Hint Provision Using Log-Based Estimation Models
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Solution Overview
Problem
Conventional help information provision methods do not provide hints to users unless their input operations are repeated, making these systems less user-friendly.
Innovation Solution
An information processing apparatus that acquires user input and application logs, uses an estimation model to evaluate the likelihood of hint items being sought by the user, and displays hints with high evaluation values, improving user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If help information is provided only when input operations are repeated, then the system avoids providing unnecessary hints, but the user-friendliness and usability deteriorate
Solution Approach 1:
The system performs preliminary analysis of user input patterns and operation history to proactively identify when hints should be provided, rather than waiting for repeated operations. The estimation model calculates likelihood values in advance based on accumulated log data, enabling the system to provide hints at the optimal moment before the user encounters difficulty.
Solution Approach 2:
The system changes the parameter for triggering hint provision from a simple repetition count to a calculated likelihood value. By using the estimation model to compute probability scores based on multiple factors (input patterns, operation history, user behavior), the system can dynamically adjust when hints are provided, improving both accuracy and user-friendliness simultaneously.
2Measurement precision
If multiple types of logs are acquired and analyzed, then the accuracy of hint selection improves, but the system complexity increases
Solution Approach 1:
The system segments the log acquisition and analysis process into distinct functional modules: a log acquirer that collects multiple types of logs (input operation logs, application execution logs, user profile logs), and a separate estimation model that processes these segmented logs. This modular segmentation allows the system to handle complex multi-source data while maintaining manageable system architecture through clear separation of concerns.
Data Source
AI summary
An information processing apparatus includes a log acquirer, a first estimation model, a finder and a hint display controller. The log acquirer acquires a log at occurrence of a predetermined event. The log includes one or both of a user's input operation history and an application history. The first estimation model calculates, based on the input log, a first evaluation value for each of a plurality of hint items that are obtained by separating a plurality of hints into categories. Each of the plurality of hints is prepared as a usage hint for the user. The first evaluation value represents likelihood of each of the plurality of hint items is a hint item that includes a hint sought by the user. The finder finds, from among the plurality of hint items, one or more hint items, in each of which the first evaluation value is a first predetermined value or greater. The hint display controller displays, on a display device, from among the plurality of hint items, the one or more hint items, in each of which the first evaluation value is the first predetermined value or greater.