Patent Publication Number: US-2022215323-A1

Title: Systems and methods for intelligent ticket management and resolution

Description:
TECHNICAL FIELD 
     The present disclosure generally relates to computerized methods and systems for intelligent ticket management and resolution and, more particularly, to computerized methods and systems for automatically assessing the resolvability and characteristics of an issue based on user-provided issue descriptions and efficiently providing solutions to the user based on the assessment. 
     BACKGROUND 
     Many service providers (e.g., financial service providers) provide computerized products and services (e.g., banking applications and services) to customers. In some cases, a customer may run into issues with a product or service and may be required to consult with the service provider in order to resolve the issue. In many cases, customers open support cases (i.e., “tickets”) with the service provider by contacting them, for example, via phone or via a web-based application. In conventional systems, the service provider will require certain information about the customer&#39;s issue that enable the ticket to be addressed by an appropriate work group or team associated with the service provider. This information can be acquired, for example, through electronic or telephonic communications, where the service provider prompts the customer to provide the information. 
     This initial exchange of information to determine the specific issue identified by the customer and identifying appropriate work groups typically requires a large amount of manual assessment by the service provider, which will need to expend electronic resources and labor hours in addressing the customer&#39;s issues. In many cases, the service provider will provide service and product manuals and other job aids (e.g., Frequently Asked Questions) that are accessible through a website in order to mitigate these issues. Customers, however, often find it difficult to navigate through these resources to find the correct job aid and are more likely to open a ticket with the service provider than consult these resources, even though the solution may have been accessible through an available job aid. 
     Therefore, there is a need for systems and methods that enable autonomous computer systems to efficiently resolve service and product related customer issues by intelligently evaluating user-provided issue descriptions and directing customers to appropriate work groups and/or job aides. 
     SUMMARY 
     One aspect of the present disclosure is directed to a computer-implemented system for efficient ticket resolution. The system may include: a memory storing instructions; and at least one processor configured to execute the instructions to perform operations including: receiving, from at least one user device, a request to resolve an issue, the request comprising language; analyzing, via natural language processing, the language in the request to determine the issue to be resolved; based on the analysis, determining whether the issue meets a condition for automated resolution; if the condition is met: extracting, via an application programming interface and from the at least one user device, information needed to resolve the issue; and resolving the issue using the extracted information; and if the condition is not met: inserting, into at least one database, a ticket comprising: an identifier associated with the at least one user device; and information associated with the issue to be resolved; assigning, based on the information, a work group to the ticket; consulting the at least one database to determine whether a job aid associated with the issue exists; forwarding, to the at least one user device, at least one of: the job aid; received communications from the work group; and an estimated amount of time to resolution based on the information. 
     Another aspect of the present disclosure is directed to a computer-implemented method for efficient ticket resolution. The method may include: receiving, from at least one user device, a request to resolve an issue, the request comprising language; analyzing, via natural language processing, the language in the request to determine the issue to be resolved; based on the analysis, determining whether the issue meets a condition for automated resolution; if the condition is met: extracting, via an application programming interface and from the at least one user device, information needed to resolve the issue; and resolving the issue using the extracted information; and if the condition is not met: inserting, into at least one database, a ticket comprising: an identifier associated with the at least one user device; and information associated with the issue to be resolved; assigning, based on the information, a work group to the ticket; consulting the at least one database to determine whether a job aid associated with the issue exists; forwarding, to the at least one user device, at least one of: the job aid; received communications from the work group; and an estimated amount of time to resolution based on the information. 
     Other systems, methods, and computer-readable media are also discussed herein. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram of an example server computer system for intelligent ticket management and resolution, consistent with embodiments of this disclosure. 
         FIG. 2  is a diagram of an example structure of a system for intelligent ticket management and resolution, consistent with embodiments of this disclosure. 
         FIG. 3  is a diagram of an example structure of an UI controller system for intelligent ticket management and resolution, consistent with embodiments of this disclosure. 
         FIG. 4  is a diagram of an example structure of an intelligent ticketing advisor system for intelligent ticket management and resolution, consistent with embodiments of this disclosure. 
         FIG. 5  is a flow diagram of an exemplary process for intelligent ticket management and resolution, consistent with embodiments of this disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The disclosed embodiments include systems and methods for intelligent ticket management and resolution. Before explaining certain embodiments of the disclosure in detail, it is to be understood that the disclosure is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the drawings. The disclosure is capable of embodiments in addition to those described and of being practiced and carried out in various ways. 
     As such, those skilled in the art will appreciate that the conception upon which this disclosure is based may readily be utilized as a basis for designing other structures, methods, and systems for carrying out the several purposes of the present disclosure. 
     Reference will now be made in detail to the present example embodiments of the disclosure, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts. 
       FIG. 1  is a block diagram of an example server computer system  100  (referred to as “server  100 ” hereinafter), consistent with some embodiments of this disclosure. Server  100  may be one or more computing devices configured to execute software instructions stored in memory to perform one or more processes consistent with some embodiments of this disclosure. For example, server  100  may include one or more memory devices for storing data and software instructions and one or more hardware processors to analyze the data and execute the software instructions to perform server-based functions and operations (e.g., back-end processes). The server-based functions and operations may include intelligent ticket management and resolution. 
     In  FIG. 1 , server  100  includes a hardware processor  110 , an input/output (I/O) device  120 , and a memory  130 . It should be noted that server  100  may include any number of those components and may further include any number of any other components. Server  100  may be standalone, or it may be part of a subsystem, which may be part of a larger system. For example, server  100  may represent distributed servers that are remotely located and communicate over a network. 
     Processor  110  may include or one or more known processing devices, such as, for example, a microprocessor. In some embodiments, processor  110  may include any type of single or multi-core processor, mobile device microcontroller, central processing unit, or any circuitry that performs logic operations. In operation, processor  110  may execute computer instructions (e.g., program codes) and may perform functions in accordance with techniques described herein. Computer instructions may include routines, programs, objects, components, data structures, procedures, modules, and functions, which may perform particular processes described herein. In some embodiments, such instructions may be stored in memory  130 , processor  110 , or elsewhere. 
     I/O device  120  may be one or more devices configured to allow data to be received and/or transmitted by server  100 . I/O device  120  may include one or more customer I/O devices and/or components, such as those associated with a keyboard, mouse, touchscreen, display, or any device for inputting or outputting data. I/O device  120  may also include one or more digital and/or analog communication devices that allow server  100  to communicate with other machines and devices, such as other components of server  100 . I/O device  120  may also include interface hardware configured to receive input information and/or display or otherwise provide output information. For example, I/O device  120  may include a monitor configured to display a customer interface. 
     Memory  130  may include one or more storage devices configured to store instructions used by processor  110  to perform functions related to disclosed embodiments. For example, memory  130  may be configured with one or more software instructions associated with programs and/or data. 
     Memory  130  may include a single program that performs the functions of the server  100 , or multiple programs. Additionally, processor  110  may execute one or more programs located remotely from server  100 . Memory  130  may also store data that may reflect any type of information in any format that the system may use to perform operations consistent with disclosed embodiments. Memory  130  may be a volatile or non-volatile (e.g., ROM, RAM, PROM, EPROM, EEPROM, flash memory, etc.), magnetic, semiconductor, tape, optical, removable, non-removable, or another type of storage device or tangible (i.e., non-transitory) computer-readable medium. 
     Consistent with some embodiments of this disclosure, server  100  includes ticketing controller system  112  that may include a UI controller system  114  and an intelligent ticketing advisor system  116 . Ticketing controller may be configured to autonomously and automatically implement intelligent ticket management and resolution using UI controller system  114  and intelligent ticketing assessor  116 . Ticketing controller  112  may be implemented as software (e.g., program codes stored in memory  130 ), hardware (e.g., a specialized chip incorporated in or in communication with processor  110 ), or a combination of both. UI controller system  114  may be configured to react to various user-initiated events (e.g., an input received from user interface  150 ). Intelligent ticketing advisor  116  may be configured to evaluate user-provided descriptions, for example, using natural language processing (NLP) and artificial intelligence (AI). 
     Server  100  may also be communicatively connected to one or more databases  140 . For example, server  100  may be communicatively connected to database  140 . Database  140  may be a database implemented in a computer system (e.g., a database server computer). Database  140  may include one or more memory devices that store information (e.g., the data outputted by ticketing controller system  112 ) and are accessed and/or managed through server  100 . By way of example, database  140  may include Oracle™ databases, Sybase™ databases, or other relational databases or non-relational databases, such as Hadoop sequence files, HBase, or Cassandra. Systems and methods of disclosed embodiments, however, are not limited to separate databases. In one aspect, server  100  may include database  140 . Alternatively, database  140  may be located remotely from the server  100 . Database  140  may include computing components (e.g., database management system, database server, etc.) configured to receive and process requests for data stored in memory devices of database  140  and to provide data from database  140 . 
     Server  100  may also be communicatively connected to at least one user interface  150 . User interface  150  may include a graphical interface (e.g., a display panel), an audio interface (e.g., a speaker), or a haptic interface (e.g., a vibration motor). For example, the display panel may include a liquid crystal display (LCD), a light-emitting diode (LED), a plasma display, a projection, or any other type of display. The audio interface may include microphones, speakers, and/or audio input/outputs (e.g., headphone jacks). In some embodiments, user interface  150  may be included in server  100 . In some embodiments, user interface  150  may be included in a separate computer system. User interface  150  may be configured to display data transmitted from server  100 . 
     In connection with server  100  as shown and described in  FIG. 1 , the systems and methods as described herein may provide a technical solution to technical problems in automated ticket management and resolution. Aspects of this disclosure may relate to autonomous testing of a computer application, including systems, apparatuses, methods, and non-transitory computer-readable media. For ease of description, a system is described below, with the understanding that aspects to the system apply equally to methods, apparatuses, and non-transitory computer-readable media. For example, some aspects of such a system can be implemented by a system (e.g., server  100  and database  140 ), by an apparatus (e.g., server  100 ), as a method, or as program codes or computer instructions stored in a non-transitory computer-readable medium (e.g., memory  130  or another storage device of server  100 ). In a broadest sense, the system is not limited to any particular physical or electronic instrumentalities, but rather can be accomplished using many different instrumentalities. 
     Consistent with some embodiments of this disclosure, a system for intelligent ticket management and resolution may include a non-transitory computer-readable medium configured to store instructions and at least one processor configured to execute the instructions to perform operations. A computer application, as used herein, may refer to a set of computer programs or modules combined in a logical manner to implement a function (e.g., a financial service). In some embodiments, the computer application may be created, maintained, updated, or executed at a server computer of the system. In some cases, because the function may be implemented by multiple, different sequences of operations the computer application may be implemented by multiple, different programs. 
     By way of example, with reference to  FIG. 1 , the system may include server  100  and database  140 . The at least one processor may be processor  110  in server  100 . The non-transitory computer-readable medium may be memory  130  in server  100 . The instructions stored in the non-transitory computer-readable medium may be used for implementing testing module  111  in server  100 . 
       FIG. 2  is a diagram of an example structure of a system  200  for intelligent ticket management and resolution, consistent with some embodiments of this disclosure. By way of example a user (e.g., a customer) associated with user device  201  (e.g., a computer, tablet, cellular phone, etc.) may experience issues with a product or service associated with a service provider (e.g., financial services) that implements the systems and methods disclosed herein to resolve customer issues. In some embodiments, user device  201  may be associated with a customer that initiates a support case by providing, via a network, a text or voice description of their issue to chatbot UI  210 , digital collaboration app UI  220 , ticketing system UI  230 , or any other suitable method of communicating with the service provider and may be required to consult with the service provider in order to resolve the issue. In some embodiments, however, user device  201  may be associated with an employee or agent associated with the service provider that is acting on behalf of a customer to help resolve their issue. For example, a customer may be in communication with a user associated with user device  201 , and user device  201  may provide updates to ticketing system UI  230  based on the communications. 
     In some embodiments, chatbot UI  210  may include a software or web-based application that may be used to conduct an online chat conversation with user device  201  via text or text to speech, and may be configured to simulate the way a human would act as a conversational partner. Digital collaboration app UI  220  may include a software or web-based application (e.g., Slack, Microsoft Teams, Google Drive, etc.) that offers services such as instant messaging, file sharing, teleconferencing, and/or video conferencing, or any other application that enables user device  201  to interact with one or more individuals associated with the service provider. Digital collaboration app UI may also be configured to view and update ticket information through UI controller system  114 . Chatbot UI  210  and digital collaboration app UI may be configured to exchange information, such as user-provided descriptions and/or language and system responses, between user device  201  and UI controller system  114 . Ticketing system UI  230  may be any software or web-based application that user device  201  may use to update or modify information relating to their ticket status (e.g., not resolved, resolved, estimated resolution time, etc.). Ticketing system UI  230  may be communicatively coupled to ticketing system  240  (e.g., via a network). 
     In some embodiments, UI controller system  114  may be configured to react to various user-initiated events through chatbot UI  210  and digital collaboration app UI  220 . For example, UI controller system  114  may conduct a dialogue with user device  201  and communicate with ticketing system  240 , intelligent ticketing advisor system (“ITA”)  116 , and/or various systems of record  270  in order to identify issues, create, view, and update tickets, and to resolve issues in real-time. These various systems of record may include systems responsible for storing and managing client data. For example, these systems may often need to be updated to correct data entry errors, or update configuration to enable additional system capabilities to be implemented. Examples of such systems include those responsible for maintaining client&#39;s customer&#39;s account data, and/or client-user ids, passwords, and permissions granted to users. Chatbot UI, digital collaboration app UI  220 , ticketing system UI  230 , ticketing system  240 , UI controller system  114 , intelligent ticket advisor system  116 , various systems of record  270 , and/or any other system, subsystem, or component associated with system  200  may be implemented on a single system and/or server or as multiple systems interconnected via a network. 
     In some embodiments, ITA  116  may be configured using natural language processing (NLP) technology, and may be configured to perform various tasks, such as text and speech processing, morphological analysis, syntactic analysis, lexical and rational semantics, discourse, or any other form of analysis of natural language data. ITA  116  may also utilize dialogue management, natural language generation, or any other suitable means of generating natural language responses or automatically answering questions based on user-provided information. 
     In some embodiments, ITA  116  may be configured to evaluate a user-provided description of an issue received from chatbot UI  210 , digital collaboration app UI, and/or ticketing system UI  230  ( FIG. 2 ). Based on the evaluation, ITA  116  may identify the issue and generate an assessment of how the issue can be most efficiently resolved. ITA  116  may continuously evaluate the ticket and update the assessment or may generate a new assessment upon receiving additional information from UI controller system  114  (e.g., additional user-provided descriptions) or from ticketing system  240  (e.g., ticket status updates). In some embodiments, the assessment may include an identification of the issue, a determination of whether the issue is immediately resolvable or can be resolved in real time, whether additional information is needed, and/or an estimated resolution time. ITA  116  may store historical ticket data and recorded issue-resolution time  280  ( FIG. 2 ) in at least one database (e.g., database  140  in  FIG. 1 ), which may be used, for example, to train one or more machine-learning algorithms utilized by ITA  116  (e.g., support vector machines, Bayesian networks, maximum entropy, conditional random field, random forest, gradient boosting, neural networks, etc.). In some embodiments, ITA  116  may be configured to access the at least one database to determine whether there is a job aid (e.g., product and/or service manuals, FAQs, troubleshooting guides, etc.) corresponding to the identified issues, and may provide user device  201 , for example via chatbot UI  210  and/or digital collaboration app UI  220 , the job aid and/or excerpt of the job aid corresponding to the issue (e.g., by sending a file or a hyperlink). 
       FIG. 3  is a diagram of an example structure of UI controller system  114  for intelligent ticket management and resolution, consistent with some embodiments of this disclosure. UI controller system  114  may include real-time dialog controller sub-system  310 , which may include dialog module  312  and dialog resolution module  314 . It is to be understood that the configuration of elements depicted in  FIG. 3  is exemplary only, and that disclosed embodiments are not limited to any particular programming or arrangement of modules, devices, and/or systems. 
     Dialog module  312  may be configured to receive user-defined issue descriptions as free-form text from chatbot UI  210  and send responses to chatbot UI  210 . Dialog module  312  may also be communicatively coupled with ticketing system  240  and various systems of record  270 , for example, via an application-programming interface (API). 
     Dialog resolution module  314  may be configured to send the user-defined issue description received by dialog module  312  to ITA  116 , which may return an assessment to dialog resolution module  314 . 
     In some embodiments, UI controller system may also include digital collaboration app controller sub-system  320 , which may include ticket-data module  322 , user-state module  324 , and alert-delivery module  326 . 
     Ticket interface module may be configured to retrieve a job aid from digital collaboration app UI  220  and forward ticket-related information (e.g., an identified issue in the ticket, an assigned work group, associated job aids, estimated time to resolution, communications from the user and the work group, etc.) to user-state module  324  and ticketing system  240 . 
     User-state module  324  may be configured to receive information from ticket-data module  322  and alert-delivery module and may maintain, monitor, and/or update ticket information based on the received information. User-state module  324  may be responsible for relating user identity within the digital collaboration application itself, to the same user&#39;s identity within the ticketing system. When an alert is generated based on a ticket-update, for example, user-state module  324  may enable the alert to be delivered to the digital collaboration instance associated with that user. 
     Alert-delivery module  326  may be configured to send alerts to digital collaboration app UI  220  based on information received from user-state module  324  or intelligent ticketing advisor system  116 . Alerts may include any notification relating to a status of a ticket. For example, alert-delivery module  326  may be configured to send digital collaboration app UI  220  an alert indicating that the ticket has been resolved, that a work group has been assigned to the ticket, that an estimated time to resolution has been updated, that an additional issue has been identified, and/or that a suitable job aid has been found. 
       FIG. 4  is a diagram of an example structure of ITA  116  for intelligent ticket management and resolution, consistent with some embodiments of this disclosure. ITA  116  may include controller sub-system  410 , job aid management system  420 , NLP assessment sub-system  430 , and NLP assessment training sub-system, and may be configured to send and receive job aid information from job aid management sub-system  420 . Controller sub-system  410  may include interface module  412 , event module  414 , and activity logging module  416 . It is to be understood that the configuration of elements depicted in  FIG. 4  is exemplary only, and that disclosed embodiments are not limited to any particular programming or arrangement of modules, devices, and/or systems. 
     Interface module  412  may be configured to exchange user-defined issue descriptions, new ticket data, and description assessments between ticketing system  240 , UI controller system  114 , event module  414 , activity logging module  416 , and NLP assessment sub-system  430 . For example, interface module  412  may be configured to forward updated ticket data received from NLP assessment sub-system  430  to ticketing system  240 . 
     Event module  414  may be configured to monitor the exchange of data through interface module  412  and forward event messages to UI controller system  114 , such as an alert regarding the status of a ticket. In some embodiments, the alerts may be interactive in form. For example, event module  414  may be configured to prompt a user to submit additional information that may be added to a given ticket stored and/or monitored by ticketing system  240 . 
     Activity logging module  416  may collect data from interface module to NLP assessment training sub-system  440  to be used as training data to improve NLP operations of ITA  116 . In some embodiments, activity logging module  416  may be configured to capture user-activity occurring via UI controller system  114 . This activity includes, for example, user-choices relative to suitability of a job aid to satisfying user needs. These activity data may be fed to the NLP assessment training sub-system  440  in order to enable more effective future recommendations to be made by NLP assessment sub-system  430  through incremental NLP model training. In some embodiments, job aid management sub-system  420  may include interface module  422  and storage module  424 , and may be configured to maintain a record of job aids and facilitate the access, insertion, deletion, and/or modification of job aid or job aid-related information. Storage module  424  may be a memory, a database, or any other form of data storage that may store any number of job aids. Interface module  422  may be configured to access storage module  424  to determine whether a job aid for a certain issue, retrieve the job aid, and transmit the job aid to UI controller system  114 . For example, NLP assessment system may evaluate a user-defined issue description and return an assessment including an identified issue to UI controller system  114  through interface module  412 . UI controller system  114  may forward the identified issue to interface module  422 , which may determine whether a job aid corresponding to the identified issue exists. 
     In some embodiments, NLP assessment sub-system  430  may include feature extraction module  432 , entity extraction module  434 , and classifier module  436 . Feature extraction module  432  may be configured to filter or pre-process the information contained in the user-defined issue description into identifiable individual properties or characteristics, such as a combination of one or more words or characters. Feature extraction module  432  may be configured to perform a variety of complex data analysis techniques, such as text transformation, vectorization, independent component analysis, latent semantic analysis, partial least squares, principal component analysis, multifactor dimensionality reduction, nonlinear dimensionality reduction, multilinear principal component analysis, or any other appropriate dimensionality reduction technique. 
     Entity extraction module  434  may be configured to identify and classify key elements in the user-defined issue description from text into one or more pre-defined categories. For example, entity extraction module  434  may be configured to locate and classify named entities mentioned in the user-provided issue description into pre-defined categories such as person names, device names, network names, organizations, locations, time expressions, quantities, monetary values, percentages, or any other category of entities. In some embodiments, entity extraction module  434  may utilize extraction rules that may be based one or more extraction techniques, such as pattern matching, linguistics, syntax, and/or semantics. 
     Classifier module  436  may be configured to determine, based on the processed user-defined issue description, a specific issue, a classification of the specific issue, and/or any other characteristics corresponding the user-defined issue description. In general, NLP assessment sub-system and/or components thereof may be configured to transform the user-defined issue description into structured, computer-readable data and assess the transformed data using data models and/or machine learning algorithms (e.g., support vector machines, Bayesian networks, maximum entropy, conditional random field, random forest, gradient boosting, neural networks, etc.) that are trained by NLP assessment training sub-system  440 . 
     In some embodiments, NLP assessment training subsystem  440  may include data cleaning module  442  and model training module  444 . NLP assessment training subsystem  440  may be configured to receive data from activity logging module  416 , such as information exchanged through interface module  412 . Data cleaning module  442  may be configured to filter the data received from activity logging module  416  and remove noise (e.g., hashtags, punctuation, numbers, etc.) that may enable model training module  444  to more easily detect patterns in the data. After the data is filtered by data cleaning module  416 , model training module  444  may be configured to process the data received from activity logging module  416  to generate data models and/or train one or more machine learning algorithms (e.g., support vector machines, Bayesian networks, maximum entropy, conditional random field, random forest, gradient boosting, neural networks, linear classifiers, etc.) related to the NLP capabilities of ITA  116 . The NLP data models generated by NLP assessment training sub-system  440  may be utilized by NLP assessment sub-system  430  to evaluate user-defined issue descriptions in order to provide interface module  412  with an assessment of the issue description. 
       FIG. 5  is a flow diagram of exemplary process  500  for intelligent ticket management and resolution, consistent with some embodiments of this disclosure. In some embodiments, process  500  may be executed by one or more processors (e.g., processor  110 ) associated with system  200 , and/or executed either partially or wholly by the one or more components of system  200  (e.g., UI controller system  114 , ITA  116 , real-time dialog controller sub-system  310 , NLP assessment sub-system  430 , classifier module  436 , etc.). For ease of discussion, process  500  will be described as being executed by UI controller system  114  and/or ITA  116 , although it is to be understood that process  500  may be executed by any suitable component or sub-component of system  200 , consistent with the present disclosure. 
     In some embodiments, process  500  may begin at step  510 . At step  510 , ITA  116  may receive, from at least one user device, a request to resolve an issue. For example, through chatbot UI  210  or digital collaboration app UI  220 , user device  201  may send a request to UI controller system  114 , which may forward the request to ITA  116  for assessment. In some embodiments, the request may include language to be analyzed by ITA  116 . The language contained in the request is not limited to a single form of communication. For example, the language contained the request may be in the form of free form text, spoken language, or any other appropriate communication medium. In some embodiments, UI controller system  114  may be configured to instantiate a digital dialogue session (e.g., a chatbot session) with user device  201 . The digital dialogue session may be used to facilitate communication between user device  201  and UI controller system  114 . For example, the request to resolve an issue may be sent to UI controller system  114  through the digital dialogue session, and UI controller system  114  return responses and prompts to user device  201  through the digital dialogue session. 
     At step  520 , ITA  116  may analyze, via natural language processing (“NLP”), the language in the request to determine the issue to be resolved. For example, ITA  116  may be programmed or otherwise configured to perform tasks such as speech and/or text recognition, natural language understanding, and natural language generation. For example, in some embodiments, ITA  116  may feed the request to at least one machine-learning algorithm. These tasks may be carried out through the use of one or more machine-learning algorithms that use historical ticket data to analyze the language and return an assessment of the language to ITA  116 . In some embodiments, the at least one machine-learning algorithm may be implemented using statistical methods, machine learning, or neural networks. For example, in some embodiments, the machine-learning algorithm may include at least one of a generalized least squares regression technique, an ordinary least squares regression technique, a tree technique (random forest, decision tree, etc.), a gradient boosting technique, a neural network technique, a support vector machine technique, or a linear classifier technique. 
     In some embodiments, ITA system  116  may train the at least one machine learning algorithm using the historical ticket data. The historical data may include previously received requests, user-provided issue descriptions in previous requests, previously assigned work groups for each previously received requests, recorded resolution times for each previously received requests, documentation associated (i.e., job aids) with at least one of a product, service, or application associated with the previously received requests, or any other information exchanged between users and UI controller system  114  associated with previous tickets. For example, NLP assessment training sub-system  440  (i.e., data cleaning module  442  and model training module  444 ) may train the at least one machine learning algorithm using information received from activity logging module  416  in order to improve the ability of NLP assessment sub-system  430  (i.e., feature extraction module  432 , entity extraction module  434 , and classifier module  436 ) to make accurate assessments based on the user-defined issue descriptions contained in the requests. 
     ITA  116  may identify the particular issue described by the user based on the analysis completed at step  520 . For example, ITA  116  may determine that user device  201  is experiencing a particular problem (e.g., an issue accessing content in an application associated with a service provider) based on the description provided. At step  530 , ITA  116  may determine whether the particular issue identified meets a condition for automated resolution. In some embodiments, the condition may be satisfied if ITA  116  is able to determine that the particular issue may be immediately resolved. ITA  116  may make this determination, for example, by consulting a record stored in at least one database (e.g., database  140 ) of pre-defined issues and associated solutions, or it may be included in the assessment generated by the at least one machine-learning algorithm implemented by NLP assessment sub-system  430 . If the condition is met, process  500  may proceed to step  540 . If the condition is not met, process  500  may proceed to step  550 . 
     At step  530 , ITA  116  may determine whether additional information is needed to resolve the issue. For example, in step  530 , ITA  116  may determine that system  200  may immediately resolve the user&#39;s inability to access content in a web-based application associated with the service provider if additional information (e.g., account information, technical specifications of the at least one user device, network information, etc.) is acquired. If additional information is needed, process  500  may proceed to step  540 . At step  540 , ITA  116  may extract information from user device  201  through one or more communication channels, such as chatbot UI  210  or digital collaboration app UI  220 . For example, if ITA  116  determines that an email address is required to resolve the issue, ITA  116  may generate an automated response or prompt requesting the email address and transmit the response or prompt to user device  201  through a digital dialogue session facilitated by chatbot UI  210 . After receiving the automated response or prompt from ITA  116 , user device  201  may return the required information to ITA  116  through chatbot UI  210 . If ITA  116  determines that no additional information is needed at step  530 , process  500  may proceed directly to step  545  from step  530 . 
     Once ITA  116  receives all of the information necessary to resolve the issue, ITA  116  may resolve the issue at step  545 . For example, ITA  116  may send user instructions to user device  201  that a user may follow to immediate resolve the issue. In some embodiments, ITA  116  may transmit instructions to user device  201  that cause user device  201  to execute operations that the resolve the issue. The issue may thus be resolved immediately without opening a ticket, therefore eliminating the need for assigning a work group and reducing the amount of resources used on facilitating communication between user device  201  and the work group. For example, an administrative user with authority to reset a user&#39;s application passwords within some particular system of record may by means of interaction with chatbot UI  210  cause a user&#39;s password to be reset by means of interaction between dialog module  312  and that system of record, and thereby obviate the need to create a ticket. As a further example, and by means of similar systemic interaction, an authorized user may cause a system of record to be updated enabling a particular feature or function of that application to be made available to users of that system. 
     If ITA  116  determines at step  530  that the condition for resolution is not met, process  500  may proceed to step  550 . At step  550 , ITA  116  may be configured to determine whether a job aid associated with the issue exists. For example, ITA  116  may consult at least one database (e.g., database  140 ) associated with system  200  to determine if a manual, FAQ, or other documentation associated with the issue identified at step  520  exists. In some embodiments, ITA  116  may consult the at least one database by conducting a search for one or more keyword-derived features associated with the identified issue or included in the user-provided issue description. For example, ITA  116  may retrieve all job aids relating to or containing the keyword “network” if user device  201  is experiencing network related issues. In some embodiments, ITA  116  can narrow search results by additional keyword-derived features contained in the user-provided issue description, such as “slow”, “wireless”, or other issue-related descriptors. The at least one database may also include a record of job aid associated with particular issues, and ITA  116  may determine that a job aid exists for the issue identified in step  520  based on the record. If the job aid exists UI controller system  114  may retrieve the job aid, for example, from interface module  422  of job aid management sub-system  420 . 
     By way of example, job aid identification may include one or more outputs from the assessment performed by ITA  116  of the user-provided issue description provided by interaction with dialog resolution module  314 . In some embodiments, this process may include a priori natural language processing of job aid content to create vectorized representation of the content. Similar NLP vectorization of user-provided issue description may be performed when a user expresses an issue by interacting with chatbot UI  210 . By evaluating the latter against similar values in the set of all job aids, the system may recommend an appropriate job aid based on a quantitative assessment of the vectorized representations and an arbitrary threshold. 
     If ITA  116  determines that a relevant job aid exists at step  550 , process  500  may proceed to step  560 . At step  560 , ITA  116  may send the relevant job aid to the at least one user device user at step  560  (e.g., through chatbot UI  210 , digital collaboration app UI  220 , and/or ticketing system UI  230 ). At step  562 , ITA  116  may determine whether the sent job aid resolved or helped resolved the issue. For example, ITA  116  may send an alert or notification to the at least one user device prompting the user to indicate whether the issue has been resolved. ITA  116  may then receive a response from the at least one user device and determine, based on the response, whether the issue has been resolved. If the issue has been resolved, process  500  may conclude at step  564 . At step  564 , ITA  116  may determine that no further action is need. In some embodiments, ITA  116  may be configured to terminate any existing communication channels with the at least one user device, and/or may cause on or more systems and/or subsystems in system  200  to terminate any processes related to the issue. 
     If ITA determines that a relevant job aid does not exist at step  550 , or if the issue was not resolved by the job aid at step  562 , process  500  may proceed to step  552 . At step  552 , ITA  116  may generate a ticket associated with the issue. In some embodiments, ITA  116  may insert the ticket into a record of tickets stored in at least one database associated with system  200  (e.g., database  240 ). ITA  116  may also send the ticket to ticketing system  240 , which may be configured to monitor the status of the ticket and update ticket information based on information received from ticketing system UI  230 , UI controller system  114 , or ITA  116 . In some embodiments, the generated ticket may include an identifier associated with user device  201 , information associated with the issue to be resolve (e.g., the particular identified issue, user-provided issue descriptions, etc.), an estimated resolution time, or any other form of information relating to the ticket that may be provided by user device  201  or generated by ITA  116 . 
     At step  554 , ITA  116  may assign a work group to the ticket. A work group may be one or more workers associated with a service provider associated with system  200  that are tasked with aiding customers (e.g., a user associated with user device  201 ) with a particular issue or grouping of issues. For example, there may be one or more work groups associated with system  200  that are assigned to help resolve technical issues, and there may be one or more separate work groups that are assigned to help resolve logistical issues. In some embodiments, the work group may be at least partially based on the assessment of the issue and/or the analysis completed by ITA  116  at step  520 . For example, ITA  116  may determine that user device  201  is experiencing a particular issue and may assign the ticket to a work group tasked with resolving that particular issue. For example, ITA  116  may identify that a customer is experiencing connectivity issues based on a description including “can&#39;t connect to the internet” and may assign the ticket to a work group associated with network issues. In some embodiments, the assignment may also be based on the availability of one or more work groups. For example, ITA  116  may be configured to consult a record stored in a database (e.g., database  140 ) indicating the availability and/or workload of work group and to bias the assignment of tickets to work groups with more availability. In some embodiments, UI controller system  114  may establish a communication link between the at least one user and the assigned work group. For example, UI controller system  114  and/or digital collaboration app UI  220  may open a communication channel through a digital collaboration application (e.g., Slack, Microsoft Teams, Google Drive, etc.) that allows user device  201  to communicate and exchange information with the assigned work group. 
     At step  556 , UI controller system  114  may forward ticket-related information to at least one user device associated with user device  201  (e.g., through chatbot UI  210 , digital collaboration app UI  220 , and/or ticketing system UI  230 ). Ticket-related information may include, for example, the job aid retrieved at step  550 , received communications from the work group, an estimated amount of time to resolution that is estimated by ITA  116 , or any other information associated with the ticket generated at step  550 . UI controller system  114  may be configured to forward the ticket-related information on a continuing basis, for example, at predetermined time intervals or upon receiving a request from user device  201  to provide a status update. 
     Disclosed embodiments may include a non-transitory computer-readable medium that stores instructions for a processor (e.g., processor  110 ) for autonomous testing of a computer application in accordance with the example flowchart of  FIG. 5  above, consistent with embodiments in the present disclosure. For example, the instructions stored in the non-transitory computer-readable medium may be executed by the processor for performing process  500  in part or in entirety. Common forms of non-transitory media include, for example, a floppy disk, a flexible disk, hard disk, solid-state drive, magnetic tape, or any other magnetic data storage medium, a Compact Disc Read-Only Memory (CD-ROM), any other optical data storage medium, any physical medium with patterns of holes, a Random Access Memory (RAM), a Programmable Read-Only Memory (PROM), and Erasable Programmable Read-Only Memory (EPROM), a FLASH-EPROM or any other flash memory, Non-Volatile Random Access Memory (NVRAM), a cache, a register, any other memory chip or cartridge, and networked versions of the same. 
     While the present disclosure has been shown and described with reference to particular embodiments thereof, it will be understood that the present disclosure can be practiced, without modification, in other environments. The foregoing description has been presented for purposes of illustration. It is not exhaustive and is not limited to the precise forms or embodiments disclosed. Modifications and adaptations will be apparent to those skilled in the art from consideration of the specification and practice of the disclosed embodiments. 
     Computer programs based on the written description and disclosed methods are within the skill of an experienced developer. Various programs or program modules can be created using any of the techniques known to one skilled in the art or can be designed in connection with existing software. For example, program sections or program modules can be designed in or by means of .Net Framework, .Net Compact Framework (and related languages, such as Visual Basic, C, etc.), Java, C++, Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with included Java applets. 
     Moreover, while illustrative embodiments have been described herein, the scope of any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of aspects across various embodiments), adaptations and/or alterations as would be appreciated by those skilled in the art based on the present disclosure. The limitations in the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described in the present specification or during the prosecution of the application. The examples are to be construed as non-exclusive. Furthermore, the steps of the disclosed methods may be modified in any manner, including by reordering steps and/or inserting or deleting steps. It is intended, therefore, that the specification and examples be considered as illustrative only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.