US 11,907,840 B2Granted

Method and system for generating a dynamic sequence of actions

AssigneeVerizon Patent and Licensing Inc.

InventorsSumit Singh, Balagangadhara Thilak Adiboina, Adithya Umakanth, Ganesh Narasimman, Sambasiva R Bhatta, Anurag Pant

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Abstract

A device may receive historical data and real-time data associated with a troubleshooting service, identify, using a machine learning model, an optimal resolution based on the historical data and the real-time data, and identify, using a graph analytics model, an optimal path of actions based on the optimal resolution. The machine learning model may be trained to identify one of the set of historical issues associated with the unresolved issue, and identify the optimal resolution based on one of the set of historical resolutions associated with the one of the set of historical issues. The graph analytics model may be trained to generate a set of paths of actions based on the historical data, and identify the optimal path based on respective numbers of actions associated with the set of paths. The device may identify optimal action based on the optimal path and the prior action.

Claims20

Dependent claims

A method, comprising:

identifying, by a device and based on a model, an optimal resolution, based on historical data and real-time data, associated with an unresolved issue;
identifying, by the device and based on a graph analytics model, an optimal action to be performed based on the unresolved issue and the optimal resolution,
wherein the graph analytics model is trained to generate a set of paths of actions between the unresolved issue and the optimal resolution, identify an optimal path of actions based on respective numbers of actions associated with the set of paths, and identify the optimal action based on the optimal path of actions, and
wherein edges of an analytical graph, associated with the set of paths, are assigned respective weights to determine path lengths of the set of paths,
wherein the analytical graph comprises a set of nodes, corresponding to historical actions between the unresolved issue and the optimal resolution, interconnected by the edges, and
wherein identifying the optimal path of actions comprises selecting a subset of nodes, of the set of nodes that connect the unresolved issue and the optimal resolution based on the respective numbers of actions associated with the set of paths;
and
causing, by the device, the optimal action to be performed.

5 more elements

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

one or more instructions that, when executed by one or more processors of a device, cause the device to:
identify, based on a model, an optimal resolution, based on historical data and real-time data, associated with an unresolved issue;
identify, based on a graph analytics model, an optimal action to be performed based on the unresolved issue and the optimal resolution,
wherein the graph analytics model is trained to generate a set of paths of actions between the unresolved issue and the optimal resolution, identify an optimal path of actions based on respective numbers of actions associated with the set of paths, and identify the optimal action based on the optimal path of actions, and
wherein edges of an analytical graph, associated with the set of paths, are assigned respective weights to determine path lengths of the set of paths,
wherein the analytical graph comprises a set of nodes, corresponding to historical actions between the unresolved issue and the optimal resolution, interconnected by the edges, and
wherein identifying the optimal path of actions comprises selecting a subset of nodes, of the set of nodes that connect the unresolved issue and the optimal resolution based on the respective numbers of actions associated with the set of paths;
and
cause the optimal action to be performed.

6 more elements

Dependencies are inferred from the claim text.

Facts

Application: 18/061,189Filed 2 Dec 2022 · continuation
Priority: 25 Nov 2019
Published: US 2023/0100605 A130 Mar 2023
Granted: 20 Feb 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification11 codes

G06N 3/08
Neural networks: learning methods
G06N 3/044
Architecture, e.g. interconnection topology: recurrent networks, e.g. Hopfield networks
G06N 3/0442
Recurrent networks, e.g. Hopfield networks: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks

Family

Application16/694,891Parent of this continuation · filed 25 Nov 2019
US 11,526,751 B2Parent of this continuation · filed 13 Dec 2022
US 2023/0100605 A1Pre-grant publication · published 30 Mar 2023
US 2021/0158146 A1Published application
US 11,907,840 B2This patent

From the US filing history. Foreign family members aren’t listed.

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