Predictive Input Navigation Using Clickstream Behavior Modeling
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Solution Overview
Problem
Existing input devices require constant movement and clicking to navigate menus and features, leading to inefficiency and discoverability issues in computing systems.
Innovation Solution
A predictive framework that captures user clickstreams to train an ML model, anticipating user actions and presenting them for selection, thereby reducing the need for manual navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional input devices are used for navigation, then device simplicity is maintained, but user productivity decreases due to constant movement and clicking
Solution Approach 1:
The system performs preliminary actions by predicting user intent and automatically executing navigation commands before the user completes their input sequence. The prediction engine analyzes partial inputs and pre-computes the intended destination, then automatically navigates there, eliminating the need for users to complete lengthy navigation sequences manually.
Solution Approach 2:
A prediction engine is introduced as an intermediary between the user and the input device. This intermediary component intercepts partial user inputs, predicts the intended target, and automatically completes the navigation action. The mediator translates incomplete user gestures into full navigation commands, reducing the physical effort required while maintaining navigation functionality.
2Adaptability or versatility
If menus and features are hidden inside submenus, then application functionality is comprehensive, but discoverability of features deteriorates
Solution Approach 1:
The system implements feedback by analyzing user input patterns and providing predictive suggestions about intended targets. As users begin typing or gesturing, the system provides real-time feedback by predicting the intended feature or menu item, allowing users to verify or correct the prediction. This feedback loop makes hidden features more discoverable without requiring users to manually navigate through all submenu levels.
Solution Approach 2:
The prediction engine performs preliminary analysis of user inputs to anticipate which hidden features the user intends to access. By pre-computing likely destinations based on partial inputs, the system proactively reveals hidden features before users have to manually navigate to them, improving discoverability while maintaining comprehensive functionality.
3Ease of operation
If input devices are designed to be simple and direct, then ease of operation is maintained, but automation capability is limited
Solution Approach 1:
The input device system provides self-service by automatically completing navigation tasks based on predicted user intent. The prediction engine monitors user inputs and autonomously executes navigation commands without requiring explicit user confirmation for each step. This self-service capability allows the system to automate routine navigation while maintaining simple, direct input methods for users.
Data Source
AI summary
One example method includes detecting an operation of an input device, obtaining a clickstream associated with a user, using information from the clickstream, generating a prediction as to a next action by the user using the input device, and presenting the prediction to the user for possible selection by the user. Selection of the prediction by the user eliminates the need for the user to perform input device manipulations that would otherwise be required if the prediction were not selected.