Intelligent UI Task Prediction via ML Models
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Solution Overview
Problem
Users face difficulty in navigating complex cloud or locally hosted applications to find relevant tasks due to long menus and tasks requiring multiple actions across applications, making task completion a tedious and time-intensive process.
Innovation Solution
A trained machine-learning model predicts tasks for users by analyzing log records and contextual user profiles, presenting them as selectable links on an intelligent user interface, with iterative model selection and reinforcement learning to enhance accuracy and adapt to user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users navigate through long menus in complex applications to find relevant tasks, then they can access task functionality, but the process becomes tedious and time-intensive
Solution Approach 1:
The system performs preliminary actions by predicting and preparing task recommendations before users actually need them. The machine learning model continuously analyzes user behavior patterns, application states, and historical data to pre-compute relevant task predictions, so that when users access the intelligent UI, ready-to-present recommendations are immediately available without requiring users to navigate through menus to find tasks
Solution Approach 2:
The patent replaces the mechanical navigation system (manual menu browsing and task searching) with an intelligent prediction system. Instead of users mechanically navigating through application menus and interfaces to locate tasks, the system uses machine learning models to automatically predict and present relevant tasks through an intelligent UI, substituting user manual exploration with automated intelligent recommendation
2Adaptability or versatility
If cloud or locally hosted applications provide comprehensive task functionality, then they can perform domain-specific operations, but the applications become increasingly complex with long menus
Solution Approach 1:
The system extracts the task identification and recommendation functionality from the complex application interface itself. By separating the task prediction function into a standalone intelligent UI layer that uses machine learning models, the system removes the burden of navigating complex application menus while preserving access to all underlying task functionalities. The intelligent UI acts as an extraction layer that presents only relevant tasks without requiring users to engage with the full complexity of the underlying applications
Solution Approach 2:
The patent introduces an intermediary intelligent UI layer between the user and the complex applications. This intermediary uses machine learning models to translate complex application functionality into simplified task predictions. The intermediary receives input from multiple applications and user contexts, processes this information through prediction models, and presents simplified task recommendations, thereby mediating between application complexity and user needs
3Quantity of substance
If the machine-learning model predicts tasks for all users, then task prediction coverage is improved, but accuracy may decrease due to diverse user behaviors
Solution Approach 1:
The system segments users into distinct clusters based on their behavior patterns, roles, and interaction characteristics. By dividing the user population into segments and training separate machine learning models for each cluster, the system achieves both broad coverage (all user segments are represented) and high accuracy (each segment receives predictions tailored to its specific patterns). This segmentation approach allows the system to handle diverse user behaviors while maintaining precision within each segment
Solution Approach 2:
The patent applies local quality by providing different prediction models and parameters for different user clusters. Instead of using a single uniform prediction approach for all users, the system tailors the prediction mechanism to local characteristics of each user segment. Each cluster receives predictions optimized for its specific behavior patterns, roles, and contexts, thereby achieving high accuracy locally while maintaining comprehensive coverage across all user types
Data Source
AI summary
The present disclosure relates to an intelligent user interface that predicts tasks for users to complete using a trained machine-learning model. In some implementations, when a user accesses the intelligent user interface, the available tasks and a user profile can be inputted into the trained machine-learning model to output a prediction of one or more tasks for the user to complete. Advantageously, the trained machine-learning model outputs a prediction of tasks that the user will likely need to complete, based at least in part on the user's profile and previous interactions with applications.