Historical Action Record for User Decision Support
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In complex computing systems, users with limited experience may struggle to choose the best action for a given condition due to the variety of possible actions and lack of time to consult more experienced users.
Innovation Solution
A method that accesses a stored historical record of user-selected actions associated with different conditions, identifies relevant actions for a current condition, and displays them on a screen, allowing users to select the most effective action based on historical data and user parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a user with limited experience is presented with multiple possible actions for a condition, then the user has more options to choose from, but the user cannot make the best choice due to lack of experience and time
Solution Approach 1:
The system collects historical data on actions taken by experienced users in response to specific conditions, and uses this feedback to generate ranked recommendations. The feedback loop continuously improves the quality of recommendations by learning from actual user behavior patterns across the user group.
Solution Approach 2:
The system acts as an intermediary between the condition and the user by introducing a recommendation engine that processes historical data and presents filtered, ranked actions. This intermediary layer translates complex historical patterns into simple, actionable recommendations that guide user decision-making.
2Loss of information
If all possible actions are displayed to the user, then complete information is provided, but the user is overwhelmed by the complexity of choosing the best action
Solution Approach 1:
The system segments the complete set of possible actions into ranked categories based on historical effectiveness. Instead of presenting all actions equally, the interface divides them into prioritized groups with the most effective actions displayed first, reducing cognitive load while maintaining information completeness.
Solution Approach 2:
The recommendation quality varies by local context - actions are ranked and presented differently based on the specific condition type and historical patterns. Each condition receives a customized, locally-optimized presentation of relevant actions rather than a generic list.
3Ease of operation
If the system provides recommended actions based on historical data, then user decision-making is enhanced, but additional processing time is required to analyze historical records
Solution Approach 1:
The system performs preliminary processing of historical data during off-peak times to build pre-computed recommendation models and action rankings. This advance preparation reduces real-time processing requirements when users need recommendations, as the heavy analytical work has already been completed.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the specific condition being analyzed, using more detailed historical analysis for critical conditions and faster, simpler matching for routine conditions. This parameter adjustment optimizes the balance between recommendation quality and processing time.
Data Source
AI summary
A method, computer program product and apparatus are provided for responding to conditions within a computing system. The method includes accessing a stored historical record of user-selected actions responsive to multiple types of conditions in a computing system, wherein each instance of a condition in the historical record is associated with a user-selected action that was taken by a user within a predetermined group of users. The method further includes identifying a current instance of a condition within the computing system, using the historical record to identify one or more actions previously used in responding to the identified current instance of a condition, and displaying the one or more identified actions on a screen of the mobile computing device.