Patent Description:
Installation, upgrade, maintenance of QCS scanner requires skilled domain expertise since it involves handling of a radioactive source, precise work with sequence of procedural steps, and should be error free. The people in the industry with the expertise dealing with the QCS scanner are growing older and reaching retirement. New TAC and service regions are facing difficulties supporting the QCS scanner due to a competency gap and lack of expertise of knowledge. The troubleshooting of the QCS scanner requires domain expertise to identify a potential issue precisely and to fix the potential issue. Also, the time required to rectify the issue may vary depending on the field expertise. The training of the QCS scanner requires time on a physical scanner and physical environment for training purposes.

Examples of currently used systems can be found in the following documents:.

This disclosure provides systems and methods for <NUM>° assistance for a QCS scanner with mixed reality (MR) and machine learning technology.

In a first embodiment, a method according to claim <NUM> is provided.

In a second embodiment, an apparatus according to claim <NUM> is provided.

In a third embodiment, a non-transitory medium according to claim <NUM> is provided.

Those skilled in the art will understand that the principles of the present disclosure may be implemented in any type of suitably arranged device or system.

<FIG> illustrates an example industrial process control and automation system <NUM> according to this disclosure. As shown in <FIG>, the system <NUM> includes various components that facilitate production or processing of at least one product or other material. For instance, the system <NUM> can be used to facilitate control over components in one or multiple industrial plants. Each plant represents one or more processing facilities (or one or more portions thereof), such as one or more manufacturing facilities for producing at least one product or other material. In general, each plant may implement one or more industrial processes and can individually or collectively be referred to as a process system. A process system generally represents any system or portion thereof configured to process one or more products or other materials in some manner.

In <FIG>, the system <NUM> includes one or more sensors 102a and one or more actuators 102b. The sensors 102a and actuators 102b represent components in a process system that may perform any of a wide variety of functions. For example, the sensors 102a could measure a wide variety of characteristics in the process system, such as pressure, temperature, flow rate, basis weight, moisture, ash, caliper, etc. Also, the actuators 102b could alter a wide variety of characteristics in the process system. Each of the sensors 102a includes any suitable structure for measuring one or more characteristics in a process system. Each of the actuators 102b includes any suitable structure for operating on or affecting one or more conditions in a process system.

At least one network <NUM> is coupled to the sensors 102a and actuators 102b. The network <NUM> facilitates interaction with the sensors 102a and actuators 102b. For example, the network <NUM> could transport measurement data from the sensors 102a and provide control signals to the actuators 102b. The network <NUM> could represent any suitable network or combination of networks. As particular examples, the network <NUM> could represent at least one Ethernet network, electrical signal network (such as a HART or FOUNDATION FIELDBUS network), pneumatic control signal network, or any other or additional type(s) of network(s).

The system <NUM> also includes various controllers <NUM>. The controllers <NUM> can be used in the system <NUM> to perform various functions in order to control one or more industrial processes. For example, a first set of controllers <NUM> may use measurements from one or more sensors 102a to control the operation of one or more actuators 102b. A second set of controllers <NUM> could be used to optimize the control logic or other operations performed by the first set of controllers. A third set of controllers <NUM> could be used to perform additional functions.

Controllers <NUM> are often arranged hierarchically in a system. For example, different controllers <NUM> could be used to control individual actuators, collections of actuators forming machines, collections of machines forming units, collections of units forming plants, and collections of plants forming an enterprise. A particular example of a hierarchical arrangement of controllers <NUM> is defined as the "Purdue" model of process control. The controllers <NUM> in different hierarchical levels can communicate via one or more networks <NUM> and associated switches, firewalls, and other components.

Each controller <NUM> includes any suitable structure for controlling one or more aspects of an industrial process. At least some of the controllers <NUM> could, for example, represent proportional-integral-derivative (PID) controllers or multivariable controllers, such as Robust Multivariable Predictive Control Technology (RMPCT) controllers or other types of controllers implementing model predictive control or other advanced predictive control. As a particular example, each controller <NUM> could represent a computing device running a real-time operating system, a WINDOWS operating system, or other operating system.

Operator access to and interaction with the controllers <NUM> and other components of the system <NUM> can occur via various operator consoles <NUM>. Each operator console <NUM> could be used to provide information to an operator and receive information from an operator. For example, each operator console <NUM> could provide information identifying a current state of an industrial process to the operator, such as values of various process variables and warnings, alarms, or other states associated with the industrial process. Each operator console <NUM> could also receive information affecting how the industrial process is controlled, such as by receiving setpoints or control modes for process variables controlled by the controllers <NUM> or other information that alters or affects how the controllers <NUM> control the industrial process.

Multiple operator consoles <NUM> can be grouped together and used in one or more control rooms <NUM>. Each control room <NUM> could include any number of operator consoles <NUM> in any suitable arrangement. In some embodiments, multiple control rooms <NUM> can be used to control an industrial plant, such as when each control room <NUM> contains operator consoles <NUM> used to manage a discrete part of the industrial plant.

Each operator console <NUM> includes any suitable structure for displaying information to and interacting with an operator. For example, each operator console <NUM> could include one or more processing devices <NUM>, such as one or more processors, microprocessors, microcontrollers, field programmable gate arrays, application specific integrated circuits, discrete logic devices, or other processing or control devices. Each operator console <NUM> could also include one or more memories <NUM> storing instructions and data used, generated, or collected by the processing device(s) <NUM>. Each operator console <NUM> could further include one or more network interfaces <NUM> that facilitate communication over at least one wired or wireless network, such as one or more Ethernet interfaces or wireless transceivers.

In accordance with this disclosure, a technique is provided for <NUM>° assistance for a QCS scanner with mixed reality (MR) and machine learning technology. One or more components of the system <NUM> (e.g., an operator console <NUM>) could be configured to perform one or more operations associated with this technique.

Although <FIG> illustrates one example of an industrial process control and automation system <NUM>, various changes may be made to <FIG>. For example, industrial control and automation systems come in a wide variety of configurations. The system <NUM> shown in <FIG> is meant to illustrate one example operational environment in which a pressure sensor could be used.

<FIG> illustrates an example device for <NUM>° assistance for a QCS scanner with mixed reality (MR) and machine learning technology according to this disclosure. In particular, <FIG> illustrates an example computing device <NUM>. In some embodiments, the computing device <NUM> could denote an operator station, server, a remote server or device, or a mobile device. The computing device <NUM> could be used to run applications. For ease of explanation, the computing device <NUM> is described as being used in the system <NUM> of <FIG>, although the device could be used in any other suitable system (whether or not related to industrial process control and automation).

As shown in <FIG>, the computing device <NUM> includes at least one processor <NUM>, at least one storage device <NUM>, at least one communications unit <NUM>, and at least one input/output (I/O) unit <NUM>. Each processor <NUM> can execute instructions, such as those that may be loaded into a memory <NUM>. Each processor <NUM> denotes any suitable processing device, such as one or more microprocessors, microcontrollers, digital signal processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or discrete circuitry.

The memory <NUM> and a persistent storage <NUM> are examples of storage devices <NUM>, which represent any structure(s) configured to store and facilitate retrieval of information (such as data, program code, and/or other suitable information on a temporary or permanent basis). The memory <NUM> may represent a random access memory or any other suitable volatile or non-volatile storage device(s). The persistent storage <NUM> may contain one or more components or devices supporting longer-term storage of data, such as a read-only memory, hard drive, Flash memory, or optical disc.

The communications unit <NUM> supports communications with other systems or devices. For example, the communications unit <NUM> could include at least one network interface card or wireless transceiver facilitating communications over at least one wired or wireless network. The communications unit <NUM> may support communications through any suitable physical or wireless communication link(s).

The I/O unit <NUM> allows for input and output of data. For example, the I/O unit <NUM> may provide a connection for user input through a keyboard, mouse, keypad, touchscreen, gesture control, image processing, or other suitable input device. The I/O unit <NUM> may also send output to a display, printer, or other suitable output device.

<FIG> illustrates an exemplary QCS scanner system <NUM> of <NUM>° assistance for a QCS scanner <NUM> with mixed reality (MR) and machine learning technology according to this disclosure. The embodiment of the exemplary QCS scanner system <NUM> illustrated in <FIG> is for illustration only. <FIG> does not limit the scope of this disclosure to any particular implementation.

The QCS scanner system <NUM> provides for a mixed reality (MR) (augmented reality (AR)/virtual reality (VR), machine learning and Chatbot solutions resolving potential issues. Using the MR, the commission of the QCS system <NUM> is made safer, easier and more user friendly by augmenting the physical conditions with interactive guidance for installation and upgrading of the QCS scanners.

The QCS system <NUM> integrates QCS scanner diagnostic messages and fault information with the HoloLens <NUM>. The QCS system <NUM> receives the solution from a local/centralized solution center and enabling an interactive Chatbot and machine learning for troubleshooting.

The QCS system creates a virtual training for the QCS scanner using VR and AR, which reduces the overall cost of training and physical hardware. The QCS system provides instruction on safe handling of a radioactive source under a hazardous environment. The QCS system creates a mimic of scanner components, which provides the detail information about wiring details, equipment location identification, checkpoints and more.

The AR solution provides an augmented physical scanner with real time data for troubleshooting. The AR solution augments the step-by-step procedure for installing a QCS scanner. The AR solution can upload real-time scanner status.

The machine learning and Chatbot <NUM> provides a solution for easy troubleshooting based on previous data with interactive live chat sessions with machine and expert chanters. The machine learning and Chatbot <NUM> can record the issue and their resolving steps for future use.

The VR solution can provide an alternate means for practicing installation and commissioning of QCS scanner without the need for access to an expensive physical component. The VR solution can mimic QCS scanner scenarios like real system training of troubleshooting, and can show live status and tips of the QCS system.

The term "<NUM>° assistance" of QCS Scanner refers to an overall support of QCS scanner. Four major modules of QCS scanner with respect to support are Module <NUM>: Troubleshooting of QCS scanner issues during on process; Module <NUM>: Training; Module <NUM>: Installation and commissioning of QCS scanner; and Module <NUM>: Annual maintenance/periodic checks. All mentioned modules of QCS scanner support require different/combination of technologies and different approaches to achieve the standardized, time-bound, predictable and robustness in the process.

<FIG> illustrates an exemplary QCS scanner troubleshooting technique <NUM> with augmented reality, Chatbot, and machine learning technology according to the embodiments of the present disclosure. The embodiment of the exemplary QCS scanner troubleshooting technique <NUM> illustrated in <FIG> is for illustration only. <FIG> does not limit the scope of this disclosure to any particular implementation.

The QCS troubleshooting technique <NUM> includes a HoloLens <NUM>, a Chatbot <NUM>, an issue identification <NUM>, an eDocumentation <NUM>, a machine learning server <NUM>, and an expert support <NUM>. The HoloLens <NUM> is a holographic computer made to identify the QCS scanner and its internal parts, virtual wiring layout, connection identification, scanner mechanical parts identification and more based on the scanner version also capable of video streaming the scanner for remote assistant.

The Chatbot <NUM> can provide an interactive voice based Chatbot technology that accepts the voice input from user and provides the necessary output to guide the user to perform the necessary actions.

The issue identification <NUM> involves integrating the HoloLens with the QCS server and QCS scanner to provide scanner related diagnostics based on the diagnostic information. The HoloLens can guide the user to a location or scanner part where the issue occurred and can provide necessary steps or action to be performed to resolve the issue.

The eDocumentation <NUM> provides the HoloLens the ability to identify an object and provide information related to the object, e.g. a wiring diagram, mechanical connections, test points, and more. The eDocumentation also provides receive any document a user requests with the help of the Chatbot, which will reduce searching times, data availability, and improve the user experience. The HoloLens can identify an object related to a field device. The user can provide a command that the HoloLens receives using an audio sensor or from an external device. The HoloLens displays a document corresponding to the document type of the identified object.

The machine-learning server <NUM> is a cloud service provided to resolve the issue based on a criticality of the issue. The user can connect to the machine learning server using the Chatbot, can request a solution, can provide the solution based on previous occurrences of similar issues, and can record the steps of procedure followed to resolve the current issues through which the system can provide a more robust and accurate solution in the future.

The expert support <NUM> is used if the machine-learning server is not able to resolve the issue and a user needs expert support. The HoloLens can request the machine-learning server to connect to an available expert. Once connected with an expert, the expert can explain the issue with actual visuals of the issue in order to resolve the issue. Once the issue is resolved, the machine-learning server can record the steps performed to resolve the issue.

<FIG> illustrates an exemplary QCS scanner training system <NUM> with virtual reality and Chatbot technology according to the embodiments of the present disclosure. The embodiment of the exemplary QCS scanner training <NUM> illustrated in <FIG> is for illustration only. <FIG> does not limit the scope of this disclosure to any particular implementation.

The QCS training <NUM> includes a HoloLens <NUM> with a Chatbot <NUM>, cloud based training manuals including cloud-based eDocument & video training <NUM> and cloud based virtual training module <NUM>, and virtual training <NUM>.

The wearable or HoloLens <NUM> is a holographic computer that can mimic the QCS scanner in a virtual world that can image the virtual QCS scanner. The HoloLens <NUM> can show how the physical scanner looks and can show internal parts that can be virtually imaged to aide in user learning for different components (e.g. sensors, mechanics, hardware, and software configurations) before going an actual scanner goes live.

The Chatbot <NUM> is an interactive voice-based Chatbot that accepts a voice input from a user and can provide a necessary output to guide the user to perform necessary actions for resolving an issue.

The cloud-based eDocument & video training <NUM> can cover a basic introduction of the QCS scanner and the industrial uses. The cloud-based virtual training modules <NUM> can cover insights of the QCS scanner, sensors, mechanics, hardware and software configurations, handling, service, and troubleshooting. Examples of training modules can include an installation and commissioning module <NUM>, a QCS application module <NUM>, a troubleshooting module <NUM>, an AMC & service module <NUM>, etc..

The virtual training <NUM> is used when the user is wearing the HoloLens and connects to the cloud-based training modules. The HoloLens selects a persona of the training module. Using virtual reality, the Chatbot user can commission, troubleshoot, view plant scenario usage without the use of or access to a physical scanner.

<FIG> illustrates an exemplary installation, commissioning and AMC system <NUM> of QCS scanner using augmented reality and Chatbot technology according to the embodiments of the present disclosure. The embodiment of the exemplary system <NUM> of QCS scanner illustrated in <FIG> is for illustration only. <FIG> does not limit the scope of this disclosure to any particular implementation.

The system <NUM> of the QCS scanner includes installation, commissioning and annual maintenance contract (AMC). The system <NUM> includes a HoloLens <NUM> with a Chatbot <NUM>, a cloud-based installation & AMC module <NUM>, installation and commissioning <NUM>, expert support <NUM>, and AMC activities <NUM>.

The HoloLens <NUM> is a holographic computer made to identify a site location and provide prerequisite conditions and checks for installing a QCS scanner. The HoloLens <NUM> can guide the user during installation and commissioning of the QCS scanner, various sensors, and internal parts using augmented reality. The HoloLens can enable the remote assistant for expert advice.

The Chatbot <NUM> is an interactive voice-based chatbot that can accept the voice input from the user and can provide necessary outputs to guide the user to perform the necessary action for resolving an issue.

The cloud-based installation & AMC module <NUM> includes different modules that can be accessed based on requirements from the cloud that are largely classified in sub-modules. The sub-modules include an installation module <NUM> for supporting various versions of QCS scanners, QCS sensor module <NUM>, QCS software installation and configuration module <NUM>, AMC activities for QCS scanner module <NUM>, etc..

The installation and commissioning <NUM> includes the HoloLens connecting to a cloud service. The HoloLens selects a QCS scanner version and sensors available for commissioning. The installation and commissioning <NUM> can guide the HoloLens with step by step procedures for commissioning the QCS scanner along with software installation and configuration for a full fledge startup of the QCS scanner.

The expert support <NUM> can provide the HoloLens with expert support for installation, commissioning, and AMC if a user cannot figure out a part of the process or an issue with the QCS scanner. The HoloLens can connect with an expert and provide a visual of the QCS scanner while the in conversation with a user of the HoloLens. The expert can control the HoloLens to indicate components to the user. That way, the expert can better explain the step or procedure of resolving an issue.

The AMC activities <NUM> are based on a customer record system that can create an AMC checklist. The AMC activities <NUM> can control the HoloLens to guide the user to perform the ACM activity that enables an engineer to collect on-the-go reports of the activity and comments. The HoloLens can generate the final report of the AMC activity for the customer and user records.

<FIG> and <FIG> illustrate an exemplary method <NUM>, <NUM> for troubleshooting a QCS scanner issue using augmented reality, Chatbot, and machine learning technology according to embodiments of the present disclosure. For example, the method described in <FIG> and <FIG> may be performed in conjunction with the computing device <NUM> in <FIG>.

In operation <NUM>, the computing device <NUM> can detect an issue with the QCS scanner. In certain embodiments, the computing device <NUM> detecting an issue includes receiving diagnostic information from a server related to a field device, such as a QCS scanner, in an industrial process control and automation system. An issue with the field device includes any malfunction that causes the field device to not function at a suitable operational requirement.

In operation <NUM>, the computing device <NUM> can connect to a QCS server and receive a list of potential issues. The list of potential issues can include typical issues that have been identified on the particular machine itself or from a common malfunction list of the device type.

In operation <NUM>, the computing device <NUM> can receive a voice command for selecting a potential issue from the list of potential issues. The list of potential issues can be display on the display or provided as audio outputs to the user. In certain embodiments, the computing device <NUM> can identify an issue of the field device based on the diagnostic information.

In operation <NUM>, the computing device <NUM> can capture the QCS scanner using an optical sensor on the HoloLens. The computing device <NUM> can detect the field device corresponding to the identified issue using the optical sensor. Once the field device is captured and detected, the computing device can identify specific components that correspond to the identified issue.

In operation <NUM>, the computing device <NUM> can display an indication on a display of the HoloLens corresponding to a component related to the potential issue of the QCS scanner. The computing device <NUM> can guide, using the display, a user to a location and a scanner part of the field device that is related to the issue.

In operation <NUM>, the computing device <NUM> can receive a request in a voice command for related documents and procedures to resolve the issue. The computing device <NUM> can provide the necessary steps or action to resolve the issue. The necessary steps or actions can be displayed on the display. Specific components related to steps in the procedure can be highlighted or marked on the display with any related documents. The computing device <NUM> can display the related documents on the display away from the highlighted or marked components.

In operation <NUM>, the computing device <NUM> can determine whether an issue is resolved. If the issue is resolved, the computing device <NUM> proceeds to operation <NUM>. If the issue is not resolved, the computing device <NUM> proceeds to operation <NUM>.

In operation <NUM>, the computing device <NUM> can detect that the issue is not resolved. The computing device <NUM> can receive operating data from the QCS server and determine that the field device is still not operating efficiently.

In operation <NUM>, the computing device <NUM> can connect the HoloLens to the cloud-based service on a machine-learning server for additional help with the potential issue. The cloud-based service can be directly related to a specific component.

In operation <NUM>, the computing device <NUM> can provide detailed information of the issue to the machine-learning server using a Chatbot service. The detailed information can include the information of the field device from the QCS server along with any information captured by the computing device <NUM>. The information captured by the computing device can include a live feed or frames captured from the optical sensor, audio captured from the user, frames captured from the display of the process used to fix the issue, etc..

In operation <NUM>, the computing device <NUM> can receive a relevant solution for the issue based on a previous history from the machine-learning server. The previous history includes issues resolved from the field device previously, as well as other field devices of the same type. The machine-learning server can provide an optimal solution based on all the input data or different alternative options.

In operation <NUM>, the computing device <NUM> can determine whether the issue is resolved. If the issue is resolved, the computing device <NUM> proceeds to operation <NUM>. If the issue is not resolved, the computing device <NUM> proceeds to operation <NUM>.

In operation <NUM>, the computer device <NUM> can request the machine-learning server to connect to a subject matter expert at a technical assistance center (TAC) center. The subject matter expert can be an individual that is experienced with the specific type of field device or an individual that has dealt with the specific issue.

In operation <NUM>, the computing device <NUM> can provide the detailed information of the issue and a live feed from the optical sensor of the HoloLens to the subject matter expert. The subject matter expert can be connected to live or sent the relevant information related to the field device.

In operation <NUM>, the computing device <NUM> can receive an expert report for resolving the issue from the subject matter expert. The expert report can include step-by-step instructions for resolving the issue. In each step, different components that correspond to the respective step can be highlighted or have a marker placed on the display for identification.

In operation <NUM>, the computing device <NUM> can log the issue type and resolve procedure to the machine-learning server. The issue type and resolve procedure can be related to a specific component or assembly of components, or related to a malfunction of the field device. In operation <NUM>, the computing device <NUM> determines that the issue has been resolved.

Although <FIG> and <FIG> illustrates one example of a method <NUM>, <NUM> for <NUM>° assistance for a QCS scanner with mixed reality (MR) and machine learning technology, various changes may be made to <FIG>. For example, various steps shown in <FIG> could overlap, occur in parallel, occur in a different order, or occur any number of times.

<FIG> and <FIG> illustrate an exemplary flowchart for installation and commissioning according to the embodiment of the present disclosure. For example, the method described in <FIG> and <FIG> may be performed in conjunction with the installation, commissioning and AMC system <NUM> in <FIG>.

In operation <NUM>, the installation, commissioning and AMC system <NUM> can begin the installation, commissioning, and AMC system of a QCS scanner.

In operation <NUM>, the installation, commissioning and AMC system <NUM>, from the wearable device, can connect to the cloud server for getting the persona for installation, commissioning, and AMC.

In operation <NUM>, the installation, commissioning and AMC system <NUM> can determine whether installation and commissioning persona is selected.

In operation <NUM>, the installation, commissioning and AMC system <NUM>, using a voice command to the wearable device, can request the cloud server for the required installation module for support version of QCS scanner.

In operation <NUM>, the installation, commissioning and AMC system <NUM> can get the list supported hardware and software installation for the QCS scanner and sensors.

In operation <NUM>, the installation, commissioning and AMC system <NUM>, using the voice command to wearable, can request the cloud server for the required module, including a QCS scanner installation module, QCS sensors modules, and QCS software installation and configuration module.

In operation <NUM>, the installation, commissioning and AMC system <NUM>, from the cloud server, can receive a required sequence of a procedure to be performed for installation and commissioning of QCS scanner in wearable device.

In operation <NUM>, the installation, commissioning and AMC system <NUM> can perform the installation and commissioning from instructions and can evaluate the result on the wearable device.

In operation <NUM>, the installation, commissioning and AMC system <NUM> can determine if the user is able to perform the instruction or set of instructions.

In operation <NUM>, the installation, commissioning and AMC system <NUM> can receive help from subject matter expertise to resolve an issue with live visual and interactive chatting.

In operation <NUM>, the installation, commissioning and AMC system <NUM> can select the AMC module.

In operation <NUM>, the installation, commissioning and AMC system <NUM>, using a voice command to the wearable device, can request the cloud server for an AMC record and checklist based on the QCS scanner version.

In operation <NUM>, the installation, commissioning and AMC system <NUM> can receive the list checklist for a selected QCS scanner for AMC.

In operation <NUM>, the installation, commissioning and AMC system <NUM>, from the cloud server, can receive required sequence of procedure to be performed for AMC QCS scanner in a wearable device.

In operation <NUM>, the installation, commissioning and AMC system <NUM> can perform the AMC from the instruction and evaluate the result on the wearable device.

In operation <NUM>, the installation, commissioning and AMC system <NUM> determines whether the user is able to perform the instructions.

In operation <NUM>, the installation, commissioning and AMC system <NUM> can receive help from a subject matter expert to resolve issues with live visual and interactive chatting.

Although FIGURES SA and SB illustrates one example of a method <NUM>, <NUM> for <NUM>° assistance for a QCS scanner with mixed reality (MR) and machine learning technology, various changes may be made to <FIG>. For example, various steps shown in <FIG> could overlap, occur in parallel, occur in a different order, or occur any number of times.

Claim 1:
A method (<NUM>, <NUM>) using a chatbot application for interactive communication between a user, a quality control system (QCS), and a remote support, the method comprising:
receiving (<NUM>) diagnostic information from a machine-learning server (<NUM>) related to a field device (<NUM>) including a QCS scanner in an industrial process control and automation system (<NUM>);
identifying (<NUM>) an issue of the field device based on the diagnostic information;
capturing (<NUM>), using an optical sensor (102a), the field device corresponding to the identified issue to identify components in the field device that correspond to the issue;
guiding (<NUM>), using a display (<NUM>), the user to the location of the field device that is related to the issue;
providing (<NUM>), using the display, necessary steps or actions to resolve the issue; and
connecting (<NUM>), via the server using the chatbot application, the user with the remote support to get modules (<NUM>-<NUM>) of installation, commissioning, annual maintenance (AMC) and training for the QCS and to resolve the issue based on a criticality of the issue.