Widget Recommendation Engine for Personalized Midstream User Interfaces
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Solution Overview
Problem
Existing user interface systems in midstream oil and gas applications fail to personalize user experiences by providing relevant widgets based on individual user interactions, leading to suboptimal user satisfaction and efficiency.
Innovation Solution
A method and system that captures user interactions with widgets to create personalized lists of recommended widgets and suggested items, using algorithms like HyperLogLog to analyze usage patterns and predict next steps, enhancing user experience and operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a general user interface system provides all available widgets to all users, then users have access to maximum options, but user satisfaction decreases due to lack of personalization and relevance
Solution Approach 1:
The system performs preliminary analysis of user interactions with widgets before presenting the interface. By capturing and analyzing user behavior data in advance, the system pre-computes personalized widget recommendations so that when users access the application, they immediately see relevant widgets tailored to their preferences and historical interactions, eliminating the need for manual customization
Solution Approach 2:
The system implements a feedback loop where user interactions with widgets are continuously captured, analyzed, and used to update future widget recommendations. The system monitors which widgets users view, interact with, and spend time on, then uses this feedback to dynamically adjust and personalize the widget presentation for each user, creating a self-improving personalized interface
2Ease of operation
If the system captures and analyzes detailed user interaction data for personalization, then user experience improves, but system complexity increases
Solution Approach 1:
The system automatically captures user interaction data through embedded tracking code in the widget interface without requiring manual input from users. The system self-services by automatically analyzing the captured data, computing user preferences, and generating personalized widget recommendations without human intervention, thereby improving user experience while managing complexity through automation
Solution Approach 2:
The system replaces complex manual analysis and customization processes with automated computational algorithms. Instead of requiring manual configuration or complex rule-based systems, the patent uses data-driven algorithms to analyze user interactions and generate personalized recommendations, substituting mechanical complexity with intelligent automation
3Productivity
If the system provides all widgets equally to all users, then implementation is simple, but productivity decreases due to users spending time searching for relevant tools
Solution Approach 1:
The system segments the widget interface by creating personalized subsets of widgets for each user based on their interaction history and preferences. Instead of presenting all widgets uniformly, the system divides and organizes widgets into relevance-based groups, placing the most relevant widgets in prominent positions and less relevant ones in secondary areas, thereby improving productivity while managing complexity through structured organization
Data Source
AI summary
A method for generating a recommendation related to user preferences. The method includes capturing user interactions related to a plurality of widgets. Each of the widgets are user interface widgets and relate to a corresponding dataset. Capturing the plurality of user interactions may include recording how much time or frequency each user spends viewing each of the widgets, recording how long since each user last interacted with each of the widgets, or recording how many users have interacted with each of the widgets. A list of recommended widgets based on the captured user interactions is created and then displayed to each user. Displaying the list of recommended widgets includes displaying the list of recommended widgets on a landing page of an application, displaying a subsequent widget to each user, or displaying a subsequent application to each user. Once displayed, the widgets provide the users with a suggested midstream facility action.