Patent Publication Number: US-11663416-B2

Title: Signal analysis in a conversational scheduling assistant computing system

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation of and claims priority of U.S. patent application Ser. No. 16/511,724, filed Jul. 15, 2019, which is a continuation of and claims priority of U.S. patent application Ser. No. 15/714,077, filed Sep. 25, 2017, the contents of which are hereby incorporated by reference in their entirety. 
    
    
     BACKGROUND 
     Computing systems are currently in wide use. Some computing systems include such things as electronic mail (e-mail) computing systems, calendaring or scheduling computing systems, among a wide variety of others. 
     Also, some computing systems include digital agents, or personal digital assistants. These types of agents or assistants can include, for example, software agents or other bots that are computer programs that may provide an interface for human interaction, using natural language, and that may perform various tasks. They illustratively use natural language understanding logic to identify a linguistic meaning of the natural language inputs and to perform certain tasks for the user, automatically, based upon those natural language inputs. For instance, based on a natural language input, the agent or assistant can set reminders, answer questions by performing searches, interact with other services, among other things. 
     In performing these tasks, such agents or bots are often involved in conducting a conversation with a user in order to perform a service. For instance, some bots or agents may be used when a user is having a conversation with other users. By way of example, assume that a first user sends an electronic mail message (an e-mail message) to a group of other users asking whether they wish to see a movie or attend other entertainment on a particular date. In order to find activities that are available, it may be that one of the users or the group also sends an e-mail message (cc&#39;ing all other users in the group) to a software agent that is configured to identify entertainment that is taking place at different venues on different dates. The software agent then surfaces, for the group, a set of entertainment activities that are taking place on that date, and may also assist the group in obtaining tickets. By way of example, the software agent may send an e-mail to the entire group indicating the set of entertainment activities and asking whether the software agent should procure tickets. If directed to do so, the software agent may conduct a dialog with one or more of the users to identify the number of tickets, the amount to be spent on the tickets, among other things. The software agent may then interact with a ticketing site to obtain the tickets. 
     It can be difficult to improve such computing systems, because it can be difficult to know what features the various users are satisfied with, and what features they may be dissatisfied with, and why. Some current systems request that users take a survey to provide feedback. However, surveys are often perceived by users as being cumbersome and time consuming. Therefore, only a relatively small set of users may actually take the survey so that the feedback is quite limited. 
     The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. 
     SUMMARY 
     A software agent, that is used to assist in providing a service, receives communications from a set of users that are attempting to use the software agent. The communications include communications that are interacting with the software agent, and communications that are not interacting with the software agent. The software agent performs natural language processing on all communications to identify such things as user sentiment, user concerns or other items in the content of the messages, and also to identify actions taken by the users. The processing is performed in order to obtain a measure of user satisfaction with the software agent. One or more action signals are then generated based upon the identified user satisfaction with the software agent. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram of one example of a computing system architecture. 
         FIGS.  2 A and  2 B  (collectively referred to herein as  FIG.  2   ) show a flow diagram illustrating one example of the operation of the architecture illustrated in  FIG.  1    in analyzing different signals to identify user satisfaction and generating control signals based upon the user satisfaction identified. 
         FIG.  3    is a block diagram showing one example of the architecture illustrated in  FIG.  1   , deployed in a cloud computing architecture. 
         FIGS.  4 - 6    show examples of mobile devices that can be used in the architectures shown in the previous figures. 
         FIG.  7    is a block diagram showing one example of a computing environment that can be used in the architectures shown in the previous figures. 
     
    
    
     DETAILED DESCRIPTION 
       FIG.  1    is a block diagram of one example of a computing system architecture  100 . Architecture  100  illustratively includes computing system  102  that generates user interfaces  104  with user input mechanisms  106  for interaction by user  108 . User  108  illustratively interacts with user input mechanisms  106  in order to control and manipulate portions of computing system  102 . 
     In the example illustrated in  FIG.  1   , computing system  102  illustratively includes one or more processors or servers  110 , electronic mail (e-mail) system  112 , calendar system  114 , communication system  116 , user interface logic  118 , scheduling assistance system  120 , and it can include a wide variety of other items  122 , as well. Calendar system  114 , itself, illustratively includes scheduling logic  124 , calendar data  126  (which may be calendar data for user  108  and/or other users), and it can include a wide variety of other calendar functionality  128 . 
     Scheduling assistance system  120  is illustratively a computing system that assists user  108  in interacting with calendar system  114 , and the calendar systems of other users, in order to perform scheduling operations. In one example, user  108  illustratively interacts with scheduling assistance system  120  by sending messages to an inbox of scheduling assistance system  120  using e-mail system  112 . However, in other examples, user  108  can communicate with scheduling assistance system  120  using other types of communication, such as natural language speech input, other messaging systems, etc. 
     Thus, scheduling assistance system  120  illustratively includes natural language understanding system  130  (which is shown as part of scheduling assistance system  120 , but which can be separate therefrom, or a remote service that is accessed by system  120 ), scheduling assistance workflow processing system  132 , user satisfaction signal analyzer and control system  134 , and it can include other items  136 . Before describing the operation of architecture  100  in more detail, a brief overview of some of the items in architecture  100 , and their operation, will first be provided. 
     E-mail system  112  illustratively provides e-mail functionality for user  108 . Therefore, in one example, user  108  can author, send, receive, open, and delete e-mail messages. User  108  can illustratively perform configuration operations to configure his or her mailbox and e-mail account, set filters, attach various attachments to different e-mail messages, and perform a wide variety of other e-mail functionality using e-mail system  112 . 
     Scheduling logic  124  in calendar system  114  illustratively allows user  108  to schedule items on his or her calendar. The calendar data representing the calendar of user  108  is illustratively stored as calendar data in calendar data store  126 . Calendar system  114  can provide a wide variety of other calendar functionality  128 , such as the ability to send and receive meeting requests, schedule tasks and appointments, schedule meetings with a variety of different users, view the availability of other users, etc. 
     Communication system  116  illustratively allows computing system  102  to communicate with a wide variety of other computing systems. For instance, it can be a communication system that allows communication over a wide area network, a local area network, a cellular communication network, a near field communication network, or a wide variety of other networks or combinations of networks. 
     User interface logic  118  illustratively generates user interfaces  104  with user input mechanisms  106 . It also illustratively detects user interaction with user input mechanisms  106  or any of a wide variety of other user input mechanisms. It can generate an indication of those user interactions and provide them to other parts of computing system  102  as well. 
     Scheduling assistance system  120  illustratively uses scheduling assistant workflow processing system  132  to detect when a user  108  requests system  120  to assist in scheduling an event, such as a meeting, a telephone call, etc. In one example, user  108  can do this by authoring a natural language e-mail message and sending it to a mailbox in e-mail system  120  corresponding to scheduling assistance system  120 . When system  120  receives a message in its mailbox, it illustratively uses natural language understanding system  130  to parse the message and identify the linguistic content (e.g., the meaning) of the content in the e-mail message. It can then identify the message as either a message seeking interactions with system  120  or one that is not, but where system  120  was copied on the message. 
     When system  120  identifies the e-mail message as one seeking interaction with system  120  (such as a request to assist user  108  in performing a scheduling operation) scheduling assistant workflow processing system  132  illustratively performs a workflow that may involve having a dialog with user  108  (e.g., through e-mail system  112  or otherwise) to identify what user  108  wishes system  120  to do. This may include scheduling assistant workflow processing system  132  asking user  108  questions to identify the task to be performed. 
     System  132  can then perform operations, such as conducting information retrieval searches, searches of the calendar systems of various users, searches of other computing systems or databases, and return results. It can then perform other actions, such as interacting with scheduling logic  124  for user  108  and other users in order to schedule a meeting or another event, or it may interact with other computing systems to perform other functions (such as purchasing tickets, making reservations, among a wide variety of other things). 
     With systems such as scheduling assistance system  120 , it can be difficult to determine the level of satisfaction that user  108  has with the system. Therefore, user satisfaction signal analyzer in control system  132  illustratively monitors various signals and actions from user  108  to determine the user&#39;s satisfaction. It can then generate control signals to take actions, based upon that satisfaction. 
     It will be appreciated that, for the sake of the present example, scheduling assistance system  120  need not be a scheduling assistance system, but may be another software agent or bot that is a participant in a conversation with one or more users, when it renders its service. For instance, instead of being a system that assists users in performing scheduling operations, it may be a system that assists users in performing translation operations in an e-mail discussion among a number of users that speak different languages. It may be a system that assists users in ordering lunch, scheduling one or more different venues, purchasing airline tickets or tickets to events, or a wide variety of other systems that participate in a conversation with a set of users, in order to perform the desired service. For purposes of example only, the present description proceeds with respect to scheduling assistance system  120  being a system that assists one or more users in scheduling items. However, the same processing can be applied to other types of assistive systems or other software agents or software programs that participate in a discussion with the users that are using the service that it provides. 
     User satisfaction signal analyzer and control system  134  illustratively includes user concern detection logic  138 , sentiment analysis logic  140 , user recommendation detection logic  142 , calendar mining logic  144 , satisfaction level detection logic  145 , feature discovery engagement logic  146 , issue identification logic  147 , control signal generator logic  148 , and it can include a wide variety of other items  150 . Calendar mining logic  144 , itself, illustratively includes meeting categorization logic  152 , service coverage determination logic  154 , trend identifying logic  156 , survey logic  158 , and it can include other items  160 . Control signal generator logic  148 , itself, illustratively includes share prompting logic  162 , and it can include a wide variety of other items  164 . 
     It will first be noted, that in one example, scheduling assistance system  120  may be included or copied on messages that not only relate directly to the scheduling assistance service that is provided by system  120 , and that expressly seek interaction with system  120 , but also on other messages in a communication thread that are not necessarily seeking interaction with system  120 . For instance, it may be that user  108  sends an e-mail message to a group of other users, and also to system  120  specifically requesting that system  120  assist the group in scheduling a meeting. By way of example, a user may type “Scheduling Assistant, please help us schedule a meeting.” Scheduling assistant workflow processing system  132  then beings executing its workflow to identify the particulars of the meeting, and to identify various meeting opportunities during which the meeting can be scheduled. In doing so, it may generate e-mails back to the group of users in the conversation, and receive responses from those users. 
     However, it may also be that the users who are participating in the conversation may generate messages for one another, copying system  120  on the messages, but not seeking direct interaction with system  120 . For instance, it may be that one of the users sends a question to the entire group of users saying “What is your favorite restaurant near the meeting location?” While this communication may be copied to system  120 , it is not seeking any interaction with system  120 . Instead, it is seeking interaction with the other users. Likewise, one of the users may type a message to all of the other users, and copying system  120 , that provides an indication as to the user&#39;s satisfaction level with system  120 . For instance, assuming that system  120  is named “The ACME Scheduling Assistant”, one of the users may type a message such as “This ACME Scheduling Assistant is very slow.” Or “This ACME Scheduling Assistant is really helpful.” Thus, even though the author of that message is not seeking interaction with system  120 , the content of the message, itself, can be analyzed to identify the satisfaction level of that user, with system  120 . 
     Similarly, the actions of the users in the conversation may also give an indication as to their satisfaction level with system  120 . For instance, if one of the users sends an email recommending that other users use “The ACME Scheduling Assistant”, this may indicate that the satisfaction level of the author of that message is relatively high. However, if the user sends a message such as “Do you know of any other scheduling assistants that we might user?”, this may indicate that the satisfaction of that user is relatively low. 
     Further, it may be that, if a user is using system  120  to schedule a majority of his or her appointments on his or her calendar, this may indicate that the user is relatively satisfied with system  120 . However, if the user is using system  120  to schedule only a small number of appointments on their calendar, relative to all appointments that are on the user&#39;s schedule, that may indicate that the user is relatively unsatisfied with system  120 . 
     Further, it may be that a user is using system  120  to schedule a certain proportion of his or her meetings in one category (such as work meetings), but using system  120  to schedule a much different proportion of his or her meetings in a different category (such as personal meetings or phone calls, etc.). This may indicate that the user is relatively satisfied when using system  120  to perform operations in certain categories of operation, but is relatively unsatisfied when using system  120  to perform operations in one or more other categories of operations. 
     Similarly, the usage trend of a user in using system  120  may be helpful as well. If the user is tending to use system  120  more often, then this may be a positive trend indicating that the user is relatively satisfied with system  120 . However, if the trend is negative, in that the user is using system  120  less often, then this may indicate that the user is not as well satisfied with system  120 . 
     Further, it may be that user  108  is engaging with system  120  in different ways. For instance, it may be that the user is engaging with system  120  to perform one set of functions (such as to schedule meetings), but is also engaging with system  120  in an attempt to find out what other types of features system  120  can be used to perform. By way of example, it may be that user  108  asks system  120  (such as by typing an e-mail and sending it to the mailbox for system  120 ) whether system  120  can also be used to “Find entertainment options”, to “Schedule phone conferences”, etc. If user  108  is engaging with system  120  to find out how else user  108  may use system  120 , this may be an indication that user  108  is relatively satisfied with system  120 , or it may be an indication that additional or different features should be enabled or added to system  120 . 
     It can thus be seen that it is not only direct feedback about the performance or satisfaction level of system  120  that is helpful in determining a user&#39;s satisfaction level. Instead, the content of all messages that system  120  receives may be analyzed to determine whether they indicate a user satisfaction level. The communications with system  120  that are directly seeking its engagement, as well as communications received by system  120 , but which are not seeking its engagement, can be helpful in this regard. In addition, not only can the contents of the communications from the various users be used to determine user satisfaction, but user actions can also be used. In one example, user satisfaction signal analyzer and control system  134  captures signals indicative of the content of the communications and the user actions, and identifies one or more user satisfaction levels. It can also generate control signals to take action or control steps based upon the identified user satisfaction. 
     For instance, user concern detection logic  138  can detect when the content of a user&#39;s message indicates that the user is concerned about a particular function or feature or part of system  120 . By way of example, it may be that the user types “This ACME Scheduling Assistant is very complicated to use.” This may indicate that the user has a concern with the interface provided by system  120 , or with other parts of the interactive process. Sentiment analysis logic  140  can be used to detect user sentiment in the contents of his or her messages. For instance, there are a wide variety of different types of sentiment analyzers that can be used to analyze the sentiment in text. Sentiment analysis logic  140  illustratively determines the correlation between user sentiments identified in the messages of the users and system  120 . If the sentiment is highly correlated to system  120 , then the message may indicate the user&#39;s sentiment with respect to the performance of system  120 . User recommendation detection logic  142  illustratively detects when a user recommends that other users use system  120 . It can also detect when a user is attempting to influence other users not to use system  120 . 
     Calendar mining logic  144  illustratively mines the calendar data in calendar data store  126  for the various users that are using system  120 . Meeting categorization logic  152  can identify different meeting categories that are on a user&#39;s calendar (such as in-person meetings, telephone conferences, business lunch meetings, personal meetings, etc.). Service coverage determination logic  154  can determine a measure indicative of how often user  108  used system  120  to schedule those meetings, in the different categories, relative to how often the user is not using system  120 . Trend identifying logic  156  illustratively identifies trends in usage, over time, by user  108  (or other users of system  120 ). 
     Survey logic  158  can be used to control scheduling assistant workflow processing system  132  to request that a user complete a survey that asks more detailed questions about the user&#39;s satisfaction level with system  120 . The users can be identified for taking the survey based upon the satisfaction level that has been determined for the user, based upon the user&#39;s usage of system  120 , or in other ways. 
     Feature discovery engagement logic  146  illustratively identifies communications that are engaging system  120  in a way in which the user  108  is seeking to identify additional features that may be offered by system  120 . This category of engagement with system  120  can be indicative of user satisfaction. 
     Satisfaction level detection logic  148  illustratively detects a satisfaction level for a user  108 , given the information detected by one or more of user concern detection logic  138 , sentiment analysis logic  140 , user recommendation detection logic  142 , calendar mining logic  144 , survey logic  158 , feature discovery engagement logic  146 , and any other items of information. Issue identification logic  147  can use the same or different information to identify any particular issues with which the user may be concerned, with respect to interacting with, and using, system  120 . 
     Control signal generator logic  148  can then generate various control signals based on the satisfaction level for a given user, based upon the issues identified for a given user, or based on other items. For instance, share prompting logic  162  can control scheduling assistant workflow processing system  132  to generate a communication with user  108  prompting, or suggesting that, the user recommend system  120  to other users. For instance, if the user is determined to be highly satisfied with system  120 , then it may be that the user will likely follow the suggestion or prompt, and recommend system  120  to other users. A wide variety of other control signals can be generated based upon the identified user satisfaction level and any issues identified for a particular user. 
       FIG.  2    is a flow diagram illustrating one example of the operation of the architecture shown in  FIG.  1   , in more detail.  FIGS.  1  and  2    will now be described in conjunction with one another. 
     It is first assumed that scheduling assistance system  120  is running and is available for interaction by user  108 . This is indicated by block  180  in the flow diagram of  FIG.  2   . It may be, as mentioned above, that system  120  is a software agent, or an intelligent digital assistant or web bot or other such computing system. It is illustratively a system that receives communications from a group of users who are attempting to use the service that it provides. It receives communications from those users, even when they are not directly attempting to interact with system  120 , once they have asked for system  120  to assist them in providing a service. 
     Natural language understanding system  130  then illustratively detects a request message from a user, requesting to use the scheduling assistance system  120 . This is indicated by block  182 . By way of example, it may be that system  120  detects an e-mail message sent to its inbox as indicted by block  184 . It may detect a voice command  186  or another type of communication  188 . Natural language understanding system  130  then identifies the linguistic meaning of that request as a request for assistance by system  120 . 
     Scheduling assistant workflow processing system  130  then conducts a dialog (such as by participating in a discussion with a group of users) to assist in scheduling something for the users, based upon the request. Of course, where system  120  is another type of assistant, bot, computer agent, etc., then it conducts a dialog to assist is providing another type of service. Conducting the dialog is indicated by block  190  in the flow diagram of  FIG.  2   . 
     In one example, scheduling assistant workflow processing system  132  sends and receives scheduling-specific messages (or messages where users are attempting to interact with system  120 ). This is indicated by block  192 . Also, in one example, system  120  receives non-scheduling-specific messages (or messages where the users may be communicating with one another, but are not attempting to interact with system  120 ). This is indicated by block  194 . Again, as discussed above, this may be messages where the users are communicating with one another, but not necessarily intending for system  120  to act on those messages, or to otherwise interact with them. 
     Natural language understanding system  130  then performs natural language understanding to obtain the linguistic meaning of the message content of all the received messages (and not just the messages where interaction with system  120  is intended or sought). Obtaining the linguistic meaning of the message content is indicated by block  196 . The dialog can be conducted in a wide variety of other ways as well, and this is indicated by block  198 . 
     User concern detection logic  138  receives the linguistic meaning of the content and performs linguistic processing on all the received messages to detect any user concerns with system  120 . This is indicated by block  200 . For instance, it may be that certain words (or other linguistic units) trigger rules or mappings that map to issues of concern. When user concern detection logic  138  sees those words in the linguistic meaning of the content of the messages, then it may access those mappings or rules to identify the concerns being expressed by the user. The user concerns may be identified by a dynamic model or in other ways as well. 
     Sentiment analysis logic  140  also performs sentiment analysis on the content of the received messages. For instance, it may identify different categories of sentiment, such as favorable, strongly favorable, disfavorable, strongly disfavorable, negative, positive, angry, or a wide variety of other sentiments, based upon the content of the received messages. Performing sentiment analysis to detect the sentiment in all received messages is indicated by block  202 . Both user concern detection logic  138  and sentiment analysis logic  140  illustratively provide an output signal indicative of any detected sentiment and user concerns. In one example, they correlate the information to the particular user and to any particular categories or features of operation of assistance system  120 . By way of example, it may be that a user comments that a particular feature of system  120  is very useful, or not very useful, etc. In that case, user concern detection logic  138  may identify the particular feature that the user is concerned with as described above, and sentiment analysis logic  140  may identify a strong positive or strong negative (or other) sentiment associated with that feature, for that user. 
     User recommendation detection logic  142  also detects any messages where a user is recommending system  120  for use by other users. It also generates an output signal indicative of the fact that the user has recommended system  120 . It may provide other information as well, such as the number of users that system  120  was recommended to, among other things. Detecting any messages in which the user recommends the service (e.g., system  120 ) to another user is indicated by block  204 . 
     Calendar mining logic  144  then mines the calendar information in calendar data store  126  to generate a measure indicative of how often the service is being used by the user, relative to scheduling in other ways. This is indicated by block  206 . In one example, system  120  has access to the user&#39;s calendar data, and can therefore count the number of meetings or other calendar items that have been scheduled. It also illustratively tracks which of those items have been scheduled by user  108  using system  120 . It can then generate a measure indicative of what proportion of the time user  108  is using system  120  to schedule different calendar items. 
     In one example, meeting categorization logic  152  detects different scenarios of usage, such as different categories of meetings that the user is scheduling. These may include work meetings, personal meetings, telephone calls, in-person meetings, off-site meetings, etc. Detecting different scenarios or categories of usage is indicated by block  208 . 
     Service coverage determination logic  154  then determines a proportion or other measure of usage, in each of the identified categories. For instance, it may be that user  108  is using system  120  a relatively large amount of the time (relative to the user scheduling without the use of system  120 ) in order to schedule personal meetings, but a relatively small amount of the time in order to schedule work meetings. Determining a proportion of usage in each category or scenario is indicated by block  210 . 
     Trend identifying logic  156  illustratively aggregates the usage proportions (or coverage information), over time, in order to determine whether the usage of user  108  in the different categories is increasing or decreasing as a proportion of the number of meetings or calendar items scheduled in those different categories. This is indicated by block  212 . Detecting or generating a measure indicative of how often the service is being used relative to scheduling in other ways can be done in a wide variety of different ways, in addition or instead of those discussed above. This is indicated by block  214 . 
     Calendar mining logic  144  illustratively generates output signals indicative of the various things detected. For instance, it can output a signal indicative of the different meeting categories or scenarios identified by logic  152 . It can output a signal indicative of service coverage determined by logic  124 , and the various trends identified by logic  156 . It can output a wide variety of other information as well. 
     Feature discover engagement logic  146  also illustratively detects when the user engages with service  120  to discover additional functionality. This is indicated by block  216 . For instance, it may identify the different categories or types of engagement that user  108  uses to engage with system  120 . Those types of engagement may include, for instance, engaging system  120  to assist in scheduling a work meeting. They may also include engagement to inquire what other types of services or features system  120  may provide. Identifying engagement as not having a scheduling intent (or as an engagement where the user is not attempting to use system  120  to schedule a meeting) is indicated by block  218 . The various types of engagement can be classified into different categories as indicated by block  220 . The particular engagements that are inquiries into what various features may be provided by system  120  can then be identified. This is indicated by block  222 . The number of engagements (or the level of those engagements measured in another way) may be indicative of user satisfaction. Detecting engagements with the service to discover additional functionality can be done in other ways as well. This is indicated by block  224 . 
     Feature discovery engagement logic  146  can also generate a variety of different output signals. For instance, it may generate output signals indicative of the different categories of engagement that a particular user is using. It may output a signal indicative of the level of engagement where the user is seeking additional features. It can output other signals as well. 
     Based upon the various signals output by the different pieces of logic or systems discussed above, satisfaction detection logic  145  then illustratively determines a user satisfaction level for scheduling assistant system  120 . This is indicated by block  226  in the flow diagram of  FIG.  2   . Again, it can be based on detected concerns, user sentiment, recommendations, a measure of coverage, non-scheduling engagements, and a wide variety of other items. This is indicated by block  228 . 
     Satisfaction level detection logic  145  can also go on to determine additional detailed information. For instance, it may generate an output indicative of the satisfaction of a given user with each of the different categories or features of system  120 , or with using system  120  in each of a plurality of different ways or under different scenarios. This is indicated by block  230 . The user satisfaction can be determined in a wide variety of other ways as well, and this is indicated by block  232 . 
     Satisfaction level detection logic  145  then illustratively outputs one or more signals indicative of the determined user satisfaction. For instance, it can output a signal indicative of the overall user satisfaction of user  108 , with respect to system  120 . It can output signals indicative of the user satisfaction corresponding to different features, the trends in the user satisfaction overall and corresponding to different features or categories or usage scenarios. It can also output a signal indicative of how confident it is with respect to the identified user satisfaction levels. For instance, if the user satisfaction levels are determined based on a relatively large amount of data, then the system may be more confident. Also, if the content of the messages or the actions are less ambiguous, then the confidence in the determined satisfaction level may be higher as well. 
     Based upon the information output by satisfaction level detection logic  145 , and any other information, control signal generator logic  148  then generates one or more control signals. This is indicated by block  234 . For instance, logic  148  can control survey logic  158 , or user interface logic  118 , or both, to conduct a natural language survey with one or more users, based upon their user satisfaction. This is indicated by block  236 . Share prompting logic  162  can control user interface logic  118  to suggest that a particular user recommend the service to other users. This is indicated by block  238 . For instance, it can use user interface logic  118  to generate a message (such as an e-mail message or other message) and surface that message for user  108  suggesting that user  108  recommend usage of system  120  to one or more other users. It may generate the message as a predefined suggestion that user  108  simply needs to forward to other users. The message may have a link where the other users can easily configure a system  120  for themselves, or otherwise. 
     Control signal generator logic  148  can also illustratively control communication system  116  to generate an intervention communication where the user satisfaction level warrants it. For instance, where a user is highly unsatisfied, then logic  148  may control communication system  116  to generate a communication to the unsatisfied user, or to a support person that may contact the unsatisfied user. Controlling communication system  116  to generate an intervention communication to the user, a support person, etc., is indicated by block  242 . 
     Control signal generator logic  148  may also illustratively generate a control signal that is provided to a remote system or another part of computing system  102 , that aggregates the results of the various items of logic and systems, over multiple users, and surfaces the aggregated results. This is indicated by block  244 . For instance, the aggregation of results may reveal that a relatively large number of users have issues with a same set of features or functionality on system  120 . It may also indicate that a relatively large number of users is seeking one or more additional items of functionality from system  120 . It may indicate that a relatively large number of users have the same types of issues or concerns with respect to system  120 . This, and a wide variety of other information, can be obtained from the aggregated satisfaction results. 
     Control signal generator logic  148  can generate control signals in a wide variety of other ways as well. This is indicated by block  246 . 
     It can thus be seen that the present discussion enhances the computing system itself. The computing system is configured to capture a set of signals that can be used, or combined, to identify user satisfaction for different features or different portions of a computing system. This can be obtained not only from communications where users are attempting to engage with the computing system, but even where users are not attempting to directly engage with that system. In addition, a wide variety of different types of control signals can be generated based upon the signals captured. 
     It will be noted that the above discussion has described a variety of different systems, components and/or logic. It will be appreciated that such systems, components and/or logic can be comprised of hardware items (such as processors and associated memory, or other processing components, some of which are described below) that perform the functions associated with those systems, components and/or logic. In addition, the systems, components and/or logic can be comprised of software that is loaded into a memory and is subsequently executed by a processor or server, or other computing component, as described below. The systems, components and/or logic can also be comprised of different combinations of hardware, software, firmware, etc., some examples of which are described below. These are only some examples of different structures that can be used to form the systems, components and/or logic described above. Other structures can be used as well. 
     The present discussion has mentioned processors and servers. In one embodiment, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. They are functional parts of the systems or devices to which they belong and are activated by, and facilitate the functionality of the other components or items in those systems. 
     Also, a number of user interface displays have been discussed. They can take a wide variety of different forms and can have a wide variety of different user actuatable input mechanisms disposed thereon. For instance, the user actuatable input mechanisms can be text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. They can also be actuated in a wide variety of different ways. For instance, they can be actuated using a point and click device (such as a track ball or mouse). They can be actuated using hardware buttons, switches, a joystick or keyboard, thumb switches or thumb pads, etc. They can also be actuated using a virtual keyboard or other virtual actuators. In addition, where the screen on which they are displayed is a touch sensitive screen, they can be actuated using touch gestures. Also, where the device that displays them has speech recognition components, they can be actuated using speech commands. 
     A number of data stores have also been discussed. It will be noted they can each be broken into multiple data stores. All can be local to the systems accessing them, all can be remote, or some can be local while others are remote. All of these configurations are contemplated herein. 
     Also, the figures show a number of blocks with functionality ascribed to each block. It will be noted that fewer blocks can be used so the functionality is performed by fewer components. Also, more blocks can be used with the functionality distributed among more components. 
       FIG.  3    is a block diagram of architecture  100 , shown in  FIG.  1   , except that its elements are disposed in a cloud computing architecture  500 . Cloud computing provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that delivers the services. In various embodiments, cloud computing delivers the services over a wide area network, such as the internet, using appropriate protocols. For instance, cloud computing providers deliver applications over a wide area network and they can be accessed through a web browser or any other computing component. Software or components of architecture  100  as well as the corresponding data, can be stored on servers at a remote location. The computing resources in a cloud computing environment can be consolidated at a remote data center location or they can be dispersed. Cloud computing infrastructures can deliver services through shared data centers, even though they appear as a single point of access for the user. Thus, the components and functions described herein can be provided from a service provider at a remote location using a cloud computing architecture. Alternatively, they can be provided from a conventional server, or they can be installed on client devices directly, or in other ways. 
     The description is intended to include both public cloud computing and private cloud computing. Cloud computing (both public and private) provides substantially seamless pooling of resources, as well as a reduced need to manage and configure underlying hardware infrastructure. 
     A public cloud is managed by a vendor and typically supports multiple consumers using the same infrastructure. Also, a public cloud, as opposed to a private cloud, can free up the end users from managing the hardware. A private cloud may be managed by the organization itself and the infrastructure is typically not shared with other organizations. The organization still maintains the hardware to some extent, such as installations and repairs, etc. 
     In the example shown in  FIG.  3   , some items are similar to those shown in  FIG.  1    and they are similarly numbered.  FIG.  3    specifically shows that computing system  102  can be located in cloud  502  (which can be public, private, or a combination where portions are public while others are private). Therefore, user  108  uses a user device  504  to access those systems through cloud  502 . 
       FIG.  3    also depicts another example of a cloud architecture.  FIG.  3    shows that it is also contemplated that some elements of computing system  102  can be disposed in cloud  502  while others are not. By way of example, data store  126  can be disposed outside of cloud  502 , and accessed through cloud  502 . In another example, system  134  (or other items) can be outside of cloud  502 . Regardless of where they are located, they can be accessed directly by device  504 , through a network (either a wide area network or a local area network), they can be hosted at a remote site by a service, or they can be provided as a service through a cloud or accessed by a connection service that resides in the cloud. All of these architectures are contemplated herein. 
     It will also be noted that architecture  100 , or portions of it, can be disposed on a wide variety of different devices. Some of those devices include servers, desktop computers, laptop computers, tablet computers, or other mobile devices, such as palm top computers, cell phones, smart phones, multimedia players, personal digital assistants, etc. 
       FIG.  4    is a simplified block diagram of one illustrative example of a handheld or mobile computing device that can be used as a user&#39;s or client&#39;s hand held device  16 , in which the present system (or parts of it) can be deployed.  FIGS.  5 - 6    are examples of handheld or mobile devices. 
       FIG.  4    provides a general block diagram of the components of a client device  16  that can run components computing system  102  or user device  504  or system  134  or that interacts with architecture  100 , or both. In the device  16 , a communications link  13  is provided that allows the handheld device to communicate with other computing devices and under some embodiments provides a channel for receiving information automatically, such as by scanning Examples of communications link  13  include an infrared port, a serial/USB port, a cable network port such as an Ethernet port, and a wireless network port allowing communication though one or more communication protocols including General Packet Radio Service (GPRS), LTE, HSPA, HSPA+ and other 3G and 4G radio protocols, 1×rtt, and Short Message Service, which are wireless services used to provide cellular access to a network, as well as Wi-Fi protocols, and Bluetooth protocol, which provide local wireless connections to networks. 
     In other examples, applications or systems are received on a removable Secure Digital (SD) card that is connected to a SD card interface  15 . SD card interface  15  and communication links  13  communicate with a processor  17  (which can also embody processors or servers from other FIGS.) along a bus  19  that is also connected to memory  21  and input/output (I/O) components  23 , as well as clock  25  and location system  27 . 
     I/O components  23 , in one embodiment, are provided to facilitate input and output operations. I/O components  23  for various embodiments of the device  16  can include input components such as buttons, touch sensors, multi-touch sensors, optical or video sensors, voice sensors, touch screens, proximity sensors, microphones, tilt sensors, and gravity switches and output components such as a display device, a speaker, and or a printer port. Other I/O components  23  can be used as well. 
     Clock  25  illustratively comprises a real time clock component that outputs a time and date. It can also, illustratively, provide timing functions for processor  17 . 
     Location system  27  illustratively includes a component that outputs a current geographical location of device  16 . This can include, for instance, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. It can also include, for example, mapping software or navigation software that generates desired maps, navigation routes and other geographic functions. 
     Memory  21  stores operating system  29 , network settings  31 , applications  33 , application configuration settings  35 , data store  37 , communication drivers  39 , and communication configuration settings  41 . Memory  21  can include all types of tangible volatile and non-volatile computer-readable memory devices. It can also include computer storage media (described below). Memory  21  stores computer readable instructions that, when executed by processor  17 , cause the processor to perform computer-implemented steps or functions according to the instructions. Similarly, device  16  can have a client system  24  which can run various applications or embody parts or all of architecture  100 . Processor  17  can be activated by other components to facilitate their functionality as well. 
     Examples of the network settings  31  include things such as proxy information, Internet connection information, and mappings. Application configuration settings  35  include settings that tailor the application for a specific enterprise or user. Communication configuration settings  41  provide parameters for communicating with other computers and include items such as GPRS parameters, SMS parameters, connection user names and passwords. 
     Applications  33  can be applications that have previously been stored on the device  16  or applications that are installed during use, although these can be part of operating system  29 , or hosted external to device  16 , as well. 
       FIG.  5    shows one example in which device  16  is a tablet computer  600 . In  FIG.  5   , computer  600  is shown with user interface display screen  602 . Screen  602  can be a touch screen (so touch gestures from a user&#39;s finger can be used to interact with the application) or a pen-enabled interface that receives inputs from a pen or stylus. It can also use an on-screen virtual keyboard. Of course, it might also be attached to a keyboard or other user input device through a suitable attachment mechanism, such as a wireless link or USB port, for instance. Computer  600  can also illustratively receive voice inputs as well. 
       FIG.  6    shows that the device can be a smart phone  71 . Smart phone  71  has a touch sensitive display  73  that displays icons or tiles or other user input mechanisms  75 . Mechanisms  75  can be used by a user to run applications, make calls, perform data transfer operations, etc. In general, smart phone  71  is built on a mobile operating system and offers more advanced computing capability and connectivity than a feature phone. 
     Note that other forms of the devices  16  are possible. 
       FIG.  7    is one example of a computing environment in which architecture  100 , or parts of it, (for example) can be deployed. With reference to  FIG.  7   , an example system for implementing some embodiments includes a general-purpose computing device in the form of a computer  810 . Components of computer  810  may include, but are not limited to, a processing unit  820  (which can comprise processors or servers from previous FIGS.), a system memory  830 , and a system bus  821  that couples various system components including the system memory to the processing unit  820 . The system bus  821  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus. Memory and programs described with respect to  FIG.  1    can be deployed in corresponding portions of  FIG.  7   . 
     Computer  810  typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer  810  and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. It includes hardware storage media including both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer  810 . Communication media typically embodies computer readable instructions, data structures, program modules or other data in a transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer readable media. 
     The system memory  830  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  831  and random access memory (RAM)  832 . A basic input/output system  833  (BIOS), containing the basic routines that help to transfer information between elements within computer  810 , such as during start-up, is typically stored in ROM  831 . RAM  832  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  820 . By way of example, and not limitation,  FIG.  7    illustrates operating system  834 , application programs  835 , other program modules  836 , and program data  837 . 
     The computer  810  may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,  FIG.  7    illustrates a hard disk drive  841  that reads from or writes to non-removable, nonvolatile magnetic media, and an optical disk drive  855  that reads from or writes to a removable, nonvolatile optical disk  856  such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive  841  is typically connected to the system bus  821  through a non-removable memory interface such as interface  840 , and optical disk drive  855  are typically connected to the system bus  821  by a removable memory interface, such as interface  850 . 
     Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program-specific Integrated Circuits (ASICs), Program-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc. 
     The drives and their associated computer storage media discussed above and illustrated in  FIG.  7   , provide storage of computer readable instructions, data structures, program modules and other data for the computer  810 . In  FIG.  7   , for example, hard disk drive  841  is illustrated as storing operating system  844 , application programs  845 , other program modules  846 , and program data  847 . Note that these components can either be the same as or different from operating system  834 , application programs  835 , other program modules  836 , and program data  837 . Operating system  844 , application programs  845 , other program modules  846 , and program data  847  are given different numbers here to illustrate that, at a minimum, they are different copies. 
     A user may enter commands and information into the computer  810  through input devices such as a keyboard  862 , a microphone  863 , and a pointing device  861 , such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  820  through a user input interface  860  that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A visual display  891  or other type of display device is also connected to the system bus  821  via an interface, such as a video interface  890 . In addition to the monitor, computers may also include other peripheral output devices such as speakers  897  and printer  896 , which may be connected through an output peripheral interface  895 . 
     The computer  810  is operated in a networked environment using logical connections to one or more remote computers, such as a remote computer  880 . The remote computer  880  may be a personal computer, a hand-held device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  810 . The logical connections depicted in  FIG.  7    include a local area network (LAN)  871  and a wide area network (WAN)  873 , but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
     When used in a LAN networking environment, the computer  810  is connected to the LAN  871  through a network interface or adapter  870 . When used in a WAN networking environment, the computer  810  typically includes a modem  872  or other means for establishing communications over the WAN  873 , such as the Internet. The modem  872 , which may be internal or external, may be connected to the system bus  821  via the user input interface  860 , or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer  810 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG.  7    illustrates remote application programs  885  as residing on remote computer  880 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
     It should also be noted that the different embodiments described herein can be combined in different ways. That is, parts of one or more embodiments can be combined with parts of one or more other embodiments. All of this is contemplated herein. 
     Example 1 is a computing system, comprising: 
     a messaging system that sends and receives group messages among a plurality of different users in a group of users; 
     an assistance computing system that receives a natural language request message from the messaging system, indicative of a user request, from a requesting user in the group of users, for interaction with the assistance system and that conducts a dialog with a user, using the messaging system, to render a service in response to the natural language request message; 
     an analyzer system that detects other natural language messages, in addition to the natural language request message and messages in the dialog, from the plurality of different users in the group of users, generates satisfaction indicia indicative of user satisfaction corresponding to the assistance computing system based on a content of the other natural language messages, the natural language request message and messages in the dialog; and 
     control signal generator logic that generates a control signal to control the computing system based on the satisfaction indicia. 
     Example 2 is the computing system of any or all previous examples wherein the control signal generator logic is configured to generate the control signal to control the messaging system to prompt the requesting user to recommend the assistance computing system to another user. 
     Example 3 is the computing system of any or all previous examples wherein the analyzer system comprises: 
     feature discovery engagement logic configured to identify a feature discovery natural language message inquiring about additional functionality offered by the assistance computing system and generating a feature discovery engagement indicator indicative of the feature discovery natural language message; and 
     satisfaction level detection logic configured to generate the satisfaction indicia based on the feature discovery engagement indicator. 
     Example 4 is the computing system of any or all previous examples wherein the assistance computing system comprises a scheduling assistant configured to render a scheduling assistance service, and wherein the analyzer system comprises: 
     calendar mining logic configured to access calendar information corresponding to the requesting user to identify a usage level indicative of a level of usage of the scheduling assistant by the requesting user; and 
     satisfaction level detection logic being configured to generate the satisfaction indicia based on the usage level. 
     Example 5 is the computing system of any or all previous examples wherein the calendar mining logic comprises: 
     service coverage determination logic configured to access the calendar information and identify how often the requesting user used the scheduling assistant in performing the scheduling operations relative to how often the user performed the scheduling operations without using the scheduling assistant to obtain a coverage indicator, the satisfaction level detection logic generating the satisfaction indicia based on the coverage indicator. 
     Example 6 is the computing system of any or all previous examples wherein the calendar mining logic comprises: 
     meeting categorization logic configured to access the calendar information and identify different categories of scheduling items for which the requesting user has performed the scheduling operation. 
     Example 7 is the computing system of any or all previous examples wherein the service coverage determination logic is configured to obtain the coverage indicator corresponding to each category of scheduling items. 
     Example 8 is the computing system of any or all previous examples wherein the calendar mining logic comprises: 
     trend identifying logic configured to identify a trend in how often the requesting user used the scheduling assistant in performing the scheduling operations relative to how often the user performed the scheduling operations without using the scheduling assistant. 
     Example 9 is the computing system of any or all previous examples wherein the analyzer system comprises: 
     user recommendation detection logic configured to detect whether the requesting user recommended the assistance computing system to other users and to generate a recommendation detected indicator, the satisfaction level detection logic being configured to generate the satisfaction indicia based on the recommendation detected indicator. 
     Example 10 is the computing system of any or all previous examples wherein the analyzer system comprises: 
     sentiment analysis logic configured to identify sentiment of the requesting user in the content of the messages and identify a correlation between the identified sentiment and the assistance computing system, the satisfaction level detection logic generating the satisfaction indicia based on the identified sentiment and the correlation to the assistance computing system. 
     Example 11 is the computing system of any or all previous examples wherein the sentiment analysis logic is configured to identify the sentiment of the requesting user in the content of the messages and identify a correlation between the identified sentiment and an individual feature of the assistance computing system, the satisfaction level detection logic generating the satisfaction indicia based on the identified sentiment and the correlation to the individual feature of the assistance computing system. 
     Example 12 is a computer implemented method, comprising: 
     receiving, at an assistance computing system, a natural language request message from a messaging system that sends and receives group messages among a plurality of different users in a group of users, the natural language request message being indicative of a user request, from a requesting user in the group of users, for interaction with the assistance system; 
     controlling the messaging system to conduct a dialog with a user to render a service in response to the natural language request message; 
     detecting other natural language messages, in addition to the natural language request message and messages in the dialog, from the plurality of different users in the group of users; 
     generating satisfaction indicia indicative of user satisfaction corresponding to the assistance computing system based on a content of the other natural language messages, the natural language request message and messages in the dialog; and 
     generating a control signal to control the computing system based on the satisfaction indicia. 
     Example 13 is the computer implemented method of any or all previous examples wherein generating the control signal comprises: 
     generating the control signal to control the messaging system to prompt the requesting user to recommend the assistance computing system to another user. 
     Example 14 is the computer implemented method of any or all previous examples wherein generating satisfaction indicia comprises: 
     identifying a feature discovery natural language message inquiring about additional functionality offered by the assistance computing system; 
     generating a feature discovery engagement indicator indicative of the feature discovery natural language message; and 
     generating the satisfaction indicia based on the feature discovery engagement indicator. 
     Example 15 is the computer implemented method of any or all previous examples wherein the assistance computing system comprises a scheduling assistant configured to render a scheduling assistance service, and wherein generating satisfaction indicia comprises: 
     accessing calendar information corresponding to the requesting user; 
     identifying a usage level indicative of a level of usage of the scheduling assistant by the requesting user; and 
     generating the satisfaction indicia based on the usage level. 
     Example 16 is the computer implemented method of any or all previous examples wherein identifying a usage level comprises: 
     identifying the level of usage, from the calendar information, by identifying how often the requesting user used the scheduling assistant in performing the scheduling operations relative to how often the user performed the scheduling operations without using the scheduling assistant; and 
     identifying a coverage indicator based on the level of usage. 
     Example 17 is the computer implemented method of any or all previous examples wherein identifying the usage level comprises: 
     identifying, from the calendar information, different categories of scheduling items for which the requesting user has performed the scheduling operation; and 
     identifying the coverage indicator corresponding to each category of scheduling items. 
     Example 18 is the computer implemented method of any or all previous examples and further comprising: 
     identifying a trend in how often the requesting user used the scheduling assistant in performing the scheduling operations relative to how often the user performed the scheduling operations without using the scheduling assistant, in each of the categories of scheduling items, based on a set of coverage indicators corresponding to each of the categories of scheduling items. 
     Example 19 is the computer implemented method of any or all previous examples wherein generating satisfaction indicia comprises: 
     detecting whether the requesting user recommended the assistance computing system to other users; 
     generating a recommendation detected indicator; and 
     generating the satisfaction indicia based on the recommendation detected indicator. 
     Example 20 is the computer implemented method of any or all previous examples wherein generating satisfaction indicia comprises: 
     identifying sentiment of the requesting user in the content of the messages; 
     identifying a correlation between the identified sentiment and the assistance computing system; and 
     generating the satisfaction indicia based on the identified sentiment and the correlation to the assistance computing system. 
     Example 21 is a computing system, comprising: 
     a software agent that receives a natural language request message indicative of a request for interaction and that provides a service based on the natural language request for interaction; 
     a messaging system that conducts a conversation that has a set of messages sent from a user to the software agent, wherein at least one message in the set of messages comprises the natural language request message; 
     a signal analyzer that analyzes content of the natural language request message and other messages in the set of messages and generates satisfaction indicia indicative of user satisfaction corresponding to the assistance computing system based on the content of the natural language request message and other messages in the set of messages; and 
     control signal generator logic that controls the messaging system based on the satisfaction indicia. 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.