18076881. TECHNOLOGY SERVICE MANAGEMENT USING GRAPH NEURAL NETWORK simplified abstract (SAP SE)
Contents
- 1 TECHNOLOGY SERVICE MANAGEMENT USING GRAPH NEURAL NETWORK
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TECHNOLOGY SERVICE MANAGEMENT USING GRAPH NEURAL NETWORK - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about the Technology
- 1.13 Original Abstract Submitted
TECHNOLOGY SERVICE MANAGEMENT USING GRAPH NEURAL NETWORK
Organization Name
Inventor(s)
Sherwin Varghese of Kochi (IN)
TECHNOLOGY SERVICE MANAGEMENT USING GRAPH NEURAL NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 18076881 titled 'TECHNOLOGY SERVICE MANAGEMENT USING GRAPH NEURAL NETWORK
Simplified Explanation
The patent application describes a computer system that uses a graph neural network to compute a knowledge graph based on historical incidents. It then updates user vectors and ranks users based on new incident data.
- The computer system computes a knowledge graph using a graph neural network and historical incident data.
- It updates user vectors based on the knowledge graph and new incident data.
- It ranks users based on the comparison of new incident vectors with updated user vectors.
Key Features and Innovation
- Utilizes a graph neural network to compute a knowledge graph.
- Updates user vectors based on new incident data.
- Ranks users based on the comparison of incident vectors with user vectors.
Potential Applications
The technology could be applied in various fields such as recommendation systems, personalized content delivery, and targeted advertising.
Problems Solved
The technology addresses the need for efficient processing and analysis of historical incident data to provide personalized user recommendations.
Benefits
- Improved user experience through personalized recommendations.
- Efficient processing of historical incident data.
- Enhanced user engagement and satisfaction.
Commercial Applications
Potential Commercial Uses and Market Implications
The technology could be valuable for companies in the e-commerce, entertainment, and social media industries looking to enhance user engagement and increase revenue through targeted advertising.
Prior Art
There may be prior art related to graph neural networks, natural language understanding algorithms, and recommendation systems that could be relevant to this technology.
Frequently Updated Research
Research on graph neural networks, natural language processing, and recommendation systems is frequently updated and could provide valuable insights into the development of this technology.
Questions about the Technology
What is the significance of using a graph neural network in computing a knowledge graph based on historical incidents?
Using a graph neural network allows for the efficient processing and analysis of complex relationships within historical incident data, leading to more accurate and personalized user recommendations.
How does the technology update user vectors and rank users based on new incident data?
The technology computes a new incident vector based on new incident data using a natural language understanding algorithm, then updates user vectors using the knowledge graph, and finally ranks users based on the comparison of the new incident vector with the updated user vectors.
Original Abstract Submitted
In some embodiments, a computer system may compute a knowledge graph using a graph neural network and source data corresponding to historical incidents, with the source data comprising knowledge base article data, historical incident data, component data, user data, and swarm data. The computer system may compute a new incident vector based on new incident data using a natural language understanding algorithm, and, for each one of a plurality of users, compute an updated user vector using the knowledge graph. The computer system may then compute a ranked list of the plurality of users based on a comparison of the new incident vector with the corresponding updated user vector of each one of the plurality of users.
