Technology service management using graph neural network
AssigneeSap SE
InventorSherwin Varghese
Life of this patent
Estimating the term…
Abstract
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.
Claims20
A computer-implemented method performed by a computer system comprising a memory and at least one hardware processor, the computer-implemented method comprising:
- computing a knowledge graph using a graph neural network and source data corresponding to historical incidents for which corresponding tickets were created in an online system, the source data comprising knowledge base article data comprising knowledge base articles used in solving the historical incidents, historical incident data comprising corresponding descriptions of the historical incidents, component data indicating components that were affected by the historical incidents, user data indicating a plurality of users who were involved in solving the historical incidents, and swarm data indicating swarms of users that were involved in solving the historical incidents;
- computing a new incident vector based on new incident data of a new incident for which a new ticket was created in the online system, the new incident data comprising a natural language description of the new incident, the new incident vector being computed using a first natural language understanding algorithm;
- for each one of the plurality of users, computing a corresponding updated user vector using the knowledge graph;
- and
- computing 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.
A system comprising:
- at least one hardware processor;
- and
- a non-transitory computer-readable medium storing executable instructions that, when executed, cause the at least one processor to perform computer operations comprising:
- computing a knowledge graph using a graph neural network and source data corresponding to historical incidents for which corresponding tickets were created in an online system, the source data comprising knowledge base article data comprising knowledge base articles used in solving the historical incidents, historical incident data comprising corresponding descriptions of the historical incidents, component data indicating components that were affected by the historical incidents, user data indicating a plurality of users who were involved in solving the historical incidents, and swarm data indicating swarms of users that were involved in solving the historical incidents;
- computing a new incident vector based on new incident data of a new incident for which a new ticket was created in the online system, the new incident data comprising a natural language description of the new incident, the new incident vector being computed using a first natural language understanding algorithm;
- for each one of the plurality of users, computing a corresponding updated user vector using the knowledge graph;
- and
- computing 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.
A non-transitory machine-readable storage medium tangibly embodying a set of instructions that, when executed by at least one hardware processor, causes the at least one processor to perform computer operations comprising:
- computing a knowledge graph using a graph neural network and source data corresponding to historical incidents for which corresponding tickets were created in an online system, the source data comprising knowledge base article data comprising knowledge base articles used in solving the historical incidents, historical incident data comprising corresponding descriptions of the historical incidents, component data indicating components that were affected by the historical incidents, user data indicating a plurality of users who were involved in solving the historical incidents, and swarm data indicating swarms of users that were involved in solving the historical incidents;
- computing a new incident vector based on new incident data of a new incident for which a new ticket was created in the online system, the new incident data comprising a natural language description of the new incident, the new incident vector being computed using a first natural language understanding algorithm;
- for each one of the plurality of users, computing a corresponding updated user vector using the knowledge graph;
- and
- computing 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.
Dependencies are inferred from the claim text.