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US 2021/0264251 A1Published application
Enhanced processing for communication workflows using machine-learning techniques
AssigneeOracle International Corporation
InventorsSudhakar Kalluri, Venkata Chandrashekar Duvvuri
Life of this patent
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Abstract
The present disclosure generally relates to evaluating communication workflows comprised of tasks using machine-learning techniques. More particularly, the present disclosure relates to systems and methods for generating a prediction of a task outcome of a communication workflow, generating a recommendation of one or more tasks to add to a partial communication workflow to complete the communication workflow, and generating a vector representation of a communication workflow.
Claims20
A computer-implemented method comprising:
- accessing a communication workflow including one or more tasks arranged in a sequential order, the communication workflow being configured to facilitate interactions with a set of user devices, each task of the one or more tasks including executable code that, upon execution, performs a function associated with the set of user devices, and the communication workflow being associated with one or more parameters that characterize each task of the one or more tasks of the communication workflow;
- defining a tree structure of the communication workflow, the tree structure including a plurality of nodes and one or more stages, wherein two nodes of the plurality of nodes of the tree structure are connected by at least one stage of the one or more stages, and wherein each task of the one or more tasks of the communication workflow corresponds to a node of the plurality of nodes or a stage of the one or more stages;
- inputting the tree structure into a machine-learning model, the inputting of the tree structure into the machine-learning model resulting in generating one or more rooted sub-trees for each node or stage of the tree structure of the communication workflow;
- and
- generating, as an output of the machine-learning model, a vector representation of the tree structure of the communication workflow, the vector representation being learned using the one or more rooted sub-trees of each node or stage of the tree structure.
2 more elements
A system, comprising:
- one or more processors;
- and
- a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more processors, cause the one or more processors to perform operations including:
- accessing a communication workflow including one or more tasks arranged in a sequential order, the communication workflow being configured to facilitate interactions with a set of user devices, each task of the one or more tasks including executable code that, upon execution, performs a function associated with the set of user devices, and the communication workflow being associated with one or more parameters that characterize each task of the one or more tasks of the communication workflow;
- defining a tree structure of the communication workflow, the tree structure including a plurality of nodes and one or more stages, wherein two nodes of the plurality of nodes of the tree structure are connected by at least one stage of the one or more stages, and wherein each task of the one or more tasks of the communication workflow corresponds to a node of the plurality of nodes or a stage of the one or more stages;
- inputting the tree structure into a machine-learning model, the inputting of the tree structure into the machine-learning model resulting in generating one or more rooted sub-trees for each node or stage of the tree structure of the communication workflow;
- and
- generating, as an output of the machine-learning model, a vector representation of the tree structure of the communication workflow, the vector representation being learned using the one or more rooted sub-trees of each node or stage of the tree structure.
5 more elements
A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause a processing apparatus to perform operations including:
- accessing a communication workflow including one or more tasks arranged in a sequential order, the communication workflow being configured to facilitate interactions with a set of user devices, each task of the one or more tasks including executable code that, upon execution, performs a function associated with the set of user devices, and the communication workflow being associated with one or more parameters that characterize each task of the one or more tasks of the communication workflow;
- defining a tree structure of the communication workflow, the tree structure including a plurality of nodes and one or more stages, wherein two nodes of the plurality of nodes of the tree structure are connected by at least one stage of the one or more stages, and wherein each task of the one or more tasks of the communication workflow corresponds to a node of the plurality of nodes or a stage of the one or more stages;
- inputting the tree structure into a machine-learning model, the inputting of the tree structure into the machine-learning model resulting in generating one or more rooted sub-trees for each node or stage of the tree structure of the communication workflow;
- and
- generating, as an output of the machine-learning model, a vector representation of the tree structure of the communication workflow, the vector representation being learned using the one or more rooted sub-trees of each node or stage of the tree structure.
2 more elements
Dependencies are inferred from the claim text.