18059423. MULTI-FEATURE RESOURCE RECOMMENDER SYSTEM FOR PROCESS OPTIMIZATION AND USER PREFERENCE INFERENCE simplified abstract (Microsoft Technology Licensing, LLC)
Contents
- 1 MULTI-FEATURE RESOURCE RECOMMENDER SYSTEM FOR PROCESS OPTIMIZATION AND USER PREFERENCE INFERENCE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MULTI-FEATURE RESOURCE RECOMMENDER SYSTEM FOR PROCESS OPTIMIZATION AND USER PREFERENCE INFERENCE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MULTI-FEATURE RESOURCE RECOMMENDER SYSTEM FOR PROCESS OPTIMIZATION AND USER PREFERENCE INFERENCE
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
Michal Adam Sroka of Birkerød (DK)
Mohammadreza Fani Sani of Lyngby (DK)
Shahab Moradi of San Bruno CA (US)
Alejandro Gutierrez Munoz of Parkland FL (US)
MULTI-FEATURE RESOURCE RECOMMENDER SYSTEM FOR PROCESS OPTIMIZATION AND USER PREFERENCE INFERENCE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18059423 titled 'MULTI-FEATURE RESOURCE RECOMMENDER SYSTEM FOR PROCESS OPTIMIZATION AND USER PREFERENCE INFERENCE
Simplified Explanation
The abstract of the patent application describes a recommender system that generates score values for event pairs in an event log, based on historical data and user preferences, to recommend event pairs for inclusion in a process.
- The recommender system creates event pairs from an event log.
- It generates score values for each event pair using historical data and features.
- User preferences are taken into account to provide recommended event pairs.
- The recommended event pairs are suggested for inclusion in a process.
Potential Applications
The technology could be applied in various industries such as e-commerce, supply chain management, and healthcare to optimize processes and improve efficiency.
Problems Solved
1. Identifying relevant event pairs in a large dataset. 2. Optimizing processes based on historical data and user preferences.
Benefits
1. Increased efficiency in process optimization. 2. Personalized recommendations based on user preferences. 3. Improved decision-making through data-driven insights.
Potential Commercial Applications
Optimizing supply chain processes for cost savings and improved performance.
Possible Prior Art
One possible prior art could be collaborative filtering algorithms used in recommendation systems, but this specific approach of generating event pairs from an event log and incorporating historical data and user preferences may be unique.
Unanswered Questions
The patent application does not provide details on how user data privacy is ensured in the recommender system. It would be important to understand the measures in place to protect user information.
What is the scalability of this technology for large datasets?
The scalability of the recommender system for processing large volumes of event data and generating recommendations efficiently is not discussed in the abstract. Understanding the system's ability to handle big data would be crucial for its practical implementation.
Original Abstract Submitted
Solutions for identifying and optimizing a process are provided herein. A recommender system creates a plurality of event pairs from an event log, and using historical data and features corresponding to the event pairs, the recommender system generates score values for each event pair. Using the score values and user preferences, the recommender system provides a recommended event pair to the user to include in a process.
