US 2022/0044111 A1Published application

Automatic flow generation from customer tickets using deep neural networks

AssigneeSap SE

InventorsMithilesh Kumar Singh, Mohammad Saad Rashid, Warren Mark Fernandes

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Abstract

In an example embodiment, actionable flows are found from customer tickets by autonomously reading and understanding customer queries using neural networks. Specifically, in an example embodiment, a deep neural network is trained to be utilized at two separate stages in a flow generation process. In one stage, the neural network is used to identify a list of repetitive queries from a repository of customer tickets. In another stage, the neural network is used to identify actionable flows from query steps obtained from the list of repetitive queries.

Claims20

Dependent claims

A system comprising:

at least one hardware processor;
and
a non-transitory computer-readable medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising:
obtaining, from a ticket repository, a plurality of tickets, at least one ticket including text pertaining to a request;
converting the text of the plurality of tickets into a standard format;
feeding the converted text to a deep neural network trained to identify one or more queries in the text of a ticket, embed the one or more queries into a set of coordinates in a multidimensional space, and group the queries into groupings of semantically similar queries by calculating a similarity score between pairs of queries using a distance metric between the embedded sets of coordinates corresponding to the queries in the pair and then grouping queries together that have similarity scores among them that exceed a predetermined threshold;
and
for one or more groups of queries output by the deep neural network:
dividing one or more queries in the group into one or more steps by comparing the text of a query to an object dictionary storing business objects available to be acted upon or about in the system and dividing the text such that each step contains a single object from the object dictionary;
and
passing the steps in one or more queries in the group to the deep neural network, thus producing groupings of semantically similar steps;
and
for a given input ticket, producing a flow of steps comprising one or more steps, each step in the flow of steps corresponding to a different grouping of semantically similar steps representing a portion of text in the input ticket.

10 more elements

A method comprising:

obtaining, from a ticket repository, a plurality of tickets, at least one ticket including text pertaining to a request;
converting the text of the plurality of tickets into a standard format;
feeding the converted text to a deep neural network trained to identify one or more queries in the text of a ticket, embed the one or more queries into a set of coordinates in a multidimensional space, and group the queries into groupings of semantically similar queries by calculating a similarity score between pairs of queries using a distance metric between the embedded sets of coordinates corresponding to the queries in the pair and then grouping queries together that have similarity scores among them that exceed a predetermined threshold;
and
for one or more groups of queries output by the deep neural network:
dividing one or more queries in the group into one or more steps by comparing the text of a query to an object dictionary storing business objects available to be acted upon or about in the system and dividing the text such that each step contains a single object from the object dictionary;
and
passing the steps in one or more queries in the group to the deep neural network, thus producing groupings of semantically similar steps;
and for a given input ticket, producing a flow of steps comprising one or more steps, each step in the flow of steps corresponding to a different grouping of semantically similar steps representing a portion of text in the input ticket.

6 more elements

A non-transitory machine-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:

obtaining, from a ticket repository, a plurality of tickets, at least one ticket including text pertaining to a request;
converting the text of the plurality of tickets into a standard format;
feeding the converted text to a deep neural network trained to identify one or more queries in the text of a ticket, embed the one or more queries into a set of coordinates in a multidimensional space, and group the queries into groupings of semantically similar queries by calculating a similarity score between pairs of queries using a distance metric between the embedded sets of coordinates corresponding to the queries in the pair and then grouping queries together that have similarity scores among them that exceed a predetermined threshold;
and
for one or more groups of queries output by the deep neural network:
dividing one or more queries in the group into one or more steps by comparing the text of a query to an object dictionary storing business objects available to be acted upon or about in the system and dividing the text such that each step contains a single object from the object dictionary;
and
passing the steps in one or more queries in the group to the deep neural network, thus producing groupings of semantically similar steps;
and for a given input ticket, producing a flow of steps comprising one or more steps, each step in the flow of steps corresponding to a different grouping of semantically similar steps representing a portion of text in the input ticket.

6 more elements

Dependencies are inferred from the claim text.