Embedded Learning Management System for Cross-Application Training
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Solution Overview
Problem
Current learning management systems are limited in assisting novice users across multiple applications and media types, as they are application-specific and do not share information or learned behaviors, making it difficult for users to learn efficiently across different tools and platforms.
Innovation Solution
A holistic learning management system (LMS) embedded in an operating system or capable of monitoring multiple applications and devices, which compares user input to model user behavior to provide personalized training suggestions and course recommendations across various applications and communication mediums.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If application-specific help wizards are used, then users receive targeted assistance for individual applications, but users cannot learn efficiently across multiple applications and media types
Solution Approach 1:
The patent implements a universal learning management system that functions across multiple applications and media types. The system monitors user behavior across different applications (word processing, spreadsheets, email, etc.) and provides integrated training recommendations, making the help system versatile rather than application-specific. This resolves the contradiction by enabling one system to serve multiple functions across diverse applications.
Solution Approach 2:
The patent merges multiple application-specific help systems into a single integrated learning management system. By combining data from various applications and media types into one unified system that tracks user behavior across all applications and provides centralized training recommendations, the system achieves both targeted assistance and cross-application learning efficiency.
2Extent of automation
If manual learning management systems are used, then users can schedule training and track progress, but the systems require manual updates and supervisor intervention to determine appropriate courses
Solution Approach 1:
The learning management system automatically monitors user behavior across applications, analyzes performance data, and generates training recommendations without requiring manual supervisor intervention. The system serves itself by autonomously tracking user interactions, identifying knowledge gaps, and proposing relevant training courses, thereby eliminating the need for manual course selection and reducing time loss.
Solution Approach 2:
The system implements continuous automated feedback loops where user behavior is monitored, analyzed, and used to generate real-time training recommendations. This automated feedback mechanism replaces manual supervisor review and manual course updates, significantly reducing the time required for learning management while maintaining high automation levels.
3Loss of information
If help wizards are limited to single applications, then they provide focused guidance, but they cannot suggest alternative applications or techniques for more complex tasks
Solution Approach 1:
The learning management system is designed as a universal platform that monitors and analyzes user behavior across multiple applications simultaneously. It can suggest alternative applications and techniques for complex tasks by drawing on data from all monitored applications, thereby preventing information loss while managing complexity through centralized intelligent processing rather than distributed application-specific logic.
Data Source
AI summary
The present invention provides a learning management system that is embedded in an application, operating system, or multiple applications. The learning management system is adapted to identify whether training courses would be desirable for a user, based on usage of one or more applications, installment of one or more applications, or occurrence of one or more key words within an application.