Real-time User Application Matching System
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Solution Overview
Problem
Users in large organizations face challenges in efficiently selecting the appropriate applications for their tasks due to complex and dynamic application landscapes, leading to inefficiencies and potential damage to the organization, such as revenue loss or legal issues, as they struggle to keep up with the latest applications and changes in their roles and responsibilities.
Innovation Solution
A real-time recommendation system that matches users with suitable applications based on their current context and historical data, using pattern matching algorithms to identify missing functionalities and provide personalized recommendations, which can also self-learn and improve over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually select applications from a complex landscape, then they can access applications, but the time required increases and productivity decreases
Solution Approach 1:
The system performs preliminary actions by pre-analyzing user profiles, task descriptions, and application catalogs to generate pre-computed match recommendations before the user needs to select an application. This eliminates the need for users to manually search through complex application landscapes, directly reducing selection time while maintaining high productivity.
Solution Approach 2:
The patent introduces an intermediary recommendation system that acts as a mediator between users and applications. This intermediary component automatically matches users with suitable applications based on their profiles and tasks, eliminating the direct manual search process and significantly reducing the time required for application selection.
2Adaptability or versatility
If the application landscape is expanded to meet diverse user needs, then application availability increases, but the complexity of selecting appropriate applications increases
Solution Approach 1:
The system enables self-service by automatically performing the complexity analysis and matching operations. The recommendation engine self-adjusts to user profiles, task requirements, and application characteristics without requiring users to manually evaluate complex application landscapes, thus maintaining high adaptability while reducing selection complexity.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously learns from user interactions, usage patterns, and outcome data to refine its recommendation algorithms. This feedback loop enables the system to handle expanded application landscapes more effectively, improving adaptability while reducing the perceived complexity for users through increasingly accurate predictions.
3Ease of operation
If real-time recommendations are generated for each user, then personalization improves, but the computational resources required increase
Solution Approach 1:
The system performs preliminary computational actions by pre-processing and storing user profile data, application metadata, and matching rules in optimized data structures. This pre-computation reduces the computational burden during real-time recommendation generation, enabling high-quality personalization without excessive resource consumption.
Solution Approach 2:
The patent applies partial action by generating recommendations based on only the most relevant user attributes, task parameters, and application characteristics rather than processing all available data. This selective approach maintains high personalization quality while significantly reducing the computational resources required for real-time processing.
Data Source
AI summary
User information for a particular user is accessed. Application information for applications that are available in an organization of the particular user is accessed. One or more pattern matches between the user information and the application information are determined. One or more application recommendations are generated based on the determined one or more pattern matches. The one or more application recommendations are provided.