User Interface Update via NLP Action Sequence Prediction
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Solution Overview
Problem
Current user data mining methods provide limited insights for software developers to understand user behavior and intentions, hindering the development of user-friendly software interfaces, especially for complex enterprise software, as they rely on basic information and predefined workflows.
Innovation Solution
A method involving the collection of 'action corpora' from user interactions to train natural language processing models, which capture semantic relations and predict user intentions, enabling the updating of user interfaces to improve ease of operation and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional user data mining methods are used to collect basic user information, then data collection is simple and straightforward, but the value and insight provided to software developers is limited and cannot guide development of user-friendly interfaces
Solution Approach 1:
The patent introduces an intermediary system consisting of element sequence generation modules and natural language processing models that transform raw user interaction data into structured, meaningful sequences. These intermediaries bridge the gap between basic data collection and deep user behavior understanding, extracting semantic relationships without requiring complex manual analysis
Solution Approach 2:
The patent replaces traditional mechanical data analysis methods with automated natural language processing models. Instead of manually analyzing user behavior patterns, the system uses trained NLP models to automatically extract meaningful insights from user interaction sequences, substituting human cognitive processes with computational algorithms
2Adaptability or versatility
If enterprise software is designed with complete functionality and workflows, then the software provides comprehensive solutions, but the user interface becomes complicated and the user guide becomes too complex to be user-friendly
Solution Approach 1:
The patent applies preliminary action by pre-training natural language processing models with element sequences derived from actual user workflows. These pre-trained models can predict user intentions and interface needs before users encounter complex situations, allowing the system to proactively simplify the interface or provide contextual guidance before complexity arises
Solution Approach 2:
The patent makes the user interface dynamic by using trained NLP models to adapt the interface presentation based on predicted user intentions. The interface can dynamically simplify or reorganize elements based on real-time analysis of user behavior patterns, allowing comprehensive functionality to be delivered in a simplified, context-aware manner
3Measurement precision
If detailed user behavior data is collected and analyzed, then deeper insights into user intentions can be obtained, but the complexity of data processing and model training increases significantly
Solution Approach 1:
The patent performs preliminary action by pre-processing user interaction data into structured element sequences during normal operation, and pre-training NLP models on historical data. This preparation work is done in advance, so when actual analysis is needed, the system can quickly process new data using the pre-trained models without requiring intensive real-time computation
Solution Approach 2:
The patent uses copying by creating simplified representations of user interactions in the form of element sequences that capture essential behavioral patterns. Instead of processing raw, complex interaction data, the system works with copied, structured representations that retain meaningful information while reducing processing complexity and time
Data Source
AI summary
Embodiments of the present disclosure provide a method, device and computer program product for updating a user interface. According to example implementations of the present disclosure, an element sequence including a plurality of elements in the user interface is obtained, each element in the element sequence being associated with each of a plurality of actions being performed by a user in the user interface, the plurality of elements in the element sequence being sorted in an order of the plurality of actions being performed by the user; a natural language processing model is trained using the element sequence, the natural language processing model being used for modeling and feature-learning of a natural language; and the user interface is enabled to be updated based on the trained natural language processing model. Therefore, software developers can have deeper insight into users' needs and develop a more user-friendly user interface.