User Behavior Modeling for Decision Support
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Solution Overview
Problem
The increasing volume of data generated daily poses inefficiencies as much of it is either ignored or not effectively utilized in decision-making processes, leading to undesirable outcomes due to the time required to sort through stored data and the abandonment of significant information.
Innovation Solution
A computer-implemented method and system that analyzes data streams to identify dependent users, detects triggering events, determines their present conditions, and provides customized support elements based on user models and condition preparedness scores, enabling informed decision-making by presenting tailored support to authorized users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is stored for future reference and analysis, then information availability is improved, but time required to sort through and retrieve useful data increases
Solution Approach 1:
The system performs preliminary actions by continuously analyzing data streams in real-time as they are generated, rather than waiting for batch processing. Triggering events are detected immediately when conditions are met, and support elements are provided on-demand. This eliminates the need to sort through stored historical data later, as the system has already processed and acted upon the information when it became available.
2Reliability
If all generated data is retained and analyzed, then decision-making accuracy is improved, but processing complexity and resource requirements increase
Solution Approach 1:
The system extracts only the essential and relevant information from data streams by identifying triggering events based on predefined conditions. Instead of processing all generated data, the system selectively extracts data that meets specific criteria (triggering rules) and provides support elements only for those cases. This reduces processing complexity while maintaining decision-making accuracy by focusing on critical information.
3Speed
If data analysis is performed in real-time, then responsiveness to user needs is improved, but computational resource consumption increases
Solution Approach 1:
The system performs partial analysis by monitoring data streams continuously but only executing full analysis and generating support elements when triggering events are detected. During normal operation, the system uses minimal computational resources to monitor for conditions. When a triggering event occurs, the system then performs the necessary analysis and provides the support element. This approach maintains responsiveness while significantly reducing overall computational resource consumption compared to continuous full-analysis.
Data Source
AI summary
In some examples, user models are generated to represent experience of users with respect to their roles. Models that represent conditions are also generated. As part of generating a support element for a particular user, a triggering event is detected (e.g., an update to a user record), a condition is identified, a condition model and a user model are compared to identify a condition preparedness score. This score is used to generate the support element, which is shared with a user device of the user.