution Approach 2:
The patent creates a universal context graph data structure that can accommodate multiple types of context clusters through a unified interface. This multi-functional structure handles diverse data types (locations, interactions, transactions, devices) using consistent processing logic, improving context understanding without proportionally increasing processing complexity.
Data Source
AI summary
Techniques for resolving multiple user requests from multiple user accounts by an interactive interface are described. An interactive interface can obtain a first multi-dimensional context graph for a first user account and a second context graph for a second user account. Each graph comprises correlated contexts related to the user account. The interface can also receive a first user request associated with the first user account and a second user request associated with the second user account; determine, based on the first graph, a first current context and one or more first previous contexts for the first user request; determine, based on the second graph, a second current context and one or more second previous contexts for the second user request; determine one or more interrelationships between the first and the second graphs; and resolve the user requests based on the contexts and the interrelationships.