Patent Publication Number: US-10762115-B2

Title: Automated response system using smart data

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This patent application is a continuation of U.S. patent application Ser. No. 15/689,138 filed Aug. 29, 2017 entitled “Automated Response System Using Smart Data”. This application claims priority to and the benefit of the above-identified application which is fully incorporated by reference herein in its entirety. 
    
    
     BACKGROUND 
     Aspects of the disclosure relate to data processing, artificial intelligence, knowledge processing, and computer-implemented rule-based reasoning having specific pattern matching or control techniques. In particular, one or more aspects of the disclosure relates to analyzing smart data using an automated response system. 
     In some instances, a support system for an enterprise organization may receive user feedback about the enterprise organization. The user feedback may describe a vast range of issues, and the system may solve each issue differently. As the enterprise organization grows more complex, the amount of user feedback, including the amount of issues and solutions associated with the user feedback, may increase exponentially. Therefore, the system may have difficulty identifying connections between user feedback information as the amount of user feedback increases. Further, the system may have difficulty leveraging the user feedback information to identify new and different solutions for the vast range of issues. 
     SUMMARY 
     Aspects of the disclosure provide effective, efficient, scalable, and convenient technical solutions that address and overcome the technical problems associated with analyzing smart data using an automated response system. 
     In accordance with one or more embodiments, a computing platform having at least one processor, a memory, and a communication interface may receive, by the at least one processor, via the communication interface, and from a user device, user feedback information comprising user feedback corresponding to an enterprise organization. Thereafter, the computing platform may identify, based on the user feedback information, identification information comprising a sender of the user feedback and an issue corresponding to the user feedback. Then, the computing platform may retrieve, from a hierarchical rules server and based on the identification information, hierarchy information comprising a hierarchy ranking of a plurality of automated responses. Subsequently, the computing platform may determine, based on analyzing the hierarchy information and the user feedback, an automated response from the plurality of automated responses. Next, the computing platform may generate, by the at least one processor, one or more commands directing an external response server to execute the automated response. Then, the computing platform may transmit, via the communication interface and to the external response server, the one or more commands directing an external response server to execute the automated response. 
     In some embodiments, the computing platform may retrieve, from the hierarchical rules server and based on the identification information, historical information corresponding to the sender of the user feedback. Subsequently, the determining the automated response from the plurality of automated responses may be further based on the historical information. In some embodiments, the historical information may comprise information indicating a number of times the sender of the user feedback submitted previous user feedback associated with the issue. In some embodiments, the historical information of the issue may comprise historical information from a plurality of user feedback and from a plurality of different senders. 
     In some embodiments, the computing platform may determine, based on the user feedback information, a biometric factor corresponding to the user feedback. Subsequently, the determining the automated response from the plurality of automated responses may be based on the biometric factor. In some embodiments, the receiving the user feedback information may comprise receiving the user feedback information from the user device via an email feedback system, a text feedback system, or an interactive voice response feedback system. 
     In some embodiments, the hierarchy information may further comprise a hierarchy ranking of a plurality of issues. Subsequently, the computing platform may determine, based on comparing the issue with the hierarchy ranking of the plurality of issues, the hierarchy ranking of the issue. Then, the determining the automated response from the plurality of automated responses may be further based on the hierarchy ranking of the issue. In some embodiments, the computing platform may receive, via the communication interface and from the external response server, a status of the user feedback. Subsequently, the computing platform may change, based on the status of the user feedback, the automated response in the hierarchy ranking of the plurality of automated responses. 
     In some embodiments, the computing platform may generate a web user interface corresponding to the user feedback, wherein the web user interface indicates the automated response and a status update for the user feedback. Subsequently, the computing platform may transmit, via the communication interface and to the user device, the web user interface. 
     In some embodiments, the computing platform may determine a trend for each of the plurality of automated responses, wherein the trend corresponds to an efficiency of a corresponding automated response from the plurality of automated responses. Thereafter, the computing platform may change, based on the trend for each of the plurality of automated responses, the hierarchy ranking of the plurality of automated responses. 
     These features, along with many others, are discussed in greater detail below. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure is illustrated by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements and in which: 
         FIGS. 1A, 1B, and 1C  depict an illustrative computing environment for analyzing smart data using an automated response system in accordance with one or more example embodiments; 
         FIGS. 2A, 2B, 2C, 2D, and 2E  depict an illustrative event sequence for analyzing smart data using an automated response system in accordance with one or more example embodiments; 
         FIGS. 3 and 4  depict example graphical user interfaces for analyzing smart data using an automated response system in accordance with one or more example embodiments; and 
         FIG. 5  depicts an illustrative method for analyzing smart data using an automated response system in accordance with one or more example embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description of various illustrative embodiments, reference is made to the accompanying drawings, which form a part hereof, and in which is shown, by way of illustration, various embodiments in which aspects of the disclosure may be practiced. It is to be understood that other embodiments may be utilized, and structural and functional modifications may be made, without departing from the scope of the present disclosure. 
     It is noted that various connections between elements are discussed in the following description. It is noted that these connections are general and, unless specified otherwise, may be direct or indirect, wired or wireless, and that the specification is not intended to be limiting in this respect. 
       FIGS. 1A and 1B  depict an illustrative computing environment for analyzing smart data using an automated response system in accordance with one or more example embodiments. Referring to  FIG. 1A , computing environment  100  may include one or more computing devices and/or other computer systems. For example, computing environment  100  may include an automated response computing platform  110 , a hierarchical rules server  120 , a user device  130 , an email feedback system  140 , a text feedback system  150 , an interactive voice response (IVR) feedback system  160 , an administrative device  170 , and/or an external response system  180 . 
     Automated response computing platform  110  may be configured to analyze user feedback and smart data by controlling and/or directing actions of other devices and/or computer systems, and/or perform other functions, as discussed in greater detail below. In some instances, automated response computing platform  110  may perform and/or provide one or more techniques to analyze user feedback and smart data. 
     Hierarchical rules server  120  may be configured to store and/or maintain hierarchical rules information to analyze user feedback and smart data. For example, hierarchical rules server  120  may be configured to store and/or maintain hierarchy information corresponding to clients, issues, actions in response to issues, and/or historical user information. In some instances, the hierarchical rules server  120  might not be another entity, but the functionalities of the hierarchical rules server  120  may be included within the automated response computing platform  110 . 
     User device  130  may be configured to be used by one or more users of computing environment  100 . For example, the user device  130  may be configured to display, present, and/or otherwise provide one or more user interfaces that enable one or more users to provide user feedback corresponding to an enterprise organization. The user device  130  may receive, from the one or more users, user input or selections. Further, the user device  130  may send the user input or selections to the automated response computing platform  110  and/or one or more other computer systems and/or devices in computing environment  100 . The user device  130  may receive, from the automated response computing platform  110  and/or one or more other computer systems and/or devices in computing environment  100 , information or data in response to the user input or selection. 
     In some examples, the user device  130  may receive, from one or more users, user feedback information indicating feedback corresponding to an enterprise organization. After receiving the user feedback information from the user, the user device  130  may transmit the user feedback information to an email feedback system  140 , a text feedback system  150 , and/or an interactive voice response (IVR) feedback system  160 . In some embodiments, the user device  130  may include a microphone and/or voice recording system. For example, the microphone and/or voice recording system may be used to record a voice recording from the user. The voice recording may be a recorded voice message of the user feedback. 
     Email feedback system  140  may be configured to receive and transmit user feedback information. For example, the user device  130  may transmit one or more emails to the email feedback system  140 . The emails may indicate user feedback corresponding to the enterprise organization. The email feedback system  140  may receive the emails from the user device  130  and may transmit the emails to the automated response computing platform  110 . In some instances, the email feedback system  140  may receive a plurality of emails from a plurality of different user devices. Each user device, such as user device  130 , may correspond to a different user. 
     Text feedback system  150  may be configured to receive and transmit user feedback information. For example, the user device  130  may transmit one or more text messages to the text feedback system  150 . The text messages may indicate user feedback corresponding to the enterprise organization. The text feedback system  150  may receive the text messages from the user device  130  and may transmit the text messages to the automated response computing platform  110 . In some instances, the text feedback system  150  may receive a plurality of text messages from a plurality of different user devices. Each user device, such as user device  130 , may correspond to a different user. 
     Interactive voice response (IVR) feedback system  160  may be configured to receive and transmit user feedback information. For example, the user device  130  may transmit one or more voice messages to the IVR feedback system  160 . The voice messages may indicate user feedback corresponding to the enterprise organization. The IVR feedback system  160  may receive the voice messages from the user device  130 , and may convert the voice messages into text. Additionally, and/or alternatively, the IVR feedback system  160  may transmit the converted text messages to the automated response computing platform  110 . In some instances, the IVR feedback system  160  may receive a plurality of voice messages from a plurality of different user devices. Each user device, such as user device  130 , may correspond to a different user. 
     Administrative device  170  may be configured to be used by one or more users of computing environment  100 . For example, the administrative device  170  may be configured to display, present, and/or otherwise provide one or more user interfaces that enable a user (e.g., an administrator) to monitor a status of the user feedback. The administrative device  170  may receive, from the one or more administrators, user input or selections and send the user input or selections to the automated response computing platform  110  and/or one or more other computer systems and/or devices in computing environment  100 . The administrative device  170  may receive, from the automated response computing platform  110  and/or one or more other computer systems and/or devices in computing environment  100 , information or data in response to the user input or selection. 
     External response system  180  may be a computing system configured to offer any desired service, and may execute an automated response. For example, the external response system  180  may execute an automated response based on the user feedback. Additionally, and/or alternatively, external response system  180  may provide one or more interfaces that facilitate communications with other systems (e.g., automated response computing platform  110 , hierarchical rules server  120 , a user device  130 , an email feedback system  140 , a text feedback system  150 , IVR feedback system  160 , an administrative device  170 , and an external response system  180 ) in computing environment  100 . In some instances, the external response system  180  may receive, from the automated response computing platform  110 , one or more commands to execute an automated response based on the user feedback, as discussed in greater detail below. In some examples, the external response system  180  may include a plurality of automated response systems, and each automated response system may execute a plurality of different automated responses based on the user feedback. 
     In one or more arrangements, hierarchical rules server  120 , user device  130 , email feedback system  140 , text feedback system  150 , IVR feedback system  160 , administrative device  170 , and/or external response system  180  may be any type of computing device capable of providing a user interface, receiving input via the user interface, and communicating the received input to one or more other computing devices. For example, hierarchical rules server  120 , user device  130 , email feedback system  140 , text feedback system  150 , IVR feedback system  160 , administrative device  170 , and/or external response system  180  may, in some instances, be and/or include server computers, desktop computers, laptop computers, tablet computers, smart phones, or the like that may include one or more processors, memories, communication interfaces, storage devices, and/or other components. As noted above, and as illustrated in greater detail below, any and/or all of hierarchical rules server  120 , user device  130 , email feedback system  140 , text feedback system  150 , IVR feedback system  160 , administrative device  170 , and/or external response system  180  may, in some instances, be special-purpose computing devices configured to perform specific functions. 
     Computing environment  100  also may include one or more computing platforms. For example, and as noted above, computing environment  100  may include the automated response computing platform  110 . As illustrated in greater detail below, the automated response computing platform  110  may include one or more computing devices configured to perform one or more of the functions described herein. For example, the automated response computing platform  110  may include one or more computers (e.g., laptop computers, desktop computers, servers, server blades, or the like). 
     Computing environment  100  also may include one or more networks, which may interconnect one or more of automated response computing platform  110 , hierarchical rules server  120 , user device  130 , email feedback system  140 , text feedback system  150 , IVR feedback system  160 , administrative device  170 , and/or external response system  180 . For example, computing environment  100  may include network  190 . Network  190  may include one or more sub-networks (e.g., local area networks (LANs), wide area networks (WANs), or the like). For example, network  190  may include a private sub-network that may be associated with a particular organization (e.g., a corporation, financial institution, educational institution, governmental institution, or the like) and that may interconnect one or more computing devices associated with the organization. For example, automated response computing platform  110 , hierarchical rules server  120 , user device  130 , email feedback system  140 , text feedback system  150 , IVR feedback system  160 , administrative device  170 , and/or external response system  180  may be associated with an enterprise organization, and a private sub-network included in network  190  and associated with and/or operated by the organization may include one or more networks (e.g., LANs, WANs, virtual private networks (VPNs), or the like) that interconnect automated response computing platform  110 , hierarchical rules server  120 , user device  130 , email feedback system  140 , text feedback system  150 , IVR feedback system  160 , administrative device  170 , and/or external response system  180 . Network  190  also may include a public sub-network that may connect the private sub-network and/or one or more computing devices connected thereto (e.g., automated response computing platform  110 , hierarchical rules server  120 , user device  130 , email feedback system  140 , text feedback system  150 , IVR feedback system  160 , administrative device  170 , and/or external response system  180 ) with one or more networks and/or computing devices that are not associated with the organization. 
     Referring to  FIG. 1B , automated response computing platform  110  may include one or more processors  111 , memory  112 , and communication interface  118 . A data bus may interconnect processor(s)  111 , memory  112 , and communication interface  118 . Communication interface  118  may be a network interface configured to support communication between the automated response computing platform  110  and one or more networks (e.g., network  190 ). Memory  112  may include one or more program modules having instructions that when executed by processor(s)  111  cause the automated response computing platform  110  to perform one or more functions described herein and/or one or more databases that may store and/or otherwise maintain information which may be used by such program modules and/or processor(s)  111 . In some instances, the one or more program modules and/or databases may be stored by and/or maintained in different memory units of the automated response computing platform  110  and/or by different computing devices that may form and/or otherwise make up the automated response computing platform  110 . For example, memory  112  may have, store, and/or include a data aggregation engine  113 , an issue routing engine  114 , a user interface module  115 , an analytics module  116 , and an autonomous response engine  117 . Autonomous response engine  117  may include instructions that direct and/or cause automated response computing platform  110  to analyze smart data and user feedback to determine an automated response, as discussed in greater detail below. 
     Data aggregation engine  113  may aggregate data used by the autonomous response engine  117  and/or the automated response computing platform  110 . For example, after receiving the user feedback from the email feedback system  140 , the text feedback system  150 , and/or the interactive voice response feedback system  160 , the data aggregation engine  113  may aggregate the user feedback. In some embodiments, the data aggregation engine  113  may store the user feedback used by the autonomous response engine  117  and/or the automated response computing platform  110  in determining an automated response and/or in performing other functions. 
     Issue routing engine  114  may route issues used by the autonomous response engine  117  and/or the automated response computing platform  110 . For example, after receiving the user feedback from the email feedback system  140 , the text feedback system  150 , and/or the interactive voice response feedback system  160 , the issue routing engine  114  may determine an issue corresponding to the user feedback. Based on the issue, the issue routing engine  114  may route the issue to the external response system  180 . 
     User interface module  115  may generate a user interface for the user device  130  and/or the administrative device  170 . For example, after receiving the user feedback from the email feedback system  140 , the text feedback system  150 , and/or the interactive voice response feedback system  160 , the user interface module  115  may generate an interactive user interface for the user feedback. In some embodiments, based on the issue and/or status of the user feedback, the user interface module  115  may generate and update the user interface as the user feedback is analyzed by the automated response computing platform  110 . 
     Analytics module  116  may receive and use hierarchy information to analyze the user feedback. For example, after receiving the user feedback from the email feedback system  140 , the text feedback system  150 , and/or the interactive voice response feedback system  160 , the analytics module  116  may receive hierarchy information from the hierarchical rules server  120 . In some instances, based on the hierarchy information, the analytics module  116  may analyze and determine an automated response for the user feedback. 
     Referring to  FIG. 1C , hierarchical rules server  120  may include one or more processors  121 , memory  122 , and communication interface  126 . A data bus may interconnect processor(s)  121 , memory  122 , and communication interface  126 . Communication interface  126  may be a network interface configured to support communication between hierarchical rules server  120  and one or more networks (e.g., network  190 ). Memory  122  may include one or more program modules having instructions that when executed by processor(s)  121  cause the hierarchical rules server  120  to perform one or more functions described herein and/or one or more databases that may store and/or otherwise maintain information which may be used by such program modules and/or processor(s)  121 . In some instances, the one or more program modules and/or databases may be stored by and/or maintained in different memory units of the hierarchical rules server  120  and/or by different computing devices that may form and/or otherwise make up the hierarchical rules server  120 . For example, memory  122  may have, store, and/or include a hierarchical rules module  123 , a hierarchical rules database  124 , and a machine learning engine  125 . Hierarchical rules server  120  may include instructions that direct and/or cause hierarchical rules server  120  to analyze user feedback and smart data to determine an automated response, as discussed in greater detail below. Hierarchical rules database  124  may store information used by the hierarchical rules module  123  and/or the hierarchical rules server  120  in analyzing user feedback, smart data, and/or in performing other functions. Machine learning engine  125  may have instructions that direct and/or cause the hierarchical rules server  120  to set, define, and/or iteratively redefine optimization rules, techniques and/or other parameters used by hierarchical rules server  120  and/or other systems in computing environment  100 . 
       FIGS. 2A, 2B, 2C, 2D, and 2E  depict an illustrative event sequence for analyzing smart data using an automated response system in accordance with one or more example embodiments. Referring to  FIG. 2A , at step  201 , the user device  130  may transmit user feedback to the IVR feedback system  160 , the email feedback system  140 , and/or the text feedback system  150 . For example, at step  201 , the user device  130  may receive user feedback information from a user.  FIG. 3  may display a sample user feedback template. For instance, as shown in  FIG. 3 , graphical user interface  300  may include one or more fields, controls, and/or other elements that may allow a user to input user feedback information. The graphical user interface  300  may allow a user to input a sender, subject, and/or content of the user feedback. For example, the sender may be the user (e.g., client of the enterprise organization) transmitting the user feedback. The subject may be a summary of the corresponding content of the user feedback. Additionally, in some instances, the subject may be a subject line of the email and/or text message. In some examples, the user feedback might not include a sender and/or subject. For example, the user device  130  may transmit an audio and/or an audiovisual file indicating the user feedback. The audio and/or audiovisual file might not include a subject and/or sender, but may include a phone number and/or other identification associated with the user device  130 . 
     Further, the user feedback information may indicate information corresponding to user feedback about an enterprise organization. For example, a user, using the user device  130 , may provide user feedback corresponding to the enterprise organization. User feedback may include surveys, rating systems, and/or questions about the mobile application. Additionally, in some examples, the user feedback may include an issue corresponding to the enterprise organization. The issue may indicate a problem, dilemma, question and/or other feedback concerning the enterprise organization. For example, the user feedback may indicate an issue, such as that a user&#39;s password has expired. A support system for the enterprise organization may use the user feedback information to assist the user in solving the issue indicated in the user feedback. 
     In some embodiments, the user device  130  may include text, email, and/or voice recording capabilities. For example, the user feedback may be in a plurality of formats, including text, email, audio, and/or audiovisual file formats. The user device  130  may receive the user feedback information as text feedback, email feedback, voice recorded feedback, and/or video recorded feedback. For instance, a microphone and/or a voice recording system may assist the user device  130  to record a voice recording of the user feedback. After user device  130  records the voice recording of the user feedback, the user device  130  may transmit the user feedback comprising the voice recording to the IVR feedback system  160 . Additionally, and/or alternatively, the user device  130  may transmit user feedback information comprising textual information and/or other non-voice information. For example, the user device  130  may transmit an email or text message indicating the user feedback information to the email feedback system  140  and/or the text feedback system  150 . Further, in some instances, the user device  130  may include only a voice recording of the user feedback, a combination of a voice recording of the user feedback and textual information and/or other non-voice information, or only textual information and/or other non-voice information. 
     At step  202 , the automated response computing platform  110  may receive the user feedback information from the email feedback system  140 , the text feedback system  150 , and/or the IVR feedback system  160 . In some examples, after receiving the user feedback information, the automated response computing platform  110  and/or the IVR feedback system may convert the user feedback, comprising the voice recording of the user feedback, into text feedback. In some instances, the email feedback system  140 , the text feedback system  150 , and/or the IVR feedback system  160  may receive a plurality of user feedback from a plurality of different user devices. The plurality of different user devices may be associated with the same user or a plurality of different users. 
     At step  203 , the automated response computing platform  110  may identify an issue corresponding to the user feedback. For example, after receiving the user feedback at step  202 , the automated response computing platform  110  may analyze the user feedback, such as the sender, subject and/or content of the user feedback, to identify an issue corresponding to the user feedback. In some embodiments, the user feedback may correspond to a support request indicating an issue. For example, the user may submit a support request or a support ticket for an issue, such as the user cannot log on an account due to an expired password. The automated response computing platform  110  may identify the issue, “expired password,” by analyzing the subject and/or text of the support request. 
     In some examples, the administrative device  170  may transmit a multitude of known issues and/or problems corresponding to the enterprise organization. Each issue and/or problem may correspond with keywords and/or synonyms of the keywords. For example, the term “expired” may be a keyword for “expired password.” Additionally, and/or alternatively, synonyms of “expired” may also be keywords used to identify the issue as “expired password.” The automated response computing platform  110  may store, in memory  112 , the multitude of issues corresponding to the enterprise organization. Then, after receiving the user feedback, the automated response computing platform  110  may parse through the user feedback for these keywords and/or synonyms. After identifying one or more keywords and/or synonyms within the user feedback, the automated response computing platform  110  may identify the issue from the corresponding keyword and/or synonyms. 
     In some instances, the automated response computing platform  110  may create a new issue based on the subject and/or content of the user feedback. For example, the automated response computing platform  110  may create a new issue from the subject and/or content of the user feedback. For instance, based on the phrases and/or words within the content of the user feedback (e.g., “does not work,” “problem area,” or “issue”), the automated response computing platform  110  may analyze the words and/or phrases, and create a new issue from the subject and/or content of the user feedback. Additionally, in some embodiments, the automated response computing platform  110  may parse through the user feedback and might not determine any keywords and/or synonyms. In such situations, the automated response computing platform  110  may create a new issue for the user feedback. 
     In some embodiments, the user feedback might not include a subject. For example, as mentioned earlier, the user feedback may be a voice recording, such as an audio or an audiovisual file. The automated response computing platform  110  may identify the issue from the content of the voice recording (e.g., the converted text file associated with the voice recording). Additionally, and/or alternatively, in some instances, while the user device  130  may transmit user feedback information including the sender and/or the subject, the automated response computing platform  110  may eventually receive the user feedback information without the sender and/or the subject. For example, the email feedback system  140  may transmit the content of the user feedback, but due to an error, the email feedback system  140  might not transmit the sender and/or subject of the user feedback. In such instances, the automated response computing platform  110  may analyze the content of the user feedback to identify the sender, subject, and/or issue of the user feedback. 
     At step  204 , the automated response computing platform  110  may identify a sender of the user feedback. For example, automated response computing platform  110  may identify a sender of the user feedback based on an email address, phone number, and/or other identification information corresponding to the user transmitting the user feedback. Referring back to  FIG. 3 , in some instances, the sender information may be easily identifiable in the user feedback. For example, the automated response computing platform  110  may identify the sender from the email address and/or phone number associated with the user feedback. In some embodiments, when the sender information is not easily identifiable, the automated response computing platform  110  may analyze the user feedback, similar to step  203  above, to determine the sender of the user feedback. For example, the user feedback may be a text file that was converted from a voice recording. The automated response computing platform  110  may parse the user feedback (e.g., text file) to identify phrases and/or words of the user feedback. For instance, the automated response computing platform  110  may determine phrases from the user feedback (e.g., “My name is . . . ” or “My email is . . . ”) and use these phrases to identify a sender of the user feedback. 
     Referring to  FIG. 2B , at step  205 , the automated response computing platform  110  may determine biometric factors associated with the user feedback. For example, the automated response computing platform  110  may analyze the user feedback to determine biometric factors for the user feedback. In some examples, the automated response computing platform  110  may parse through the user feedback to determine words and/or phrases of the user feedback associated with the biometric factors. Additionally, and/or alternatively, the automated response computing platform  110  may determine the biometric factor based on the issue of the user feedback. For example, some issues, such as “employee appreciation,” may be associated with a good or “happy” biometric factor, whereas some issues, such as “lost driver&#39;s license” or “lost passport,” may be associated with a bad or “angry” biometric factor. 
     At step  206 , the automated response computing platform  110  may generate an identifier for the user feedback. For example, at step  206 , automated response computing platform  110  may generate an identifier, such as a tracking number, tracking identifier, and/or another identifier, for the received user feedback. In some instances, the automated response computing platform  110  may generate the identifier based on whether the received user feedback is from the email feedback system  140 , the text feedback system  150 , and/or the IVR feedback system  160 . Further, in some embodiments, the automated response computing platform  110  may use the identifier to track the user feedback. For instance, the user may seek to track the status of the user feedback, such as a support ticket. The user, using user device  130 , may transmit a request for a status of the user feedback, such as a request to determine whether the issue has been resolved. The automated response computing platform  110  may use the generated identifier for the user feedback to determine the status of the user feedback. Then, the automated response computing platform  110  may transmit the status of the user feedback to the user device  130 . 
     At step  207 , the automated response computing platform  110  may aggregate the user feedback information. For example, automated response computing platform  110  may receive a plurality of user feedback from a plurality of different user devices. At step  207 , the automated response computing platform  110  may aggregate, using data aggregation engine  113 , the plurality of user feedback based on the sender, issue, subject, biometric factors, and/or file format (e.g., email, text, audio, and/or audiovisual). In some examples, the automated response computing platform  110  may use the aggregated user feedback to determine the automated response for the user feedback. For instance, the automated response computing platform  110  may aggregate the user feedback from the same sender, and determine an automated response based on the aggregated user feedback. 
     Further, in some embodiments, the automated response computing platform  110  may aggregate the received user feedback for the same issue (e.g., “password expired”). In such embodiments, the automated response computing platform  110  may determine an automated response for the aggregated user feedback with the same issue. Additionally, in some instances, the automated response computing platform  110  may aggregate the received user feedback for the same subject. For example, a chain of emails may include the same subject line. The automated response computing platform  110  may aggregate the chain of emails that include the same subject line and determine an automated response for the chain of emails. Also, in some examples, the automated response computing platform  110  may aggregate the user feedback using the biometric factors. For example, many users may submit user feedback indicating appreciation for employees of the enterprise organization. The automated response computing platform  110  may aggregate the user feedback associated with the “happy” biometric factor, and then determine an automated response based on the biometric factor. 
     At step  208 , the automated response computing platform  110  may retrieve historical information for the sender from the hierarchical rules server  120 . For example, a sender of the user feedback may be a client of the enterprise organization. The automated response computing platform  110  may retrieve the historical information for the sender (e.g., client) based on the identified sender, identified issue, and/or subject of the user feedback. In some instances, the historical information may include previously received user feedback from the sender (e.g., client). For example, the sender, using user device  130  and/or another user device associated with the sender, may have previously transmitted user feedback indicating one or more issues. The automated response computing platform  110  may retrieve historical information, including the previous user feedback from the sender. In some embodiments, the historical information may include previously received user feedback related to the subject. For example, the received user feedback at step  202  may be from a chain of emails, text messages, and/or corresponding voice messages from the sender. The automated response computing platform  110  may retrieve historical information, including the chain of emails, text messages, and/or corresponding voice messages from the sender. 
     In some instances, the automated response computing platform  110  may retrieve a previous automated response and/or a previous biometric factor associated with the sender. For example, after receiving previous user feedback, the automated response computing platform  110  may have performed an automated action or automated response. Additionally, the automated response computing platform  110  may have associated the previously received user feedback with certain biometric factors. 
     Referring to  FIG. 2C , at step  209 , the automated response computing platform  110  may retrieve historical information for the issue from the hierarchical rules server  120 . For example, the automated response computing platform  110  may retrieve the historical information for the issue based on the identified sender, identified issue, and/or subject of the user feedback. In some examples, the historical information may include previously received user feedback for the issue. For example, multiple senders may have transmitted a plurality of previous user feedback about the issue. The automated response computing platform  110  may retrieve historical information, including the plurality of previous user feedback about the issue. 
     In some instances, the automated response computing platform  110  may retrieve a previous automated response and/or a previous biometric factor associated with the issue. For example, after receiving previous user feedback, the automated response computing platform  110  may have performed an automated action or automated response. Additionally, the automated response computing platform  110  may have associated previously received user feedback with certain biometric factors. 
     At step  210 , the automated response computing platform  110  may retrieve hierarchy information for issues. For example, at step  210 , automated response computing platform  110  may retrieve, from the hierarchical rules server  120 , hierarchy information for the issues. For instance, each issue may be treated differently based on the importance of the issue for the enterprise organization. Some issues may be more important than other issues, and the hierarchy information may indicate a higher priority and/or a preference for the more important issues. Based on the higher priority and/or preference, the hierarchical rules server  120  may create, monitor, and/or update a hierarchy ranking of issues using the machine learning engine  125 . At step  210 , the automated response computing platform  110  may retrieve the hierarchy information indicating the hierarchy ranking of issues from the hierarchical rules server  120 . 
     In some instances, after retrieving the hierarchy ranking of issues, the automated response computing platform  110  may determine a position of the issue corresponding to the user feedback within the hierarchy ranking of issues. For example, the hierarchy ranking of issues may include the issues and a corresponding ranking associated with each issue. After identifying the issue at step  203 , the automated response computing platform  110  may compare the identified issue with hierarchy ranking of issues to determine the corresponding ranking of the issue. Additionally, and/or alternatively, in some examples, rather than a corresponding ranking associated with each issue, the ranking may correspond to a numerical (e.g., 1-100) and/or alphabetical (e.g., A-E) ranking. The automated response computing platform  110  may compare the identified issue at step  203  with the hierarchy ranking of issues to determine the numerical and/or alphabetical ranking of the issue. A high numerical or alphabetical ranking (e.g., 90 or A) may correspond to a high priority issue, whereas a low numerical or alphabetical ranking (e.g., 20 or E) may correspond to a low priority issue. 
     At step  211 , the automated response computing platform  110  may retrieve, from the hierarchical rules server  120 , hierarchy information indicating actions and/or responses for the issues. For example, based on the client and/or the issue, the hierarchical rules server  120  may create a hierarchy of automated responses to address and/or solve the issues. Some of the automated responses may be more severe or effective than others. And, based on the severity and/or effectiveness of the automated responses, the hierarchical rules server  120  may create, update, and/or monitor a hierarchy ranking of the automated responses using the machine learning engine  125 . For instance, more severe automated responses, such as “contact supervisor,” may be higher ranked than less severe automated responses, such as “send reminder email. 
     In some instances, the automated responses may route the issue associated with the user feedback to one or more external response systems  180 . For example, the one or more external response systems  180  may be a support system, such as a help desk, that resolves issues for the enterprise organization. The external response systems  180  (e.g., the support systems) may be ranked by their effectiveness in resolving issues. Further, each external response systems  180  may perform unique actions and/or responses that range in severity. For instance, some external response systems  180  may perform more severe actions and/or responses, whereas other external response systems  180  may perform less severe actions and/or responses. As explained below, after determining the automated response, the automated response computing platform  110  may route, using the issue routing engine  114 , the issue determined at step  203 , to one or more support systems associated with the enterprise organization. 
     At step  212 , the automated response computing platform  110  may determine a subset of the actions and/or responses from the hierarchy of actions and/or responses. For example, based on the ranking of the issue and/or the historical information for the user feedback, the automated response computing platform  110  may determine a subset of automated responses from the hierarchy of automated responses. In some embodiments, a higher ranked issue may be associated with a higher subset or part of the hierarchy ranking of the automated responses, whereas a lower ranked issue may indicate a lower subset or part of the hierarchy ranking of automated responses. Additionally, and/or alternatively, the automated response computing platform  110  may use the historical information to narrow the subset of automated responses. 
     In some instances, an automated response may be incompatible with particular issues and/or clients. For example, the automated action of “contacting a supervisor” may be incompatible with the issue “lost passport.” As such, the automated response computing platform  110  may remove incompatible automated responses from the hierarchy ranking of automated responses prior to determining the subset of automated responses. Further, in some examples, an automated response may route the issue to an external response system  180  that is not capable of resolving the issue. As such, the automated response computing platform  110  may remove automated responses that route issues to external response systems  180  that are not capable of resolving the issue. 
     At step  213 , the automated response computing platform  110  may determine an escalation of the user feedback. For example, after retrieving the historical information, the automated response computing platform  110  may escalate the user feedback based on the number of times the automated response computing platform  110  receives user feedback indicating the same client and/or the same issue. For example, by escalating the user feedback, the automated response computing platform  110  may move the subset of responses up the hierarchy ranking of responses. In some instances, the automated response computing platform  110  may determine the number of times the automated response computing platform  110  has received the user feedback indicating the same client and/or the same issue. Afterwards, as explained below, the automated response computing platform  110  may determine the automated response in the subset based on the number of times the automated response computing platform  110  received the user feedback. 
     Referring to  FIG. 2D , at step  214 , the automated response computing platform  110  may determine trends associated with the user feedback. For example, the automated response computing platform  110  may analyze, using the analytics module  116 , trends for the identified sender and/or client, identified issue, historical information, and/or automated responses to issues. Based on the historical information (e.g., past user feedback), the trends may indicate the performance (e.g., success, failure, and/or efficiency) associated with the identified client, identified issue, and/or the automated responses. In some embodiments, the automated response computing platform  110  may determine the performance by the number of received user feedback describing the particular issue and/or the particular client over a period of time. For example, if the number surpasses a pre-defined threshold for a period of time, the automated response computing platform  110  may determine that the actions and/or responses that are in response to the issue might not be effective. Additionally, if the amount is below the pre-defined threshold for a period of time, the automated response computing platform  110  may determine the action and/or response that are in response to the issue may be effective. 
     In some instances, the automated response computing platform  110  may determine the trends for the identified client, the identified issue, and/or the identified automated response. For example, the automated response computing platform  110  may determine the performance of the automated responses for a plurality of issues. Based on the performance, the automated response computing platform  110  may determine that particular automated responses may be more or less effective for particular issues. Additionally, and/or alternatively, the automated response computing platform  110  may determine the performance of the automated responses for a plurality of clients. Based on the performance, the automated response computing platform  110  may the determine that particular automated responses may be more or less effective for particular clients. Also, the automated response computing platform  110  may determine the trends for the automated responses. For example, the automated response computing platform  110  may determine the performance of the automated response for a plurality of clients over a plurality of issues. Based on the performance, the automated response computing platform  110  may the determine that the automated response may be more or less effective for particular issues and/or particular clients. 
     At step  215 , the automated response computing platform  110  may determine the automated response for the user feedback. For example, the automated response computing platform  110  may determine the automated response based on the determined subset of actions and/or responses, the escalation of user feedback, the biometric factor, and/or the trends associated with the user feedback. In some instances, the automated response computing platform  110  may first determine whether to change the subset of actions and/or responses based on the escalation of the user feedback (e.g., moving up the subset of actions and/or responses in the hierarchy ranking). Afterwards, the automated response computing platform  110  may determine, from the trends at step  214 , the effectiveness for each of the actions and/or responses in the subset. The automated response computing platform  110  may determine the action and/or response from the subset based on the trends for the identified issue and/or the identified client. In some examples, the automated response computing platform  110  may determine the automated response and/or action in the subset based on the number of times the automated response computing platform  110  receives the user feedback indicating the client and/or issue. For instance, the automated response computing platform  110  may select a different and/or more severe automated response each time the automated response computing platform  110  receives user feedback indicating the client and/or issue. 
     In some embodiments, the automated response computing platform  110  may determine the automated response in the subset based on the biometric factor associated with the user feedback. For example, based on a “good” or “happy” biometric factor, the automated response computing platform  110  may select a less severe automated response from the subset of automated responses. In other examples, based on a “bad” or “angry” biometric factor, the automated response computing platform  110  may select a more severe automated response. 
     In some instances, computing environment  100  may include a plurality of different external response systems  180 . For example, the different external response systems  180  may be associated with different support systems for the enterprise organization. Additionally, the different external response systems  180  may be tasked with executing different automated responses. After determining the automated response, the automated response computing platform  110  may determine the external response system  180  that is tasked with executing the automated response. Then, the automated response computing platform  110  may route, using the issue routing engine  114  and based on the factors listed above, the issue and/or user feedback to the appropriate external response system  180 . 
     At step  216 , the automated response computing platform  110  may transmit the automated response. For example, after determining the automated response, the automated response computing platform  110  may transmit one or more commands directing the external response system  180  to execute the automated response. In some examples, the automated response may route the issue and/or the user feedback to the appropriate external response system  180 . Thus, at step  216 , after determining the appropriate external response system  180 , the automated response computing platform  110  may transmit or route the user feedback, the issue, and/or additional actions and/or responses to the appropriate external response system  180 . 
     At step  217 , the automated response computing platform  110  may generate a user interface for the user feedback. For example, the automated response computing platform  110  may generate, using the user interface module  115 , a user interface for the user feedback. As shown in  FIG. 4 , graphical user interface  400  may include one or more fields, controls, and/or other elements that may allow a user (e.g., an administrator and/or a user) to interact with links and/or identify information associated the user feedback. For example, the graphical user interface  400  may allow a user and/or an administrator to use links and/or graphs to view the trend analysis  410 , the graph of the trend analysis  420 , the tracking identifier  430 , the current status  440 , the historical data for issue  450 , and/or related issues from other users  460 . The trend analysis and/or the graph of the trend analysis  420  may correspond to the determined trends associated with the user feedback at step  214 . The tracking identifier  430  may correspond with the generated tracking identifier at step  206 . The historical data for the issue  450  may correspond with the retrieved hierarchy information for the clients at step  208  and/or the hierarchy information for user feedback at step  209 . The related issue from other users  460  may correspond to the retrieved hierarchy information for issues at step  210 . Using graphical user interface  400 , the user may be able to obtain real-time live updates corresponding to the issue identified at step  203 . Further, the current status  440  may identify a status of the user feedback, such as completed, in progress, or need more assistance. Additionally, the current status  440  may identify the automated response determined at step  215 . The administrator may use graphical user interface  400  to determine the issue, automated response, and/or assess the performance of the automated response. 
     Referring to  FIG. 2E , at step  218 , the automated response computing platform  110  may determine a status of the user feedback based on the automated response. In some instances, after transmitting the one or more commands to execute the automated response, the automated response computing platform  110  may monitor whether new user feedback indicating the identified client and/or the identified issue has been received within a pre-defined period of time. After receiving the new user feedback indicating the identified client and/or issue, the automated response computing platform  110  may determine the status of the user feedback as not completed. However, when new user feedback indicating the identified client and/or issue has not been received in the pre-defined period of time, the automated response computing platform  110  may determine the status of the user feedback as completed. Further, in some embodiments, the automated response computing platform  110  may monitor whether new user feedback indicating the identified client and/or the identified issue surpasses a pre-defined threshold. Based on monitoring whether the new user feedback has been received in a pre-defined period of time and/or whether the new user feedback surpasses a pre-defined threshold, the automated response computing platform may update the performance for the automated response. 
     In some examples, after executing the determined automated response, the automated response computing platform  110  may transmit a request to the user device  130  and/or the administrative device  170  inquiring about the status. In response to the request, the automated response computing platform  110  may receive, from the user device  130  and/or administrative device  170 , an input indicating whether the issue has been completed, in progress, or not resolved. For example, referring back to  FIG. 4 , the user and/or administrator may select the current status link  440  to input the current status of the user feedback. 
     At step  219 , the automated response computing platform  110  may update the trends of the user feedback. For example, the automated response computing platform  110  may determine a client, issue, automated response, and/or status (e.g., performance) of the automated response as explained above. Based on the status or performance of the automated response, the automated response computing platform  110  may update, using the analytics module  116 , the trends for the identified client, identified issue, and/or identified automated response. When the automated response computing platform  110  receives a new user feedback, the automated response computing platform  110  may use the new and updated trends to determine the automated response for the client and/or issue. 
     At step  220 , the automated response computing platform  110  may generate one or more commands to update the hierarchy information based on the updated trends at step  219 . For example, the automated response computing platform  110  may generate one or more commands to update the hierarchy information including the hierarchy ranking of the issues and/or the automated responses based on the updated trends. At step  221 , the automated response computing platform  110  may transmit the commands to update the hierarchy information to the hierarchal rules server  120 . The hierarchal rules server  120  may receive the commands, and using the machine learning engine  125 , may continuously update the hierarchy information based on new user feedback. 
     By updating the trends at step  219 , the hierarchy information may also be updated. For example, the automated response computing platform  110  may analyze the status of the user feedback and may determine the automated response caused a change in status (e.g., completed, in progress, or not resolved). Then, the automated response computing platform  110  may generate one or more commands to add, remove, and/or change the hierarchy ranking of the issue and/or the automated responses based on the status or performance of the user feedback. For instance, the one or more commands may move the automated response in the hierarchy ranking of the automated responses up or down in the hierarchy ranking. Additionally, and/or alternatively, after updating the trends, the automated response computing platform  110  may transmit these updated trends to the hierarchical rules server  120 . After receiving these commands, the hierarchical rules server  120  may monitor and/or update, using machine learning engine  125 , the hierarchy ranking for the issue and/or the automated responses. In some instances, after receiving the status of each user feedback, the hierarchical rules server  120  may monitor and/or update the hierarchy ranking for the issue and/or the automated responses. Thus, the hierarchical rules server  120  may continue to set, define, and/or iteratively redefine optimization rules, techniques and/or other parameters used by hierarchical rules server  120  and/or the automated response computing platform  110  in determining the automated responses. For example, the hierarchical rules server  120  may continuously add, change, and/or remove issues, and/or automated responses from the hierarchy rankings as user feedback is analyzed. 
     Further, in some embodiments, a particular client and/or a particular issue may be associated with an individual hierarchy ranking of automated responses. In such instances, the automated response computing platform  110  may determine unique hierarchy rankings of automated responses for the particular clients and/or particular issues. Then, the automated response computing platform  110  may add, remove, and/or change the hierarchy ranking of the automated responses for particular clients and/or issues based on the determined status. For example, the status of the user feedback may indicate “completed” for the particular client and/or issue. The automated response computing platform  110  may add or change (e.g., move up) the automated response in the hierarchy ranking of automated responses for the client and/or issue. In other examples, the status of the user feedback may indicate “not resolved” for the particular client and/or issue. The automated response computing platform  110  may remove or change (e.g., move down) the automated response in the hierarchy ranking of automated responses for the client and/or issue. 
       FIG. 5  depicts an illustrative method for analyzing smart data using an automated response system in accordance with one or more example embodiments. Referring to  FIG. 5 , at step  505 , a computing platform having at least one processor, a memory, and a communication interface may receive, by the at least one processor, via the communication interface, and from a user device, user feedback information comprising user feedback corresponding to an enterprise organization. At step  510 , the computing platform may identify, based on the user feedback information, identification information comprising a sender of the user feedback and an issue corresponding to the user feedback. At step  515 , the computing platform may retrieve, from a hierarchical rules server and based on the identification information, hierarchy information comprising a hierarchy ranking of a plurality of automated responses. At step  520 , the computing platform may determine, based on analyzing the hierarchy information and the user feedback, an automated response from the plurality of automated responses. At step  525 , the computing platform may generate, by the at least one processor, one or more commands directing an external response server to execute the automated response. At step  530 , the computing platform may transmit, via the communication interface and to the external response server, the one or more commands directing an external response server to execute the automated response. 
     One or more aspects of the disclosure may be embodied in computer-usable data or computer-executable instructions, such as in one or more program modules, executed by one or more computers or other devices to perform the operations described herein. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform particular tasks or implement particular abstract data types when executed by one or more processors in a computer or other data processing device. The computer-executable instructions may be stored as computer-readable instructions on a computer-readable medium such as a hard disk, optical disk, removable storage media, solid-state memory, RAM, and the like. The functionality of the program modules may be combined or distributed as desired in various embodiments. In addition, the functionality may be embodied in whole or in part in firmware or hardware equivalents, such as integrated circuits, application-specific integrated circuits (ASICs), field programmable gate arrays (FPGA), and the like. Particular data structures may be used to more effectively implement one or more aspects of the disclosure, and such data structures are contemplated to be within the scope of computer executable instructions and computer-usable data described herein. 
     Various aspects described herein may be embodied as a method, an apparatus, or as one or more computer-readable media storing computer-executable instructions. Accordingly, those aspects may take the form of an entirely hardware embodiment, an entirely software embodiment, an entirely firmware embodiment, or an embodiment combining software, hardware, and firmware aspects in any combination. In addition, various signals representing data or events as described herein may be transferred between a source and a destination in the form of light or electromagnetic waves traveling through signal-conducting media such as metal wires, optical fibers, or wireless transmission media (e.g., air or space). In general, the one or more computer-readable media may be and/or include one or more non-transitory computer-readable media. 
     As described herein, the various methods and acts may be operative across one or more computing servers and one or more networks. The functionality may be distributed in any manner, or may be located in a single computing device (e.g., a server, a client computer, and the like). For example, in alternative embodiments, one or more of the computing platforms discussed above may be combined into a single computing platform, and the various functions of each computing platform may be performed by the single computing platform. In such arrangements, any and/or all of the above-discussed communications between computing platforms may correspond to data being accessed, moved, modified, updated, and/or otherwise used by the single computing platform. Additionally, or alternatively, one or more of the computing platforms discussed above may be implemented in one or more virtual machines that are provided by one or more physical computing devices. In such arrangements, the various functions of each computing platform may be performed by the one or more virtual machines, and any and/or all of the above-discussed communications between computing platforms may correspond to data being accessed, moved, modified, updated, and/or otherwise used by the one or more virtual machines. 
     Aspects of the disclosure have been described in terms of illustrative embodiments thereof. Numerous other embodiments, modifications, and variations within the scope and spirit of the appended claims will occur to persons of ordinary skill in the art from a review of this disclosure. For example, one or more of the steps depicted in the illustrative figures may be performed in other than the recited order, and one or more depicted steps may be optional in accordance with aspects of the disclosure.