Patent Description:
This disclosure generally relates to integrations in a system.

Systems may include different applications and services that are integrated together. For example, a system may use integrations to bind together many kinds of applications to work in conjunction in various automated operations. However, integrations may experience failures. For example, an integration between two different applications may fail when a process that exchanges data between the applications fails.

<CIT> discloses a computer-implemented method that includes generating, by a processor of a problem-tracking system, a problem ticket. The problem ticket describes a problem encountered by a first user, and the problem ticket is assigned to at least one qualified user to resolve. The method also includes receiving additional information relating to the problem from at least one second user. The method also includes storing the received additional information in association with the problem ticket. The receiving and the storing of the additional information occurs while the problem ticket is waiting to be resolved by the at least one qualified user. The method also includes providing the stored received additional information and the problem ticket to the at least one qualified user.

<CIT> discloses a system and method for predictive ticketing in information technology (IT) systems. The method includes extracting a plurality of features from monitoring data related to an IT system, wherein the plurality of features includes at least one incident parameter, wherein the monitoring data includes machine-generated textual data; applying a machine learning model to the extracted plurality of features, wherein the machine learning model is configured to output a suitable insight for an incident represented by the at least one incident parameter, wherein the suitable insight is selected from among a plurality of historical insights; and generating a predictive ticket based on the suitable insight, wherein the predictive ticket includes a textual description of an expected future symptom in the IT system.

In general, an aspect of the subject matter described in this specification may involve a process for resolving contextual tickets. A contextual ticket may describe an issue that was experienced by a user of a system. For example, a contextual ticket may describe that a user was unable to send a meter location change to a meter data management system.

A system may provide a guided process for creation of a contextual ticket. For example, the system may guide a user to create a contextual ticket in response to the user being unable to send a meter location change to a meter data management system. The guided contextual ticket creation may enable users of the system to create highly relevant contextual tickets, where contextual data in the tickets help increase speed of issue resolution and provide a foundation for automation in resolving the issue.

The contextual data in the contextual tickets can include data that specifies one or more of integration systems, specific flows, application programming interface end points, real time integration status, message flow information, infrastructure status, impact score, possible root causes, possible solutions, and correlated past tickets.

The system may generate a ticket knowledge graph that represents the contextual data of the contextual ticket, and may provide the graph to a machine-learning trained action determination engine. The engine may output an indication of an action for resolving the issue indicated by the contextual ticket, and the system may then initiate the action indicated by the engine.

One advantage may be the issues may be more quickly resolved based on automatic determination and initiation of actions to resolve the issues. Another advantage may be that actions that resolve the issues may be more accurately determined by the machine-learning trained action determination engine than by a human operator. Yet another advantage is that non-standardized information may be standardized, and then used by a machine-learning trained model that requires standardized input.

In some aspects, the subject matter described in this specification may be embodied in methods that may include the actions of receiving a request from a user to generate a contextual ticket that indicates an issue with an integration for sending a meter location change to a meter data management system, obtaining baseline information for the issue, wherein the baseline information comprises a description provided by the user and a navigation point for the issue, generating, based on the baseline information, a ticket knowledge graph, comprising: generating extracted information based on the baseline information, wherein the extracted information comprises one or more of integration status, technical metadata and integration type; and generating the ticket knowledge graph based on the baseline information and the extracted information; providing the ticket knowledge graph to a machine-learning trained action determination engine, receiving, from the machine-learning trained action determination engine, an indication of an action for resolving the issue, and automatically providing, based on the indication of the action for resolving the issue, the action to a particular engine to resolve the issue.

Other implementations of this and other aspects include corresponding systems, apparatus, and computer programs, may be configured to perform the actions of the methods, encoded on computer storage devices. A system of one or more computers can be so configured by virtue of software, firmware, hardware, or a combination of them installed on the system that in operation cause the system to perform the actions. One or more computer programs can be so configured by virtue of having instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.

The foregoing and other embodiments can each optionally include one or more of the following features, alone or in combination. For instance, in some aspects generating, based on the baseline information, a ticket knowledge graph includes generating extracted information based on the baseline information, generating calculated information based on the baseline information and the extracted information, generating correlated information based on the calculated information, generating predicted information based on the correlated information, and generating a ticket knowledge graph based on the baseline information, extracted information, the calculated information, the correlated information, and the predicted information.

In certain aspects, generating a ticket knowledge graph based on the baseline information, the extracted information, the calculated information, the correlated information, and the predicted information includes generating the ticket knowledge graph to include nodes that represent the baseline information, the extracted information, the calculated information, the correlated information, and the predicted information. In some aspects, the ticket knowledge graph includes links that represent relationships between the nodes.

In some aspects, a root node of the ticket knowledge graph represents a navigational point of the issue. In certain aspects, generating extracted information based on the baseline information includes obtaining a common information model for integration and generating the extracted information from the common information model for integration based on the baseline information.

In certain aspects, providing the ticket knowledge graph to a machine-learning trained action determination engine includes providing the baseline information to a first action predictor engine, providing the extracted information to a second action predictor engine, providing the calculated information to a third action predictor engine, providing the correlated information to a fourth action predictor engine, providing the predicated information to a fifth action predictor engine, receiving respective actions from the first action predictor engine, the second action predictor engine, the third action predictor engine, the fourth action predictor engine, and the fifth action predictor engine, providing the respective actions to an action selection engine, and receiving a selected action from the action selection engine.

In some aspects, generating, based on the baseline information, a ticket knowledge graph includes generating a standardized data structure that represents at least a portion of the extracted information, a portion of the predicted information, and at least a portion of the calculated information and providing the standardized data structure to a machine-learning regression model, where the machine-learning regression model is trained based on standardized data structures and labeled outputs that represent values for at least a second portion of the predicted information.

The details of one or more implementations are set forth in the accompanying drawings and the description, below. Other potential features and advantages of the disclosure will be apparent from the description and drawings, and from the claims.

<FIG> illustrates a diagrams of an exemplary system <NUM> for guided contextual ticket creation. The system <NUM> includes a navigation point identifier <NUM> that identifies a navigation point, a contextual data extractor <NUM> that extracts contextual data based on the navigation point, and a ticket generator <NUM> that generates a contextual ticket based on the contextual data. The navigation point identifier <NUM>, the contextual data extractor <NUM>, and the ticket generator <NUM> may be implemented as one or more computer systems.

The navigation point identifier <NUM> may receive a request from a user to generate a ticket regarding a particular integration that has an issue, obtain a structured representation model of integrations in the system including the particular integration, and identify the navigation point that corresponds to the particular integration. For example, a user may navigate through a graphical representation of the model of integrations and request to submit a ticket for an integration for sending a meter location change to a meter data management system. A navigation point may be a specific user interface in an integration navigator. An integration navigator may be an application that provides a graphical user interface that users may use to view various integrations and create contextual tickets for issues.

In the example, the navigation point identifier <NUM> may receive the request, search through the model of integrations for a particular link that corresponds to the integration for sending a meter location change to a meter data management system, and then output text that specifies the particular link.

The contextual data extractor <NUM> extracts contextual data based on the navigation point that was identified by the navigation point identifier <NUM>. For example, the contextual data extractor may extract specific flows, application programming interface end points, real time integration status, message flow information, infrastructure status, impact score, possible root causes, possible solutions, and correlated past tickets that correspond to the particular link that corresponds to the integration for sending a meter location change to a meter data management system.

The contextual data extractor <NUM> extracts contextual data based on (i) extracting static contextual data based on the navigation point, (ii) extracting dynamic contextual data based on the static contextual data, and (iii) determining calculated contextual data based on the dynamic contextual data. For example, the contextual data extractor <NUM> may (i) extract static contextual data that includes specific flows, application programming interface end points, (ii) from the specific flows, application programming interface end points then extract dynamic contextual data that includes real time integration status, message flow information, infrastructure status, and (iii) from the real time integration status, message flow information, infrastructure status then calculate contextual data that includes impact score, possible root causes, possible solutions, and correlated past tickets that correspond to the particular link that corresponds to the integration for sending a meter location change to a meter data management system.

The ticket generator <NUM> generates a contextual ticket based on the contextual data. For example, the ticket generator <NUM> may receive specific flows, application programming interface end points, real time integration status, message flow information, infrastructure status, impact score, possible root causes, possible solutions, and correlated past tickets that correspond to the particular link that corresponds to the integration for sending a meter location change to a meter data management system, and, in response, generate a contextual ticket that includes a link that corresponds to the integration for sending a meter location change to a meter data management system, a user description of the issue, technical metadata, recent tickets, relevant documentation link, integration status, contact information, and probably root cause.

<FIG> illustrates a diagram of another exemplary model <NUM> for guided contextual ticket creation. The model <NUM> includes a business process area <NUM> impacted by an issue, an application <NUM> impacted by the issue, an integration <NUM> impacted by the issue, technical information <NUM>, a ticketing tool <NUM>, and an integration navigator <NUM>.

The business process area <NUM> may be a collection of related, structured activities, people, and/or equipment in which a specific sequence produces a service or product for a particular customer or customers. For example, a utility customer information system may include six high level business process areas of federal insurance contributions act, billing, device, sales service, marketing, and digital.

The application <NUM> may be computer software that supports a process. For example, the application <NUM> may be a service order system, a meter reading system, etc. The integration <NUM> may be a data exchange with an application. For example, applications and a core system may have fifteen output and ten inbound integrations between each other.

Technical information <NUM> may be information associated with a specific integration. For example, technical information may be a Uniform Resource Locator (URL), security information, logging index, server, etc. A ticketing tool <NUM> may be a tool that resolves tickets. For example, the ticketing tool <NUM> may be software that automatically determines an action to resolve an issue and then initiates the action.

The integration navigator <NUM> may enable users to start by displaying business process areas and receiving a user selection of one, after selection of a business area then displaying connected applications and receiving a user selection of one, after selection of a connected application then displaying integration lists and receiving a user selection of one, after selection of an integration list then displaying a corresponding integration flow, integration sequence, and integration metadata.

An integration list for an application may refer to a list of integrations used by an application. An integration flow may be a visual depiction of end to end detailed message flow of an integration. An integration sequence may be detailed sequential steps of a specific integration flow. Integration metadata may be technical data associated with a specific integration flow.

<FIG> illustrates a diagram of example stages <NUM> for resolving a contextual ticket. The stages <NUM> may include integration ticket creation <NUM>, obtain baseline information <NUM>, generate extracted information <NUM>, generate calculated information <NUM>, generate correlated information <NUM>, generate predicted information <NUM>, and action space determination <NUM>.

The integration ticket creation <NUM> may be user initiated from a graphical user interface of an integration navigator, proactively generated by the integration navigator without an explicit request to generate by the user, or automatically generated from middleware monitoring.

The baseline information <NUM> may be foundational information provided by the user and integration navigator when a user tries to create a ticket for any suspected issue. The baseline information may indicate a qualitative description of the issue as provided by the user, and a navigational path from the integration navigator system which helps in understanding the exact integration, connected application, and flow impacted based on a common information model for integration (CCIMI) model. The CCIMI model is described in more detail below in regards to <FIG>. The navigational path may be the previously described navigation point.

The extracted information may be generated by getting relevant information from various middleware platforms, connected systems, and document repository systems directly related to the components for which the incident has been raised. The extracted information may represent the most direct relevant data related to the system, process and flows impacted. The system may generate the extracted information by making application programming interface (API) calls, database queries, log extraction, or any other data retrieval techniques. Some example of data elements extracted may be integration status, technical metadata of the integration, relevant documentation, support schedule, integration type, dependency, and issue raised by, etc..

The calculated information may be generated based on the user-provided information and extracted information, and may be the result of various calculation operations to gather additional secondary data relevant to the incident. The system may be able to add the calculated information based on intelligent calculation functions by getting a reference from the baseline and the extracted information. Some examples of a calculated information may be raised by manager, number of tickets raised by user in last predetermined number of days, average past resolution time for similar tickets raised by the user, recent tickets, contact information, net promotor score (NPS) for the group, user sentiment, and knowledge graph of relevant documentation, etc..

The correlated information may be generated based on gathering correlated information using the baselined, extracted, and calculated information. The correlated information may represent information that is not directly connected to the incident but may help understand the impact or root cause of the issues in a broader context. Correlated information may include recently deployed changes, related key performance indicators (KPI), related KPI trending, related active tickets, and related active tickets root cause, etc..

Predicted information may be added through sophisticated machine learning algorithms. These machine learning models may gather predicted information dependent on the data from the previous stages. Predicted information may include Service Level Agreement expiration probability, priority, business impact, best assigned person, resolution time prediction, and probable root cause, etc..

The action space that may be determined includes actions that may be determined to resolve the issue. For example, the action space may include executing a bot or workflow to resolve the issue, determining a resolution confidence score, determining whether a callout is required, and determining whether leadership communication is required.

<FIG> illustrates a diagram of a ticket knowledge graph <NUM>. The ticket knowledge graph <NUM> includes various nodes that represent data. The nodes may include baseline information nodes 410A-F, extracted information nodes 420A-G, calculated information nodes 430A-H, correlated information nodes 440A-D, and predicted information nodes 450A-F. The root node of the ticket knowledge graph <NUM> may be the baseline information node 410A that represents the navigational path.

Links between the nodes may represent relationships between the nodes. For example, node 410A may be linked to the node 420E that represents the relevant documentation by a link that indicates that the relevant documentation is determined from the navigational path with an integration navigator query, the node 420E may be linked to the node 420F that represents support schedule by another link that indicates that the support schedule is determined from the relevant documentation with an integration navigator query, and the node 420F may be linked to the node 430J that represents contact info by a link that indicates that the contact info is determined from the support schedule with data mining, and the node 430J may be linked to the node 450A that represents best assigned person by a link that indicates that the best assigned person is determined from the contact info with a calculation function.

<FIG> illustrates a diagram of an exemplary action determination architecture for resolving a contextual ticket. The action determination architecture may provide a robust framework that utilizes the ticket knowledge graph. Actions that resolve the issue indicated by the contextual ticket may be predicted based on each type of information in the ticket knowledge graph, and an action determination engine may determine the final output by taking recommendations from each type into consideration.

For example, a baseline information engine <NUM> may obtain the baseline information and provide the baseline information to the action predictor engine 560A, which outputs a predicted action to the action selection engine <NUM>. An extracted information engine <NUM> may generate the extracted information from the baseline information and provide the extracted information to the action predictor engine 560B, which outputs a predicted action to the action selection engine <NUM>. A calculated information engine <NUM> may generate the calculated information from the extracted information and provide the calculated information to the action predictor engine 560C, which outputs a predicted action to the action selection engine <NUM>. A correlated information engine <NUM> may generate the correlated information from the calculated information and provide the correlated information to the action predictor engine 560D, which outputs a predicted action to the action selection engine <NUM>. A predicted information engine <NUM> may generate the predicted information from the correlated information and provide the predicted information to the action predictor engine 560E, which outputs a predicted action to the action selection engine <NUM>. The action selection engine <NUM> may receive all the predicted actions from the action predictor engines 560A-E and output a final action.

The action predictor engines 560A-E may be machine-learning trained models that are trained to predict an action that will resolve an issue based on corresponding types of information in the tree knowledge graph <NUM>. For example, the action predictor engine 560A may be trained to receive a tree knowledge graph that only includes baseline information and output an action that is predicted to resolve an issue with the baseline information, where the action predictor engine 560A is trained with training data that includes respective sets of baseline information and corresponding actions that resolve issues with the sets of baseline information. In another example, the action predictor engine 560B may be trained to receive a tree knowledge graph that includes both baseline information and extracted information, and output an action that is predicted to resolve an issue with both the baseline information and extracted information, where the action predictor engine 560B is trained with training data that includes respective sets of baseline information, extracted information, and corresponding actions that resolve issues with the sets of baseline information and extracted information.

The action selection engine <NUM> may be a machine-learning trained model that is trained to select an action that will resolve an issue based on a set of input actions. For example, the action selection engine <NUM> may be trained to receive a set of five actions, each from one of the action predictor engines 560A-E, and select one of the actions from the set. In some implementations, the action selection engine <NUM> may be trained to select a particular action based on training data that indicates sets of input actions labeled with corresponding actions to select. For example, the training data may indicate that a set of Action A, Action B, Action A, Action C, Action D is labeled with a selection of action C. In another example, the training data may indicate that a set of Action A, Action B, Action B, Action C, Action D is labeled with a selection of action D. In some implementations, the action predictor engines 560A-E may each output a confidence score for an action, and the action selection engine <NUM> may be trained with training data that indicates sets of actions, confidence scores for each action, and labels of corresponding actions to select for each set.

<FIG> illustrates a diagram of an example common information model for integration (CCMI). The CCMI may be a structured representation model of a distributed integration landscape and define relationships between each component in the model. For example, the CCMI may include representations of business process areas, connected applications, integrations, integration flows, integration sequences, integration metadata, integration documentation, and integration contacts.

<FIG> illustrates a flowchart of an example process <NUM> for resolving a contextual ticket. The process <NUM> may be performed by the system <NUM> shown in <FIG> or another system. Briefly, and as will be described in more detail below, the process <NUM> includes receiving a request from a user to generate a contextual ticket that indicates an issue with an integration (<NUM>), obtaining baseline information for the issue (<NUM>), generating, based on the baseline information, a ticket knowledge graph (<NUM>), providing the ticket knowledge graph to a machine-learning trained action determination engine (<NUM>), receiving, from the machine-learning trained action determination engine, an indication of an action for resolving the issue (<NUM>), and initiating, based on the indication of the action for resolving the issue, the action for resolving the issue (<NUM>).

The process <NUM> includes receiving a request from a user to generate a contextual ticket that indicates an issue with an integration (<NUM>). For example, the navigation point identifier <NUM> may receive a request from a user to create a ticket for an issue while the user is viewing a particular integration in the integration navigator.

The process <NUM> includes obtaining baseline information for the issue (<NUM>). For example, the navigation point identifier <NUM> may obtain baseline information for the issue. In some implementations, obtaining baseline information for the issue may include obtaining a description provided by the user and identifying a navigation point for the issue. For example, the navigation point identifier <NUM> may extract the user entered description of "meter location change didn't change location" from the request and identify a navigation point in the integration navigator for the meter location change.

The process <NUM> includes generating, based on the baseline information, a ticket knowledge graph (<NUM>). For example, the contextual data extractor <NUM> may obtain the navigation point and description, and from both generate the ticket knowledge graph <NUM>.

In some implementations, generating, based on the baseline information, a ticket knowledge graph includes generating extracted information based on the baseline information, generating calculated information based on the baseline information and the extracted information, generating correlated information based on the calculated information, generating predicted information based on the correlated information, and generating a ticket knowledge graph based on the baseline information, extracted information, the calculated information, the correlated information, and the predicted information. For example, the contextual data extractor <NUM> may perform the stages <NUM>-<NUM> shown in <FIG> to iteratively obtain the different types of information based on the previously obtained types of information, and then generate the ticket knowledge graph <NUM> from the different types of information.

In some implementations, generating a ticket knowledge graph based on the baseline information, the extracted information, the calculated information, the correlated information, and the predicted information includes generating the ticket knowledge graph to include nodes that represent the baseline information, the extracted information, the calculated information, the correlated information, and the predicted information. For example, the contextual data extractor <NUM> may generate the ticket knowledge graph <NUM> that includes baseline information nodes 410A-F, extracted information nodes 420A-G, calculated information nodes 430A-H, correlated information nodes 440A-D, and predicted information nodes 450A-F.

In some implementations, generating, based on the baseline information, a ticket knowledge graph includes generating a standardized data structure that represents at least a portion of the extracted information, a portion of the predicted information, and at least a portion of the calculated information and providing the standardized data structure to a machine-learning regression model, where the machine-learning regression model is trained based on sets of standardized data structures and labeled outputs that represent values for at least a second portion of the predicted information. For example, the contextual data extractor <NUM> may provide information of raised by, integration status, support schedule, probable root cause, priority, best assigned person, average past resolution time, and sentiment analysis of the ticket to a data pre-processing engine that standardizes the information into a standardized data structure, and the standardized data structure may then be provided to a machine-learning regression model that outputs a SLA expiration probability of either low, medium, or high based on the standardized data structure. In some implementations, generating a standardized data structure may include determining a name of a role of a user that raised the ticket and generating the standardized data structure to include the name of the role of the user instead of a name of the user that raised the ticket.

The process <NUM> includes providing the ticket knowledge graph to a machine-learning trained action determination engine (<NUM>). For example, the ticket generator <NUM> may provide the contents of the ticket to the action predictor engines 560A-E, which then provide outputs to an action selection engine <NUM>.

In some implementations, providing the ticket knowledge graph to a machine-learning trained action determination engine includes providing the baseline information to a first action predictor engine, providing the extracted information to a second action predictor engine, providing the calculated information to a third action predictor engine, providing the correlated information to a fourth action predictor engine, providing the predicated information to a fifth action predictor engine, receiving respective actions from the first action predictor engine, the second action predictor engine, the third action predictor engine, the fourth action predictor engine, and the fifth action predictor engine, providing the respective actions to an action selection engine, and receiving a selected action from the action selection engine.

For example, the baseline information may be provided to the action predictor engine 560A which outputs a first predicted action, the extracted information may be provided to the action predictor engine 560B which outputs a second predicted action, the calculated information may be provided to the action predictor engine 560C which outputs a third predicted action, the correlated information may be provided to the action predictor engine 560D which outputs a fourth predicted action, the predicted information may be provided to the action predictor engine 560E which outputs a fifth predicted action, and the action selection engine <NUM> may receive the five predicted actions and select to output a particular action to resolve the issue based on the five predicted actions. The process <NUM> includes receiving, from the machine-learning trained action determination engine, an indication of an action for resolving the issue (<NUM>). For example, the ticket generator <NUM> may receive an indication of an action performed by a particular workflow.

The process <NUM> includes initiating, based on the indication of the action for resolving the issue, the action for resolving the issue (<NUM>). For example, the ticket generator <NUM> may, in response to receiving the indication of the action and without user input after receiving the indication, automatically provide the contextual ticket to a particular engine that performs the particular workflow. In another example, the ticket generator <NUM> may, in response to receiving the indication of the action of notify leadership and without user input after receiving the indication, automatically generate and transmit a notification to leaders regarding the issue.

<FIG> illustrates a schematic diagram of an exemplary generic computer system <NUM>. The system <NUM> may be implemented on the system <NUM>.

The system <NUM> includes a processor <NUM>, a memory <NUM>, a storage device <NUM>, and an input/output device <NUM>. Each of the components <NUM>, <NUM>, <NUM>, and <NUM> are interconnected using a system bus <NUM>. The processor <NUM> is capable of processing instructions for execution within the system <NUM>. In one implementation, the processor <NUM> is a single-threaded processor. In another implementation, the processor <NUM> is a multi-threaded processor. The processor <NUM> is capable of processing instructions stored in the memory <NUM> or on the storage device <NUM> to display graphical information for a user interface on the input/output device <NUM>.

In various different implementations, the storage device <NUM> may be a floppy disk device, a hard disk device, a solid state drive, an optical disk device, a tape device, universal serial bus stick, or some other storage device.

The features described can be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. The apparatus can be implemented in a computer program product tangibly embodied in an information carrier, e.g., in a machine-readable storage device, for execution by a programmable processor; and method steps can be performed by a programmable processor executing a program of instructions to perform functions of the described implementations by operating on input data and generating output. The described features can be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. A computer program is a set of instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result.

The elements of a computer are a processor for executing instructions and one or more memories for storing instructions and data.

To provide for interaction with a user, the features can be implemented on a computer having a display device such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user and a keyboard and a pointing device such as a mouse or a rail trackball by which the user can provide input to the computer.

Claim 1:
A computer-implemented method comprising:
receiving a request from a user to generate a contextual ticket that indicates an issue with an integration for sending a meter location change to a meter data management system;
obtaining baseline information (<NUM>; <NUM>) for the issue, wherein the baseline information comprises a description provided by the user and a navigation point for the issue;
generating, based on the baseline information, a ticket knowledge graph (<NUM>), the generating comprising:
generating extracted information (<NUM>) based on the baseline information, wherein the extracted information comprises one or more of integration status, technical metadata and integration type; and
generating the ticket knowledge graph based on the baseline information and the extracted information;
providing the ticket knowledge graph to a machine-learning trained action determination engine (<NUM>);
receiving, from the machine-learning trained action determination engine, an indication of an action for resolving the issue; and
automatically providing, based on the indication of the action for resolving the issue, the action to a particular engine to resolve the issue.