Contextual UI Recommendations via Activity Recognition
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Solution Overview
Problem
Current online user interfaces lack personalization, providing a similar experience for all users, which makes it challenging to customize the online experience for individual users or devices.
Innovation Solution
A computer system that automatically populates graphical elements on a user interface by receiving information from third-party server operators, applying a trained machine-learning model to predict user activity patterns, and populating graphical elements with contextual recommendations based on these patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a standardized user interface is provided to all users, then the system complexity is reduced and ease of operation is improved, but the adaptability to individual user needs deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting user activity data and training machine learning models in advance to predict user needs before they are explicitly expressed. This allows the interface to be pre-personalized based on predicted patterns, achieving adaptability without requiring complex real-time customization mechanisms from the user.
Solution Approach 2:
The system enables self-service personalization by automatically analyzing user behavior patterns and generating customized interface elements without user intervention. The machine learning model autonomously adapts the interface to individual users based on their activity data, eliminating the need for manual customization while maintaining high adaptability.
2Adaptability or versatility
If machine learning models are applied to predict user patterns, then the adaptability to individual users is improved, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary machine learning model that mediates between raw user activity data and the user interface. This model acts as a bridge, translating complex behavioral patterns into simplified interface recommendations, thereby achieving high adaptability without directly exposing the complexity of data processing to the user or system architecture.
Solution Approach 2:
The system creates simplified copies or representations of complex user behavior patterns through the machine learning model. Instead of directly processing and responding to all raw activity data, the model generates condensed pattern representations that drive interface personalization, reducing the computational complexity while maintaining adaptability.
3Productivity
If graphical elements are automatically populated based on predicted patterns, then the productivity of information delivery is improved, but the loss of information control by users increases
Solution Approach 1:
The system implements feedback mechanisms where user responses to automatically populated graphical elements are collected and used to refine future predictions. This allows the system to adjust its information delivery based on user preferences and corrections, maintaining productivity while gradually improving user control over the information presented.
Data Source
AI summary
A computer system can automatically populate graphical elements on a user interface. The system can receive sets of information from one or more third-party server operators. The system can create, for each set of information, a respective graphical element for the user interface. The respective graphical element can indicate at least one service provided in a respective set of information. The system can collect, via the user interface, a set of data from a specific user. The system can apply a trained machine-learning model to the set of data to predict a pattern in activities associated with the specific user. The system can automatically populate a particular graphical element in the user interface based on the pattern in activities associated with the specific user. The particular graphical element can include at least one recommended action related to the at least one service indicated by the particular graphical element.