Context-Aware Dialogue UI Navigation Using Event-Based Step Prediction
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Solution Overview
Problem
Dialogue systems typically fail to accurately determine user intentions beyond text input and do not integrate contextual information from application environments, leading to ineffective user interface navigation.
Innovation Solution
A context-aware dialogue system that tracks user interface events and integrates contextual information to predict and provide proactive assistance, using a combination of event serialization, caching, and deep learning models to determine the next user interface steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dialogue systems only track text input, then the system complexity is low, but the accuracy of determining user intentions is insufficient
Solution Approach 1:
The system segments the tracking of user interactions into distinct components: text input tracking, UI event tracking (mouse movements, clicks, scrolling), and contextual information tracking. Each component processes specific types of data independently before integrating them in the dialogue state tracking module, thereby improving intention accuracy without creating a monolithic complex system
Solution Approach 2:
The patent implements a nested structure where the dialogue state tracking module contains sub-modules for text processing, UI event processing, and contextual information processing. The UI event serializer and event cache manager are nested within the broader system architecture, allowing hierarchical organization of complexity
2Ease of operation
If dialogue systems ignore application context, then the system design is simple, but the effectiveness of user interface navigation is reduced
Solution Approach 1:
The patent introduces an intermediary layer called the UI event serializer that bridges the application environment and the dialogue system. This serializer converts raw UI events into a standardized format that the dialogue state tracking module can process, enabling context integration without direct complex coupling between the application and dialogue system
Solution Approach 2:
The event cache manager performs preliminary actions by pre-processing and caching UI events before they reach the dialogue state tracking module. This preliminary organization of contextual information reduces the integration complexity during actual dialogue processing
3Measurement precision
If the system tracks comprehensive UI events, then the prediction accuracy of next steps is improved, but the data processing load increases
Solution Approach 1:
The event cache manager performs preliminary filtering and organization of UI events, maintaining only the most relevant event history needed for accurate next-step prediction. This preliminary action reduces the data volume processed by the dialogue state tracking module while preserving prediction accuracy
Solution Approach 2:
The system extracts only the essential features from comprehensive UI event data for feeding into the dialogue state tracking module. By taking out and retaining only the most predictive event characteristics, the system maintains high prediction accuracy while reducing overall data processing load
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.