Patent Publication Number: US-2021192145-A1

Title: Processing transactional feedback

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 16/461,125 by Ranatunga et al., entitled “Processing Transactional Feedback.” filed May 15, 2019; which is a U.S. National Stage Filing under 35 U.S.C. § 371 from International Patent Application No. PCT/CN2018/087485 by Ranatunga et al., entitled “Processing Transactional Feedback,” filed May 18, 2018; each of which is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     Embodiments of the present disclosure relate to processing transactional feedback, and more particularly, to processing transactional feedback for efficient user access, automated trend identification, and user notification. 
     BACKGROUND 
     Conventionally, as users of a networked system provide feedback for a transaction, there is limited organization with feedback typically being arranged linearly. In other examples, the feedback is arranged or filtered according to the feedback being positive, negative, or neutral. Although the user may arrange the feedback in different ways, additional conclusions regarding the state of the transactional system is usually done by the user (e.g., the recipient of the feedback). 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various ones of the appended drawings merely illustrate example embodiments of the present disclosure and cannot be considered as limiting its scope. 
         FIG. 1  is a block diagram illustrating a system, according to some example embodiments. 
         FIG. 2  is a block diagram depicting a feedback system, according to one example embodiment. 
         FIG. 3  is a block diagram depicting a feedback system, according to another example embodiment. 
         FIG. 4  is an illustration depicting a user interface generated by a feedback system, according to one example embodiment. 
         FIG. 5  is a data flow diagram illustrating a flow of data, according to one example embodiment. 
         FIG. 6  is a flow chart diagram illustrating a method for processing feedback, according to one example embodiment. 
         FIG. 7  is a flow chart diagram illustrating a method for processing feedback, according to another example embodiment. 
         FIG. 8  is another flow chart diagram illustrating a method for processing feedback, according to one example embodiment. 
         FIG. 9  is a block diagram illustrating an example of a software architecture that may be installed on a machine, according to some example embodiments. 
         FIG. 10  illustrates a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to an example embodiment. 
     
    
    
     The headings provided herein are merely for convenience and do not necessarily affect the scope or meaning of the terms used. 
     DETAILED DESCRIPTION 
     The description that follows includes systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative embodiments of the disclosure. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide an understanding of various embodiments of the inventive subject matter. It will be evident, however, to those skilled in the art, that embodiments of the inventive subject matter may be practiced without these specific details. In general, well-known instruction instances, protocols, structures, and techniques are not necessarily shown in detail. 
     In certain embodiments, a system, as described herein, is specially configured to receive a plurality of comments at a particular phase of a transaction with a member of an online marketplace. For example, a particular phase may be a purchasing phase of a transaction. 
     The system then classifies the comments into a sentiment classification according to term included in the respective comments. Then, the system generates a sub-category for the comment by applying a natural language processing module. The generated sub-category is, in certain embodiments, not selected from a set of predetermined sub-categories but is, rather, generated according to the language included in the respective comment. 
     In another example embodiment, the system is configured to present a user interface allowing a user to select categories or sub-categories and filters the messages according to the selected categories (including newly generated sub-categories). 
     In one particular example embodiment, the system notifies a user in response to a change in a trend for a certain category. For example, in response to an increase in complaints regarding the selection of an item to purchase, the system may determine an increased likelihood of an error or fault in the selection mechanisms and notify the user. This particular benefit includes the system identifying a likely error and notifying the user without the user reviewing the comments. Other technical benefits are described herein. 
     With reference to  FIG. 1 , an example embodiment of a high-level client-server-based network architecture  100  is shown. A network system  102  provides server-side functionality via a network  104  (e.g., the Internet or wide area network (WAN)) to one or more client devices  110 .  FIG. 1  illustrates, for example, a web client  112  (e.g., web a browser), client application(s)  114 , executing on the client device  110 . 
     The client device  110  may comprise, but is not limited to, a mobile phone, desktop computer, laptop, personal digital assistant (PDA), smart phone, tablet, ultra-book, netbook, laptop, multi-processor system, microprocessor-based or programmable consumer electronics, game console, set-top box, or any other communication device that a user may utilize to access the network system  102 . In some embodiments, the client device  110  may comprise a display module (not shown) to display information (e.g., in the form of user interfaces). In further embodiments, the client device  110  may comprise one or more of a touch screen, accelerometer, gyroscope, camera, microphone, global positioning system (GPS) device, and so forth. The client device  110  may be a device of a user that is used to perform a transaction involving digital items within the network system  102 . In one embodiment, the network system  102  is a network-based marketplace that responds to requests for product listings, publishes publications comprising item listings of products available on the network-based marketplace, and manages payments for these marketplace transactions. 
     One or more users  106  may be a person, a machine, or other means of interacting with the client device  110 . In embodiments, the user  106  is not part of the network architecture  100 , but may interact with the network architecture  100  via the client device  110  or another means. For example, one or more portions of the network  104  may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a WAN, a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, a wireless network, a WiFi network, a WiMax network, another type of network, or a combination of two or more such networks. 
     Each client device  110  may include one or more applications (also referred to as “apps”) such as, but not limited to, a web browser, messaging application, electronic mail (email) application, an e-commerce site application (also referred to as a marketplace application), and the like. In some embodiments, the client device  110  may include an application that is configured to locally provide the user interface and at least some of the functionalities with the application configured to communicate with the network system  102 , on an as needed basis, for data and/or processing capabilities not locally available (e.g., access to a database of items available for sale, to authenticate a user  106 , to verify a method of payment, etc.). Conversely, in other embodiments, the client device  110  may use its web browser to access a site (or a variant thereof) hosted on the network system  102 . 
     In example embodiments, the user  106  is not part of the network architecture  100 , but may interact with the network architecture  100  via the client device  110  or other means. For instance, the user  106  provides input (e.g., touch screen input or alphanumeric input) to the client device  110  and the input is then communicated to the network system  102  via the network  104 . In this instance, the network system  102 , in response to receiving the input from the user  106 , communicates information to the client device  110  via the network  104  to be presented to the user  106 . In this way, the user  106  can interact with the network system  102  using the client device  110 . 
     An application program interface (API) server  120  and a web server  122  are coupled to, and provide programmatic and web interfaces respectively to, one or more application server(s)  140 . The application server(s)  140  may host one or more publication systems  142  which may comprise one or more modules or applications and each of which may be embodied as hardware, software, firmware, or any combination thereof. The application server(s)  140  are, in turn, shown to be coupled to one or more database servers  124  that facilitate access to one or more information storage repositories or database(s)  126 . In an example embodiment, the database(s)  126  are storage devices that store information to be posted (e.g., publications) to the publication system(s)  142 . The database(s)  126  may also store digital item information in accordance with example embodiments. 
     A third party application  132 , executing on third party server(s)  130 , is shown as having programmatic access to the network system  102  via the programmatic interface provided by the API server  120 . For example, the third party application  132 , utilizing information retrieved from the network system  102 , supports one or more features or functions on a website hosted by the third party. 
     The publication system(s)  142  may provide a number of publication functions and services to users  106  that access the network system  102 . While the publication system  142  is shown in  FIG. 1  to both form part of the network system  102 , it will be appreciated that, in alternative embodiments, the publication system  142  may form part of a service that is separate and distinct from the network system  102 . 
     Further, while the client-server-based network architecture  100  shown in  FIG. 1  employs a client-server architecture, the present inventive subject matter is, of course, not limited to such an architecture, and could equally well find application in a distributed, or peer-to-peer, architecture system, for example. The publication system  142 , and feedback system  150 , could also be implemented as standalone software programs, which do not necessarily have networking capabilities. 
     The web client  112  may access the publication system  142  via the web interface supported by the web server  122 . The programmatic client accesses the various services and functions provided by the publication system  142  via the programmatic interface provided by the API server  120 . The programmatic client may, for example, be a publisher application to enable users  106  to author and manage publications on the network system  102  in an off-line manner, and to perform batch-mode communications between the programmatic client and the network system  102 . 
     In certain example embodiments, the 3D integration system  146  generates the 3D model to be displayed using the web client  112 . In one example, the 3D integration system  146  generates the model, stores the model at a remote server, and returns a universal resource locator (URL) to access the model using the web client  112 . 
     Additionally, a third party application(s)  132 , executing on a third party server(s)  130 , is shown as having programmatic access to the network system  102  via the programmatic interface provided by the API server  120 . For example, the third party application  132 , utilizing information retrieved from the network system  102 , may support one or more features or functions on a website hosted by the third party. 
     In one example embodiment, the feedback system  150  is configured to receive comments for a particular phase of a transaction with a member of the network system  102 , determine a sentiment and a category of comments, where the category is selected from a set of predefined categories, generate a sub-category by applying a natural language processing module, and generate a display allowing a user  106  to select a category, or sub-category, and filter the comments according to the selection by the user  106 . In this way, the feedback system  150  provides quick access to feedback comments that are related to the selected category. 
       FIG. 2  is a block diagram depicting a feedback system  150 , according to one example embodiment. In this example embodiment, the feedback system  150  includes a collection module  220 , a sentiment module  230 , a display module  250 , and a category module  240 . 
     In one example embodiment, the collection module  220  is configured to receive a plurality of comments at a particular phase of a transaction with a member of an online marketplace. In certain embodiment, the collection module  220  retrieves comments by querying a database  126  of comments. For example, the network system  102  may receive comments from an application server  140  that is configured to request comments from users  106  using the network system  102  and store the comments in the database  126 . 
     As one skilled in the art may appreciate, the collection module  220  may also receive comments in many other ways. For example, the collection module  220  may load comments from storage, receive comments over an electronic network interface, or in any other way. 
     In one example embodiment, the particular phase of a transaction includes a particular step in completing a transaction between two users  106  of the networked system  102 . Examples include, but are not limited to: item selection, display, identity verification, item options selections, shopping cart management, checkout, payment, delivery options selections, returns management, or other, or the like. 
     In one example embodiment, the sentiment module  230  is configured to classify one of the comments according to text included in the comment. For example, the comment text may include, “I hate it when . . . ,” “this site stinks,” or the like and, in response, the sentiment module  230  determines that the sentiment for the comment is negative. In other examples, the sentiment module  230  identifies a sentiment according to specific terms included in the comment. For example, text of the comment includes terms like “awesome,” “wonderful,” “perfect,” “love,” or other teams that indicated pleasure, acceptance, satisfaction, or the like, and the sentiment module  230  determines that the sentiment of the comment is positive. In another example, the sentiment module  230  classifies a comment as “neutral” in response to none of the terms in the comment matching a term associated with either the “positive” classification or the “negative” classification. In one specific example embodiment, the sentiments are selected from a set of predefined sentiments, such as, but not limited to, “positive,” “negative,” and “neutral. 
     In another example embodiment, the sentiment module  230  applies a machine learning system ( FIG. 3 :  270 ) that outputs a sentiment classification in response to receiving the comment, or the text of the comment. Further example embodiments of the machine learning system  270  are described in relation to  FIG. 3 . In one non-limiting example, the sentiment module  230  employs a trained machine learning system to output a sentiment classification in response to receiving the text of the comment. 
     For example, an administrator of the feedback system  150  may train a machine learning system  270  on a large database of comments that have been manually classified into a sentiment classification by humans. The machine learning system  270  trains on this data and is then configured to output a sentiment classification in response to receiving text of the message or a set of terms that are included in the message. 
     In another example embodiment, the category module  240  is configured to determine a category for the comment selected from a set of predefined categories. In certain embodiments, such categories include, but are not limited to: a bug, operating correctly, a compliment, feature request, a complaint, a related comment, unclassified, a recommendation, or other. 
     In one example embodiment, the category module  240  determines a category for the comment according to specific terms that are included in the comment. In one example, the comment includes “I hate it when . . . ” In response, the category module  240  classifies the comment as a complaint. In another example, the comment includes “broken,” and the category module  240  determines that the comment is a declaration of a bug. In another example, a comment includes “I love it,” and the category module  240  determines that the comment is a compliment. In another example, the comment includes, “need to add” and the category module  240  determines that the comment is a feature request. 
     In one example embodiment, the category module  240  tokenizes each term in the comment and looks up the term in a database of terms and/or phrases. For example, if the comment includes the phrase “can&#39;t pay” the category module  240  determines that the comment is a complaint because the phrase “can&#39;t pay” in found in a database of phrases that have been associated with a complaint. 
     In one example embodiment, the category module  240  determines a category for a comment in response a number of term in the message matching a highest number of terms in a database of terms associated with a particular category. 
     For example, where four terms are associated with a “bug” category and three terms are associated with a “complaint” category, the category module  240  determines that the comment is a “bug” because more terms matched the category “bug” than other categories. 
     In another example embodiment, the category module  240  loads a set of associations from a remote database of association between specific terms and a category. In this example embodiment, the category module  240  loads the associations from the database  126  and determines the category for a comment according to one or more of the associations. Furthermore, in this example embodiment, an administrator of the feedback system  150  may add associations to the database  126  and the category module  240  automatically determines a category for a comment according to the newly added association. 
     In another example embodiment, the category module  240  applies a trained machine learning system ( FIG. 2 :  270 ), that outputs a category in response to receiving the comment as input. For example, an administrator of the feedback system  150  may train a machine learning system  270  on terms found in comments and a selected category. In this way, the machine learning system  270  is configured to output a category in response to receiving a set of terms found in a comment. Further example embodiments that include the machine learning system  270  are described in relation to  FIG. 3 . 
     In one example embodiment, the category module  240  applies a natural language processing module to the comment to generate a sub-category for the comment. In contrast with the category, which is selected from a set of predetermined categories, the sub-category is generated without regard to a set of predefined sub-categories. In one example, the category module  240  generates a sub-category of “technical problem with imagery.” 
     In this example embodiment, the category is “a bug” and the generated sub-category is “technical problem with imagery.” Accordingly, the sub-category of “technical problem with imagery,” is associated with the category of “bug” because at least one comment was classified as “a bug” and further sub-classified as “technical problem with imagery.” Therefore, as additional sub-categories are generated, they are associated with the respective categories based on comments that are classified and sub-classified accordingly. 
     In one example embodiment, the display module  250  is configured to generate a display of the categories associated with the particular phase of the transaction as previously described. In this example embodiment, the generated display includes each of the categories as well as a set of graphical elements representing each of the generated sub-categories. A specific example of such a display is depicted in  FIG. 4 . 
     In other example embodiments, the sub-categories that are associated with the categories are connected in the generated display. In one example, the sub-categories are placed directly next to the associated category. In another example, graphical elements representing the sub-categories are connected to graphical elements representing the categories via a graphical line, or other connecting graphical element. 
     Accordingly, a user  106  of the networked system  102  desiring to review comments associated with a particular phase of a transaction may select the phase and, in response, the display module  250  generates the display showing the categories and associated sub-categories that were generated based on the comments. 
     In one example embodiment, the display module  250  receives a selection of a category and, in response, filters comments to display those comments that are associated with the category selection. For example, the comments that were assigned the selected category are displayed while other comments that were not assigned the selected category are not displayed. 
     In one example embodiment, the display module  250  increases a graphical size of the selected category in response to the selection. This may further clarify to the user  106  which category was selected and serve as a reminder to the user  106  how the displayed comments are filtered. 
     In another example embodiment, the display module  250  receives a selection of a sub-category and, in response, highlights the selected sub-category, displays a title describing the selected sub-category, and filters the displayed comments that were associated with the sub-category. 
       FIG. 3  is a block diagram  300  depicting a feedback system, according to another example embodiment. This example embodiment includes a collection module  220 , a sentiment module  230 , a display module  250 , a category module  240 , a notification module  260 , a machine learning system  270 , and a weight module  280 . The collection module  220 , the sentiment module  230 , the display module  250 , and the category module  240  may or may not be substantially similar to those depicted in  FIG. 2 . 
     In one example embodiment, the notification module  260  is configured to alert the user  106  in response to a change in a trend in one of the categories. In one example embodiment, a trend for a category includes less than five comments per day. For example, a particular user  106  may have received less than five comments regarding payment per day each day over the past month that are related to complaints regarding payment. Therefore, a trend for complaints regarding payment may be less than five comments per day. 
     In one example embodiment, in response to receiving more than 10 complaints regarding payment in a given day, or response to more than 10 complaints regarding payment for a consecutive threshold number of days, the notification module  260  determines that there is a significant change in a trend for comments associated with a particular category. Of course, other changes in trend may be determined by the notification module  260 , as one skilled in the art may appreciate, and this disclosure is meant to include all such changes in trend. 
     Other examples of trends for a given category include related comments per month, comments per hour, or the like. Changes in a trend include, but are not limited to, comments exceeding 200% of a trend, 300%, or any other change in a trend. 
     In one example embodiment, alerting the user  106  includes transmitting an email to the user  106 , transmitting an SMS text message to the user  106 , displaying a notification to the user  106  via an electronic graphical user interface, or any other electronic or digital communication, as one skilled in the art may appreciate. 
     In another example embodiment, the machine learning system  270  is configured to output a category for a comment in response to receiving text of the comment. In one example, the machine learning system  270  is trained by an administrator of the system by providing a set of comments with categories. In response, the machine learning system  270  trains on the set of comments and learns to output a category based on text of a comment. 
     In one example embodiment, the weight module  280  applies a weight for each comment according to a user feedback score for the user  106  providing the comment. 
     As one skilled in the art may appreciate, there are many different metrics to determine a quality of feedback by a user  106 . Some examples include length of the feedback, sophistication of terms used in the comment, grammar quality of the comment, whether the user  106  purchased the product commenting on, whether the user  106  engaged in an actual transaction, an amount of business, or other aspects of the user  106  providing the comment. In one example embodiment, the display module  250  filters comments to those comments from users  106  who exceed a certain threshold user weight. 
     In another example embodiment, the feedback system  150  selects a natural language processing module according to the determined category. For example, an administrator of the feedback system  150  may customize a natural language processing module for each category and the feedback system  150  may select a natural language processing module to use to generate the sub-category according to the selected category. 
       FIG. 4  is an illustration depicting a user interface  400  generated by a feedback system  150 , according to one example embodiment. In one example embodiment, the user interface  400  includes a legend  420 , a sundial display  421  for categories and sub-categories, and a display  430  for comments that are associated with the categories. 
     In one example embodiment, the legend  420  provides a mapping between certain categories and a color or pattern. In this example embodiment, the sundial  421  includes an outer ring  422  and an inner ring  423 . The inner ring  423  includes a graphical section for each of the predetermined categories. The outer ring  422  includes a graphical section for each of the generated categories for the respective category in the outer ring  422 . 
     In one example embodiment, the display module  250  adjusts a size of a graphical section  410  of an inner ring  423  according to a percentage of comments that were mapped to a category represented by the section. For example, in response to 50% of the comments being mapped to the category represented by the graphical section  410 , the display module  250  adjusts the size of the graphical section  410  to encompass 50% of the inner ring  423 . Accordingly, the display module  250  displays an inner ring  423  that accurately represents the respective percentages of comments mapped to the respective categories. 
     In another example embodiment, as with the inner ring  423 , the display module  250  also adjusts the sizes of the graphical segments  412  according to a percentage of comments that were mapped to the particular graphical segment  412 . Also, in this example embodiment, the display module  250  places the graphical segments  412 A,  412 B in the outer ring  422  to be in contact with the graphical section  410  in the inner ring  423  in response to the graphical segments  412  representing sub-categories that are associated with the category represented by the graphical section  410 . 
     In one example embodiment, the display module  250  filters comments in a portion of the display  430  that displays filtered comments. For example, in response to a user  106  selecting the graphical element  423 , the display module  250  filter the comments to display only those comments that were mapped to the category represented by the selected graphical segment  423 . Furthermore, in response to the user  106  selecting the graphical segment  412 A, the display module  250  filters the comments to display only those comments that were mapped to the sub-category represented by the selected graphical segment  412 A. 
     In another example embodiment, the display module  250  generates a row of categories that were selected from a set of comments with graphical elements for each of the categories being sized according to a percentage of comments that were associated with the respective categories. Attached to the row of categories, in this example embodiment, is another row of the generated sub-categories wherein each graphical element representing a sub-category touches the graphical element for the category associated with the sub-category. For example, “touches” may at least include the graphical element for the sub-category being rendered to displayed using at least one pixel that is neighboring at least one pixel of the graphical element for the associated category. 
       FIG. 5  is a data flow diagram illustrating a flow of data, according to one example embodiment. In this data flow diagram  500  the collection module  220  receives user feedback  510 . 
     First, the feedback system  150  performs a semi-supervised category detection  512  to identify a theme or topic for the feedback, such as by using word embedding to extract keywords from the feedback. This category detection is performed in a hierarchal manner, allowing selection of a category in a deep hierarchy of categories. The feedback system  150  then infers a category (or topic) at  514 , such as by using a Naive Bayes classifier. The feedback (e.g., individual comments or submissions) can then be classified into one or more of the inferred categories. Although word embedding and Naïve Bayes is discussed, other data mining, machine learning, or deep learning techniques can be used. 
     In one example embodiment, either concurrently or at a different point in time, the feedback system  150  performs sentiment analysis  520  on the feedback to identify one or more sentiment classifications, such as but not limited to positive, negative, or neutral. The feedback system  150  then classifies  522  the feedback into one or more of the inferred sentiment classifications based on comments included in the feedback. 
     The feedback system  150  can include hardware or software components to perform natural language analysis or processing of the customer feedback to generate an understanding  530  of the feedback, such as to extract or generate one or more sub-categories  532 . Such software or hardware modules can include implementations of a selection of artificial intelligence or machine learning techniques for performing natural language processing tasks, such as syntax analysis including segmentation and part-of-speech tagging, semantics analysis including intent detection, discourse analysis including automatic summarization and entity/attribute extraction, and speech recognition. The one or more sub-categories  532  can include one or more attributes of the feedback, such as a summary of the key or most important information in the feedback. 
     In another example embodiment, the feedback system  150  then combines the determined category, sentiment, and generated sub-category in a ranking and visualization step  524 , such as in a visual display. In this step  524 , the display module  250  may rank or display comments (e.g., based on category, sentiment, and generated sub-category of the comments) in a variety of different ways as described herein. Furthermore, the notification module  260  may perform anomaly detection and/or alerting  540  in response to identifying a trend in user feedback or comments. 
       FIG. 6  is a flow chart diagram illustrating a method  600  for processing feedback, according to one example embodiment. Operations in the method  600  may be performed by the feedback system  150 , using modules described above with respect to  FIG. 2 . 
     In one example embodiment, the method  600  begins, and at operation  612 , the collection module  220  receives a plurality of comments at a particular phase of a transaction with a member of an online marketplace. For example, the collection module  220  may receive the comments via a web page transmitted via an application server  140  operating as part of the network system  102 . 
     The method  600  continues at operation  614  and the sentiment module  230  classifies one or more of the plurality of comments into a sentiment classification. For example, the sentiment module  230  may apply a trained machine learning system  270  to the comment and accept the output (e.g., an indicated sentiment classification) of the trained machine learning system  270 . 
     The method  600  continues at operation  616  and the category module  240  determines a category for each of the one or more comments in the plurality of the comments, the category selected from a set of predefined categories. The method  600  continues and at operation  618  the category module  240  applies a natural language processing module to the comment to generate a sub-category associated with the selected category. In certain embodiments, there is no limit on the number of generated sub-categories as the natural language processing module may generate any number of sub-categories. 
     The method  600  continues at operation  619  and the category module  240  associates the generated sub-categories with their respective categories for the one or more comments. The method  600  continues at operation  620  and the display module  250  generates a display of the determined categories for the particular transaction and the associated generated sub-categories, each sub-category being connected to their respective categories. For example, as depicted in  FIG. 5 , the display module  250  may generate a ring diagram that displays the selected categories from the comments in an inner ring  423  and displays the generated sub-categories on an outer ring  422  connected to the selected category for the comment on the inner ring  423 . 
       FIG. 7  is a flow chart diagram illustrating a method for processing feedback, according to another example embodiment. Operations in the method  700  may be performed by the feedback system  150 , using modules described above with respect to  FIG. 3 . 
     In one example embodiment, the method  700  begins and at operation  712 , the feedback system  150  trains a machine learning system  270  to output a category in response to receiving text of a comment. In one example, the feedback system  150  loads a set of training data from a data storage device and trains a machine learning system  270  on the training data, as one skilled in the art may appreciate. 
     The method  700  continues and at operation  714 , the collection module  220  retrieves comments from a remote database of comments for a particular phase of a transaction with a member of an online marketplace. For example, the collection module  220  may construct and submit an SQL query to the database  126  and receive comments that satisfy the conditions of the SQL query. 
     The method  700  continues at operation  716  and the sentiment module  230  classifies the comments into a sentiment classification. For example, the sentiment module  230  may classify the comment according to one or more selections at an electronic user interface (e.g., in an example where a user  106  specifically indicates a sentiment using the electronic user interface). 
     The method  700  continues at operation  718  and the category module  240  determines a category for each of the one or more comments in the plurality of the comments, the category selected from a set of predefined categories. The method  700  continues and at operation  720  and category module  240  applies a natural language processing module to the comment to generate a sub-category associated with the selected category. In certain embodiments, there is no limit on the number of generated sub-categories as the natural language processing module may generate any number of sub-categories, as one skilled in the art may appreciate. 
     The method  700  continues at operation  722  and the display module  250  generates a display of the determined categories for the particular transaction and the associated generated sub-categories, each sub-category being connected to their respective categories. For example, the display module  250  may generate a top row of categories and a second row of sub-categories where each of the sub-categories are connected to their respective categories. In this example embodiment, the display module  250  adjusts the size of graphical elements for the categories in order to accommodate the sub-categories. 
     The method  700  continues at operation  724  and the notification module  260  alerts the user  106  in response to a change in a trend in one of the categories. In one example embodiment, alerting the user  106  includes transmitting an email to the user  106 , transmitting an SMS text message to the user  106 , displaying a notification to the user  106  via an electronic graphical user interface, or any other electronic or digital communication, as one skilled in the art may appreciate. 
       FIG. 8  is another flow chart diagram illustrating a method  800  for processing feedback, according to one example embodiment. Operations in the method  800  may be performed by the feedback system  150 , using modules described above with respect to  FIGS. 2-5 . In one example embodiment, the feedback system  150  begins the method  800  at operation  812  by receiving a plurality of comments at a particular phase of a transaction with a member of an online marketplace, as described herein. At operation  814 , the feedback system  150  can determine a sentiment of one or more the received comments. At operation  816 , the feedback system can determine a classification for one or more of the received comments based, for example, categories determined or identified by the feedback system  150 . At operation  818 , the feedback system can generate one or more sub-classifications or sub-categories. At operation  820 , the feedback system can generate a graphical display illustrating the determined classifications and connections between the classifications and associated sub-classifications. At operation  822 , the feedback system can receive a selection of category, a sub-category, of a feedback comment though the display. At operation  824 , the feedback system can filter the feedback comments according the received selection. In one example, when a category is selected, the feedback system can modify the graphical display to show filtered feedback comments having a classification matching the selected category. 
     Machine and Software Architecture 
     The modules, methods, applications, and so forth described in conjunction with  FIGS. 1-8  are implemented, in some embodiments, in the context of a machine and associated software architecture. The sections below describe representative software architecture(s) and machine (e.g., hardware) architecture that are suitable for use with the disclosed embodiments. 
     Software architectures are used in conjunction with hardware architectures to create devices and machines tailored to particular purposes. For example, a particular hardware architecture coupled with a particular software architecture will create a mobile device, such as a mobile phone, tablet device, or so forth. A slightly different hardware and software architecture may yield a smart device for use in the “internet of things.” While yet another combination produces a server computer for use within a cloud computing architecture. Not all combinations of such software and hardware architectures are presented here, as those of skill in the art can readily understand how to implement the inventive subject matter in different contexts from the disclosure contained herein. 
     Modules, Components, and Logic 
     Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied on a machine-readable medium) or hardware modules. A “hardware module” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein. 
     In some embodiments, a hardware module may be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware module may include dedicated circuitry or logic that is permanently configured to perform certain operations. For example, a hardware module may be a special-purpose processor, such as a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC). A hardware module may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware module may include software executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware modules become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. 
     Accordingly, the phrase “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein, “hardware-implemented module” refers to a hardware module. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where a hardware module comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware modules) at different times. Software accordingly configures a particular processor or processors, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time. 
     Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented module” refers to a hardware module implemented using one or more processors. 
     Similarly, the methods described herein may be at least partially processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an API). 
     The performance of certain of the operations may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the processors or processor-implemented modules may be distributed across a number of geographic locations. 
       FIG. 9  is a block diagram illustrating an example of a software architecture  900  that may be installed on a machine, according to some example embodiments. The software architecture  900  may be used in conjunction with various hardware architectures herein described.  FIG. 9  is merely a non-limiting example of a software architecture and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein. The software architecture  900  may be executing on hardware such as machine  1000  of  FIG. 16  that includes, among other things, processors  1010 , memory  1030 , and I/O components  1050 . A representative hardware layer  904  is illustrated and can represent, for example, the machine  1000  of  FIG. 10 . The representative hardware layer  904  comprises one or more processing units  906  having associated executable instructions  908 . Executable instructions  908  represent the executable instructions of the software architecture  902 , including implementation of the methods, modules, and so forth of  FIGS. 1-8 . Hardware layer  904  also includes memory and/or storage modules  910 , which also have executable instructions  908 . Hardware layer  904  may also comprise other hardware as indicated by  912 , which represents any other hardware of the hardware layer  904 , such as the other hardware illustrated as part of machine  1000 . 
     In the example architecture of  FIG. 9 , the software architecture  902  may be conceptualized as a stack of layers where each layer provides particular functionality. For example, the software architecture  902  may include layers such as an operating system  914 , libraries  916 , frameworks/middleware  918 , applications  920 , and presentation layer  944 . Operationally, the applications  920  and/or other components within the layers may invoke API calls  924  through the software stack and receive a response, returned values, and so forth illustrated as messages  926  in response to the API calls  924 . The layers illustrated are representative in nature and not all software architectures have all layers. For example, some mobile or special purpose operating systems may not provide a frameworks/middleware layer  918 , while others may provide such a layer. Other software architectures may include additional or different layers. 
     The operating system  914  may manage hardware resources and provide common services. The operating system  914  may include, for example, a kernel  928 , services  930 , and drivers  932 . The kernel  928  may act as an abstraction layer between the hardware and the other software layers. For example, the kernel  928  may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. The services  930  may provide other common services for the other software layers. The drivers  932  may be responsible for controlling or interfacing with the underlying hardware. For instance, the drivers  932  may include display drivers, camera drivers, Bluetooth® drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audio drivers, power management drivers, and so forth depending on the hardware configuration. 
     The libraries  916  may provide a common infrastructure that may be utilized by the applications  920  and/or other components and/or layers. In one specific embodiment, the various modules of the feedback system  150  are implemented as an application  920 . The libraries  916  typically provide functionality that allows other software modules to perform tasks in an easier fashion than to interface directly with the underlying operating system  914  functionality (e.g., kernel  928 , services  930  and/or drivers  932 ). The libraries  916  may include system libraries  934  (e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries  916  may include API libraries  936  such as media libraries (e.g., libraries to support presentation and manipulation of various media format such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., an OpenGL framework that may be used to render two-dimensional and/or three dimensional graphic content on a display), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g., WebKit that may provide web browsing functionality), and the like. The libraries  916  may also include a wide variety of other libraries  938  to provide many other APIs to the applications  920  and other software components/modules. 
     The frameworks  918  (also sometimes referred to as middleware) may provide a higher-level common infrastructure that may be utilized by the applications  920  and/or other software components/modules. For example, the frameworks  918  may provide various graphic user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks  918  may provide a broad spectrum of other APIs that may be utilized by the applications  920  and/or other software components/modules, some of which may be specific to a particular operating system or platform. In one example embodiment, at least a portion of the 3D integration system  146  is implemented as middleware. In one example, the 3D model module is implemented as middleware and is accessible by any application  920 . 
     The applications  920  include built-in applications  940  and/or third party applications  942 . Examples of representative built-in applications  940  may include, but are not limited to, a contacts application, a browser application, a book reader application, a location application, a media application, a messaging application, and a game application, or other, or the like. Third party applications  942  may include any of the built in applications as well as a broad assortment of other applications. In a specific example, the third party application  942  (e.g., an application developed using the Android™ or iOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as iOS™, Android™, Windows® Phone, or other mobile operating systems. In this example, the third party application  942  may invoke the API calls  924  provided by the mobile operating system such as operating system  914  to facilitate functionality described herein. 
     The applications  920  may utilize built in operating system functions (e.g., kernel  928 , services  930 , and/or drivers  932 ), libraries (e.g., system  934 , APIs  936 , and other libraries  938 ), frameworks/middleware  918  to create user interfaces to interact with users  106  of the system. Alternatively, or additionally, in some systems, interactions with a user  106  may occur through a presentation layer, such as presentation layer  944 . In these systems, the application/module “logic” can be separated from the aspects of the application/module that interact with a user  106 . 
     Some software architectures utilize virtual machines. In the example of  FIG. 9 , this is illustrated by virtual machine  948 . A virtual machine  948  creates a software environment where applications/modules can execute as if they were executing on a hardware machine (such as the machine  1000  of  FIG. 10 , for example). A virtual machine  948  is hosted by a host operating system (operating system  914  in  FIG. 9 ) and typically, although not always, has a virtual machine monitor  946 , which manages the operation of the virtual machine  948  as well as the interface with the host operating system (i.e., operating system  914 ). A software architecture executes within the virtual machine  948  such as an operating system  950 , libraries  952 , frameworks/middleware  954 , applications  956  and/or presentation layer  958 . These layers of software architecture  902  executing within the virtual machine  948  can be the same as corresponding layers previously described or may be different. 
     Example Machine Architecture and Machine-Readable Medium 
       FIG. 10  is a block diagram illustrating components of a machine  1000 , according to some example embodiments, able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically,  FIG. 10  shows a diagrammatic representation of the machine  1000  in the example form of a computer system, within which instructions  1016  (e.g., software, a program, an application  920 , an applet, an app, or other executable code) for causing the machine  1000  to perform any one or more of the methodologies discussed herein may be executed. 
     For example, the instructions  1016  may cause the machine  1000  to execute the flow diagrams of  FIGS. 6-8 . Additionally, or alternatively, the instructions  1016  may implement the modules depicted in  FIG. 2  or  FIG. 3 . Specifically, the instructions  1016  may implement the various functions of the collection module  220 , the sentiment module  230 , the display module  250 , the category module  240 , the notification module  260 , the machine learning system  270 , or the weight module  280 . 
     The instructions  1016  transform the general, non-programmed machine into a particular machine programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the machine  1000  operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  1000  may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine  1000  may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions  1016 , sequentially or otherwise, that specify actions to be taken by machine  1000 . Further, while only a single machine  1000  is illustrated, the term “machine” shall also be taken to include a collection of machines  1000  that individually or jointly execute the instructions  1016  to perform any one or more of the methodologies discussed herein. 
     The machine  1000  may include processors  1010 , memory  1030 , and I/O components  1050 , which may be configured to communicate with each other such as via a bus  1002 . In an example embodiment, the processors  1010  (e.g., a central processing unit (CPU), a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a graphics processing unit (GPU), a digital signal processor (DSP), an ASIC, a radio-frequency integrated circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, processor  1012  and processor  1014  that may execute instructions  1016 . The term “processor” is intended to include multi-core processors  1010  that may comprise two or more independent processors  1012 ,  1014  (sometimes referred to as “cores”) that may execute instructions  1016  contemporaneously. Although  FIG. 10  shows multiple processors  1010 , the machine  1000  may include a single processor  1012  with a single core, a single processor  1012  with multiple cores (e.g., a multi-core processor), multiple processors  1012 , 1014  with a single core, multiple processors  1012 , 1014  with multiples cores, or any combination thereof. 
     The memory/storage  1030  may include a memory  1032 , such as a main memory, or other memory storage, and a storage unit  1036 , both accessible to the processors  1010  such as via the bus  1002 . The storage unit  1036  and memory  1032  store the instructions  1016  embodying any one or more of the methodologies or functions described herein. The instructions  1016  may also reside, completely or partially, within the memory  1032 , within the storage unit  1036 , within at least one of the processors  1010  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  1000 . Accordingly, the memory  1032 , the storage unit  1036 , and the memory of processors  1010  are examples of machine-readable media. 
     As used herein, “machine-readable medium” means a device able to store instructions  1016  and data temporarily or permanently and may include, but is not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., erasable programmable read-only memory (EEPROM)) and/or any suitable combination thereof. The term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions  1016 . The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., instructions  1016 ) for execution by a machine (e.g., machine  1000 ), such that the instructions  1016 , when executed by one or more processors of the machine  1000  (e.g., processors  1010 ), cause the machine  1000  to perform any one or more of the methodologies described herein. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” excludes signals per se. 
     The I/O components  1050  may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components  1050  that are included in a particular machine  1000  will depend on the type of machine  1000 . For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components  1050  may include many other components that are not shown in  FIG. 10 . The I/O components  1050  are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limiting. In various example embodiments, the I/O components  1050  may include output components  1052  and input components  1054 . The output components  1052  may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components  1054  may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like. 
     In further example embodiments, the I/O components  1050  may include biometric components  1056 , motion components  1058 , environmental components  1060 , or position components  1062  among a wide array of other components. For example, the biometric components  1056  may include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram based identification), and the like. The motion components  1058  may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components  1060  may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometer that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components  1062  may include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like. 
     Communication may be implemented using a wide variety of technologies. The I/O components  1050  may include communication components  1064  operable to couple the machine  1000  to a network  1080  or devices  1070  via coupling  1082  and coupling  1072 , respectively. For example, the communication components  1064  may include a network interface component or other suitable device to interface with the network  1080 . In further examples, communication components  1064  may include wired communication components, wireless communication components, cellular communication components, near field communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices  1070  may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB). 
     Moreover, the communication components  1064  may detect identifiers or include components operable to detect identifiers. For example, the communication components  1064  may include radio frequency identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components  1064 , such as, location via Internet Protocol (IP) geo-location, location via Wi-Fi® signal triangulation, location via detecting a NFC beacon signal that may indicate a particular location, and so forth. 
     Transmission Medium 
     In various example embodiments, one or more portions of the network  1080  may be an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, the Internet, a portion of the Internet, a portion of the PSTN, a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network  1080  or a portion of the network  1080  may include a wireless or cellular network and the coupling  1082  may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other type of cellular or wireless coupling. In this example, the coupling  1082  may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard setting organizations, other long range protocols, or other data transfer technology. 
     The instructions  1016  may be transmitted or received over the network  1080  using a transmission medium via a network interface device (e.g., a network interface component included in the communication components  1064 ) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions  1016  may be transmitted or received using a transmission medium via the coupling  1072  (e.g., a peer-to-peer coupling) to devices  1070 . The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions  1016  for execution by the machine  1000 , and includes digital or analog communications signals or other intangible medium to facilitate communication of such software. 
     Language 
     Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein. 
     Although an overview of the inventive subject matter has been described with reference to specific example embodiments, various modifications and changes may be made to these embodiments without departing from the broader scope of embodiments of the present disclosure. Such embodiments of the inventive subject matter may be referred to herein, individually or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single disclosure or inventive concept if more than one is, in fact, disclosed. 
     The embodiments illustrated herein are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. 
     As used herein, the term “or” may be construed in either an inclusive or exclusive sense. Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, modules, engines, and data stores are somewhat arbitrary, and particular operations are illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present disclosure. In general, structures and functionality presented as separate resources in the example configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present disclosure as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.