Automated order troubleshooting

Patent metadata

Field	Value
Publication number	US-11308545-B2
Application number	US-201816159428-A
Country	US
Kind code	B2
Filing date	Oct 12, 2018
Priority date	Oct 12, 2018
Publication date	Apr 19, 2022
Grant date	Apr 19, 2022

Abstract

Examples of automated order troubleshooting are described. In an example embodiment, sales-specific data sources associated with at least one of a process, an organization, and an industry relevant for sales operations are monitored. From the monitored sales-specific data, an operation behavioral pattern is identified, based on predefined rules. Subsequently, a behavior model capturing the operation behavioral pattern is constructed using a pre-existing behavior model library. Using the behavior model, a potential event relating to an order received to be fulfilled using the sales operation is predicted, the potential event being indicative of an issue affecting the order. Accordingly, the issue affecting the order is proactively remediated to automatically troubleshoot the order.

First claim

What is claimed is:

1. An Order Troubleshooting Automation (OTA) system comprising:
a processor; and
a memory storing instructions, which when executed by the processor, cause the system to:
query online sales-specific data sources associated with at least one of a process, an organization, and an industry relevant for sales operations by accessing at least one of a social media website, an online web portal, and webpage associated with at least one of the process, the organization, and the industry relevant for the sales operations;
monitor the queried sales-specific data sources in real-time and retrieve sales-specific data from the queried sales-specific data sources;
identify an operation behavioral pattern from the sales-specific data, based on predefined rules, the predefined rules configured based on the at least one of the process, the organization, and the industry, wherein the operation behavioral pattern indicates current and uture trends in the at least one of the process, the organization, and the industry,
wherein, to identify the operation behavioral pattern, the processor is configured to perform an analysis on resource requirements of at least one of the process, the organization, and the industry by determining whether the at least one of the process, the organization, and the industry is sensitive to an one or a combination of latency, throughput, computational power, and memory, wherein the determination is performed based on characterization of workload of the at least one of the process, the organization, and the industry based on computational intensiveness and communication intensiveness of the workload;
upon identification of the operation behavioral pattern, construct a behavior model capturing the operation behavioral pattern using a pre-existing behavior model library and the sales-specific data, wherein pre-existing behavior model library includes a template for generating the behavior model by capturing the operation behavioral pattern;
receive, from a client, a request pertaining to an order for a service or a product;
deploy the behavior model to predict a potential event relating to the order received to be fulfilled using the sales operation, based on the behavior model, wherein the potential event is predicted by tracking one or more activities associated with the sales operation, wherein the potential event is indicative of an issue affecting the order, wherein, to predict the potential event, the processor is configured to:
determine whether the order conforms to the behavior model based on an operation behavioral pattern associated with the order by determining whether the order falls within a set of predetermined operation behavior patterns associated with the behavior model;
in response to the determination that the order does not conform to the behavior model:
predict the potential event relating to the order to be fulfilled using the sales operation; and
calibrate the behavior model by incorporating the operation behavioral pattern associated with the order into the behavior model; and

upon the prediction of the potential event, determine adjustments in resource requirements for the order based on the calibrated behavior model; and
wherein the order is modified according to the determined adjustments in the resource requirements to automatically remediate the issue affecting the order, thereby facilitating automatically troubleshooting of the order in an effective manner.

2. The OTA system as claimed in claim 1 , wherein the processor is to:
generate a hypothesis based on historical data; and
provide a recommendation based on the hypothesis to remediate the issue.

3. The OTA system as claimed in claim 1 , wherein the processor is to capture a plurality of steps of the sales operation, each of the plurality of steps being identified as an issue-causing juncture in the sales operation.

4. The OTA system as claimed in claim 1 , wherein the processor is to trace the sales operation from order placement to final delivery to predict the potential event.

5. The OTA system as claimed in claim 1 , wherein the processor is to:
investigate the order to determine whether the order conforms to the behavioral model or not, based on the operation behavioral pattern associated with the order;
generate a hypothesis for the order, when the order does not conform to the behavioral model; and
incorporate the hypothesis in the behavioral model to predict the potential event.

6. The OTA system as claimed in claim 1 , wherein the processor is to generate an alert for stakeholders in the sales operation, in response to the prediction of the potential event.

7. A method performed by a processor, the method comprising:
querying sales-specific data sources associated with at least one of a process, an organization, and an industry relevant for sales operations, wherein the online querying includes accessing at least one of a social media website, and online web portal, and webpage associated with at least one of the process, the organization, and the industry relevant for the sales operations;
monitoring the queried sales-specific data sources in real-time and retrieving sales-specific data from the queried. sales-specific data sources;
identifying an operation behavioral pattern from the sales-specific data, based on predefined rules, the predefined rules configured based on the at least one of the process, the organization, and the industry. wherein the operation behavioral pattern indicates current and future trends in the at least one of the process, the organization, and the industry,
wherein the identifying comprises performing an analysis on resource requirements of at least one of the process, the organization, and the industry by determining whether the at least one of the process, the organization, and the industry is sensitive to any one or a combination of latency, throughput, computational power, and memory, wherein the determination is performed based on characterization of workload of the at least one of the process, the organization, and the industry based on computational intensiveness and communication intensiveness of the workload;
upon identification of the operation behavioral pattern, constructing a behavior model capturing the operation behavioral pattern using a pre-existing behavior model library and the sales-specific data, wherein pre-existing behavior model library includes a template for generating the behavior model by capturing the operation behavioral pattern;
receiving, from a client, a request pertaining to an order for a service or a product;
predicting a potential event relating to the order received to be fulfilled using the sales operation, based on the behavior model, wherein the potential event is predicted by tracking one or more activities associated with the sales operation, wherein the potential event is indicative of an issue affecting the order, wherein the predicting the potential event comprises:
determining whether the order conforms to the behavior model based on an operation behavioral pattern associated with the order by determining whether the order falls within a set of predetermined operation behavior patterns associated with the behavior model;
in response to the determination that the order does not conform to the behavior model:
predicting the potential event relating to the order to be fulfilled using the sales operation; and
calibrating the behavior model by incorporating the operation behavioral pattern associated with the order no the behavior model, and upon the prediction of the potential event, determining adjustments in resource requirements for the order based on the calibrated behavior model,

wherein the order is modified according to the determined adjustments

Assignees

• Accenture Global Solutions Ltd

Inventors

• Li Chung-Sheng
• Munguia Tapia Emmanuel
• Fan Jingyun
• Moffat Danielle
• Connors Colin
• Moharreri Kayhan

Classifications

• G06Q30/0635 Primary

  replenishment orders; recurring orders · CPC title

• G06N20/00

  Machine learning · CPC title

• G06Q10/0635 Primary

  Risk analysis of enterprise or organisation activities · CPC title

• G06Q10/087

  Inventory or stock management, e.g. order filling, procurement or balancing against orders · CPC title

• G06Q10/0874

  Inventory fulfillment (shipping G06Q10/083) · CPC title