GUI Communication Ordering Using User-Specific Priority Scores
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Solution Overview
Problem
Existing systems fail to prioritize enterprise announcements effectively on graphical user interfaces based on user-specific data, leading to suboptimal user engagement and information dissemination.
Innovation Solution
A method and system that utilize machine learning to determine user-specific identifiers or scores for digital communications, prioritizing their display on a graphical user interface based on personal data, and arranging them in categories with higher engagement likelihood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If enterprise announcements are displayed without prioritization, then all users receive uniform information, but user engagement and information relevance deteriorate
Solution Approach 1:
The patent applies local quality by customizing the display priority and arrangement of announcements based on individual user characteristics. Each user receives a personalized view where announcements are ordered according to their specific relevance, rather than a uniform display for all users. This is achieved through machine learning models that analyze user data and determine personalized prioritization.
Solution Approach 2:
The system changes the parameter of announcement priority from a fixed, uniform value to a dynamic, user-specific value. Machine learning models generate personalized priority scores that adjust the display order of announcements based on individual user profiles, behaviors, and preferences, thereby optimizing relevance for each user.
2Measurement precision
If machine learning models are used to prioritize announcements, then user-specific relevance improves, but system complexity increases
Solution Approach 1:
The system employs self-service by using machine learning models that automatically analyze user data and generate prioritization rankings without requiring manual intervention. The models continuously learn from user interactions and automatically adjust announcement priorities, reducing the need for manual system configuration and management.
Solution Approach 2:
The patent replaces manual or rule-based prioritization mechanisms with machine learning-based automated systems. Instead of using fixed rules or manual curation to determine announcement importance, the system uses predictive analytics and user behavior analysis to dynamically prioritize content, substituting mechanical processes with intelligent algorithms.
3Loss of information
If announcements are personalized based on user data, then information transparency improves, but data processing requirements increase
Solution Approach 1:
The system performs preliminary action by pre-processing and analyzing user data in advance to build user profiles and preferences. Machine learning models are trained beforehand to recognize patterns and predict user interests, enabling fast prioritization decisions when announcements need to be displayed without requiring intensive real-time processing.
Data Source
AI summary
Systems and methods are disclosed that improve network data processing by prioritizing digital communications for display on a graphical user interface. The system comprises a computing system with at least one processing device and at least one memory device, wherein the computing system executes computer-readable instructions. A network connection operatively connects at least one user device and the computing system. Upon execution of the computer-readable instructions, the computing system is configured to: receive, via user software application installed on the at least one user device, personal data of a user; predict, via the computing system, a user-specific score for each of the digital communications based on the personal data of the user; and display, via the user software application, at least one of the digital communications on a graphical user interface of the at least one user device according to a display order based on the predicted user-specific score.