Context-Aware Dialogue UI Step Prediction for Web Navigation
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Solution Overview
Problem
Existing dialogue systems fail to accurately determine user intentions and navigate complex web interfaces due to limited context awareness, ignoring UI states and events, leading to ineffective user assistance.
Innovation Solution
A context-aware dialogue system that tracks UI events and states, using deep learning models to predict next user interface steps based on a combination of user inputs, UI events, and system events, providing proactive assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional dialogue systems are used, then system simplicity is maintained, but user intention recognition accuracy deteriorates due to limited context awareness
Solution Approach 1:
The patent merges multiple data sources (UI events, dialogue history, user actions) into a unified context representation that feeds into the dialogue system. This integration allows the system to accurately determine user intentions by combining information from various channels rather than relying on dialogue text alone.
Solution Approach 2:
The system performs preliminary tracking and analysis of UI events and user actions before the actual dialogue processing occurs. By pre-processing and storing contextual information from the user interface, the system prepares context data in advance, enabling more accurate intention recognition during dialogue without adding complexity to the core dialogue engine.
2Adaptability or versatility
If UI events and states are tracked, then context awareness is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts only the most relevant features and events from the complex stream of UI events and user actions. Rather than processing all raw data, the system identifies and extracts key contextual elements (such as significant user actions, state changes, and relevant UI events) that are most useful for determining user intentions, thereby reducing processing complexity while maintaining context awareness.
Solution Approach 2:
The system employs a universal context representation framework that handles multiple types of data (UI events, dialogue history, user actions) through a single integrated processing pipeline. This multi-functional approach allows the same processing mechanisms to handle diverse data types, reducing overall system complexity despite the variety of inputs.
3Measurement precision
If deep learning models are used for prediction, then prediction accuracy is improved, but computational resources required increase
Solution Approach 1:
The patent applies deep learning models selectively rather than uniformly across all dialogue scenarios. The system uses sophisticated deep learning prediction for complex, ambiguous situations where high accuracy is critical, while relying on simpler rule-based or heuristic methods for straightforward cases. This partial application of computationally intensive methods reduces overall resource consumption while maintaining high prediction accuracy when needed.
Data Source
AI summary
In the present application, a method of predicting next UI steps for a user by a context-aware dialogue system is disclosed. A plurality of user interface (UI) events associated with a UI is tracked. A predicted next UI step is determined based on at least a portion of the plurality of UI events. A dialogue system component is caused to indicate the predicted next UI step.