Patent Publication Number: US-10789576-B2

Title: Meeting management system

Description:
BACKGROUND 
     The present invention relates generally to a meeting management system, and more particularly, but not by way of limitation, to a meeting management system for conversational topic tracking and agenda intervention. 
     Meetings often get off track (e.g., off topic) and devolve into discussions of side topics, or fail to make it through all scheduled agenda topics in an allotted time. As it is difficult to find a time when all participants can assemble, and a delay in decision-making can lead to an overall slowdown in a project, conventional techniques have been proposed to have meetings more closely follow their proposed or planned agendas. 
     Conventional techniques provide a possible metric for meeting effectiveness as covering the agenda items, but these conventional techniques do not use this metric for automatic meeting facilitation. 
     Other conventional techniques address how to signal deviations from meeting agenda and meeting discussion with respect to topics/conversation (e.g., keywords), but not with respect to the physical context of the meeting (e.g., people, location) as detected by sensors or the like. 
     Thus, there is a technical problem in that the conventional techniques are incapable of automatically detecting digressions and intervening to put the meeting back on track and nor do the conventional techniques provide a determination of which topics had been covered versus which remain could be used to generate cues to improve time allocation during meetings to similarly make them more effective. 
     SUMMARY 
     In an exemplary embodiment, the present invention can provide a meeting management system, including a target determination circuit configured to determine a target of an agenda for a meeting, the target of the agenda including which users correspond to a topic, a behavior capturing circuit configured to capture behavior of the users during the meeting, a tracking and identifying circuit configured to track the behavior of the users during the meeting and identifying a user of the users corresponding to the behavior, and a difference determination circuit configured to determine if a difference between the behavior of the users and the target of the agenda is greater than a threshold value. 
     Further, in another exemplary embodiment, the present invention can provide a non-transitory computer-readable recording medium recording a meeting management program, the program causing a computer to perform: determining a target of an agenda for a meeting, the target of the agenda including which users correspond to a topic, capturing behavior of the users during the meeting, tracking and identifying the behavior of the users during the meeting and identifying a user of the users corresponding to the behavior, and determining if a difference between the behavior of the users and the target of the agenda is greater than a threshold value. 
     Even further, in another exemplary embodiment, the present invention can provide a meeting management method, including determining a target of an agenda for a meeting, the target of the agenda including which users correspond to a topic, capturing behavior of the users during the meeting, tracking and identifying the behavior of the users during the meeting and identifying a user of the users corresponding to the behavior, and determining if a difference between the behavior of the users and the target of the agenda is greater than a threshold value. 
     There has thus been outlined, rather broadly, an embodiment of the invention in order that the detailed description thereof herein may be better understood, and in order that the present contribution to the art may be better appreciated. There are, of course, additional exemplary embodiments of the invention that will be described below and which will form the subject matter of the claims appended hereto. 
     It is to be understood that the invention is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the drawings. The invention is capable of embodiments in addition to those described and of being practiced and carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein, as well as the abstract, are for the purpose of description and should not be regarded as limiting. 
     As such, those skilled in the art will appreciate that the conception upon which this disclosure is based may readily be utilized as a basis for the designing of other structures, methods and systems for carrying out the several purposes of the present invention. It is important, therefore, that the claims be regarded as including such equivalent constructions insofar as they do not depart from the spirit and scope of the present invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The exemplary aspects of the invention will be better understood from the following detailed description of the exemplary embodiments of the invention with reference to the drawings. 
         FIG. 1  exemplarily shows a block diagram illustrating a configuration of a meeting management system  100 . 
         FIG. 2  exemplarily shows a high level flow chart for a meeting management method  200 . 
         FIG. 3  exemplarily shows pre-processing of an agenda  150 . 
         FIG. 4  exemplarily shows finding digression of a meeting using the agenda  150 . 
         FIG. 5  exemplarily shows detecting dwell time of the meeting using the agenda  150 . 
         FIG. 6  depicts a cloud computing node  10  according to an embodiment of the present invention. 
         FIG. 7  depicts a cloud computing environment  50  according to another embodiment of the present invention. 
         FIG. 8  depicts abstraction model layers according to an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     The invention will now be described with reference to  FIGS. 1-8 , in which like reference numerals refer to like parts throughout. It is emphasized that, according to common practice, the various features of the drawing are not necessarily to scale. On the contrary, the dimensions of the various features can be arbitrarily expanded or reduced for clarity. Exemplary embodiments are provided below for illustration purposes and do not limit the claims. 
     With reference now to  FIG. 1 , the meeting management system  100  includes a pre-processing circuit  101 , a target determination circuit  102 , a behavior capturing circuit  103 , a tracking and identifying circuit  104 , a difference determination circuit  105 , and a flagging circuit  106 . The meeting management system  100  includes a processor  180  and a memory  190 , with the memory  190  storing instructions to cause the processor  180  to execute each circuit of meeting management system  100 . The processor and memory may be physical hardware components, or a combination of hardware and software components. 
     Although the meeting management system  100  includes various circuits, it should be noted that a meeting management system can include modules in which the memory  190  stores instructions to cause the processor  180  to execute each module of meeting management system  100 . 
     Also, each circuit can be a stand-alone circuit, unit, etc. that can be interconnected to cooperatively produce a transformation to a result. 
     With the use of these various circuits, the meeting management system  100  may act in a more sophisticated and useful fashion, and in a cognitive manner while giving the impression of mental abilities and processes related to knowledge, attention, memory, judgment and evaluation, reasoning, and advanced computation. That is, a system is said to be “cognitive” if it possesses macro-scale properties—perception, goal-oriented behavior, learning/memory and action—that characterize systems (i.e., humans) that all agree are cognitive. 
     Cognitive states are defined as functions of measures of a user&#39;s total behavior collected over some period of time from at least one personal information collector (including musculoskeletal gestures, speech gestures, eye movements, internal physiological changes, measured by imaging circuits, microphones, physiological and kinematic sensors in a high dimensional measurement space, etc.) within a lower-dimensional feature space. In one exemplary embodiment, certain feature extraction techniques are used for identifying certain cognitive and emotional traits. Specifically, the reduction of a set of behavioral measures over some period of time to a set of feature nodes and vectors, corresponding to the behavioral measures&#39; representations in the lower-dimensional feature space, is used to identify the emergence of a certain cognitive states over that period of time. One or more preferred embodiments use certain feature extraction techniques for identifying certain cognitive states. The relationship of one feature node to other similar nodes through edges in a graph corresponds to the temporal order of transitions from one set of measures and the feature nodes and vectors to another. Some connected subgraphs of the feature nodes are herein also defined as a cognitive state. The present application also describes the analysis, categorization, and identification of these cognitive states by means of further feature analysis of subgraphs, including dimensionality reduction of the subgraphs, for example by means of graphical analysis, which extracts topological features and categorizes the resultant subgraph and its associated feature nodes and edges within a subgraph feature space. 
     Although as shown in  FIGS. 6-8  and as described later, the computer system/server  12  is exemplarily shown in cloud computing node  10  as a general-purpose computing circuit which may execute in a layer the meeting management system  100  ( FIG. 8 ), it is noted that the present invention can be implemented outside of the cloud environment. 
     The meeting management system  100  receives a textual description of a planned meeting (i.e., an agenda  150 ). The agenda  150  can include desired/optional times  340  (e.g., see  FIG. 3 ) for topics, participants to discuss those topics, locations to have those discussions, etc. 
     Based on the agenda  150 , a number of conceptual topics for discussion can be extracted by the pre-processing circuit  101 . A number of expected words are associated with each topic. For example, as exemplarily shown in  FIG. 3 , the agenda  150  includes keywords  320  and names  330  which are analyzed by the pre-processing circuit  101 . The pre-processing circuit  101  associates “who” should be talking during a meeting based on the text of the agenda  150  (i.e., the names  330 ), the topics, and the keywords  320 . 
     The pre-processing circuit  101  also can determine a location associated with the topics of the agenda  150  and names  330  in the agenda  150 . That is, as shown in  FIG. 3 , the pre-processing circuit  101  can identify that “Astor” is a specific location in which “Rachel”, “David M.”, “Andy”, and “Yedendra” should be located during that portion of the agenda  150  (i.e., at the “Astor” building”). 
     The input description of a meeting (i.e., agenda  150 ) has no specific format and may be any of a single paragraph, a list of topics, a prioritized list, and/or a list with suggested time allocations. 
     From the input sequence of the agenda  150 , a set of target topics, the person who should be talking, and the location of the meeting can be determined in a number of ways by the target determination circuit  102 . For example, the target determination circuit  102  can take top-level text entries in an outline as topics directly. The target determination circuit  102  can also use a pre-existing ontology of concepts, each associated with a collection of keywords or phrases. These keywords could be correlated with the raw text of the meeting description to select the importance and ordering of the pre-existing topics. Note that the agenda  150  may not be complete. In this case, the target determination circuit  102  may compute a target amount of time and/or break down topics, subtopics, dividing time between them. This can be done based on keywords  320  such as “important”, “main topic”, etc. indicating that a topic on the agenda  150  should receive more time than a topic associated with keywords  320  that indicate brevity of discussion for a topic. 
     Further, the target determination circuit  102  can assign a priority and an optimal dwell time to each extracted topic by the pre-processing circuit  101 . This can be done by dividing the time evenly between all the topics (with equal priorities), giving more time to topics with longer textual descriptions, or considering the first N topics as essential (high priority) and assigning M % of the meeting time to them, while assigning the remaining time to the remaining topics (again, perhaps divided evenly). For example, the target determination circuit  102  can determine a number of expected words for each topic and accordingly associate a time to spend on the topic based on the expected words for the topic. 
     Also, the target determination circuit  102  can determine the expected words to be discussed based on the topic. Based on the target agenda  150  and planned time, the target determination circuit  102  can suggest splitting of the meeting into multiple meetings. 
     The target determination circuit  102  can determine “who” (i.e., the specific person) is associated with a particular topic based on the names  330  and keywords  320  associated with a topic in the agenda  150 . 
     The behavior capturing circuit  103  captures the behavior of the participants of the meeting. Behavior can include speech utterances, typed text, gestures, actions on a Graphical User Interface (GUI), displayed content, etc. that reflect what is occurring during the meeting. That is, during the meeting, the behavior capturing circuit  103  extracts words from the auditory stream over time or uses a sensor device  140  to capture an image of the speaker. The words are then correlated with expected words for each topic to infer which topic (if any) is being discussed at each time. 
     It should be noted that the sensor device  140  can include an imaging means, an audio detection means, a motion detection means, etc. That is, the sensor device  140  is capable of measuring any behavior of a participant of the meeting using multiple measuring capabilities. 
     The behavior capturing circuit  103  can use facial recognition in order to capture who is speaking based on an image captured by the sensor device  140 . 
     The tracking and identifying circuit  104  continuously tracks the behavior of the meeting and identifies who is speaking and topic categories based on the behavior. That is, the association of keywords and phrases with a topic could be of a static, hand-generated set of weighted inference links. Alternatively, the word lists and weights can be generated automatically based on the statistical correlation of words in documents about a certain topic as compared to the correlation of these same words in documents concerning a different topic (e.g. compared with a different topic on the meeting agenda, compared with the least closely correlated topic in the meeting agenda vs. background English language usage of words—to give various degrees of being “off-topic”). Or, the correlations might be constructed hierarchically wherein a top level topic suggests several mid-level topics which, in turn, suggest further low-level topics. Here, it may be that only low-level topics are specific enough to have high word/phrase correlations, or topics at all levels may suggest certain words with varying confidences. 
     The tracking and identifying circuit  104  continuously tracks and identifies who is speaking. During the meeting, speech recognition can be performed by the behavior capturing circuit  103  for one or more of the participants. This recognition phase might include dedicated audio streams from each participant, developed through remote acoustic beam forming or by a collection of local microphones. The recognition phase performed by the behavior capturing circuit  103  might also include customized acoustic models for known speakers stored in the database  130  to improve the quality of the speech transcript. Specialized language models or grammars tailored to the likely subject matter or domain of the meeting might also be employed to boost fidelity. The person who is speaking can also be tracked and identified using facial recognition based on an image captured by the sensor device  140 . 
     The tracking and identifying circuit  104  can identify topic categories that a person is speaking about based on the behavior by, for example, the inference of a topic from either text or a speech transcript within a mathematical weighting framework. Also, the tracking and identifying circuit  104  can utilize term frequency, inverse document frequency (TF-IDF). Alternatively, the influences of various keywords could be combined more directly based on probabilities. This can be done by using suitable (e.g. Bayesian) methods to estimate p(topic|word) or p(topic|word distribution) and error bars on these estimates, using suitable assumptions about independence between different keywords and multiple occurrences of the same keyword 
     Further, the tracking and identifying circuit  104  can track and identify words in the meeting versus the agenda  150  by applying “thesaurus amplification” to both the agenda  150  and the speech transcript as captured by the behavior capturing circuit  103 . The “thesaurus amplification” can use key words in each separate document to suggest additional keywords for that document. For example, if the behavior capturing circuit  103  captures “network bandwidth”, this might dredge up the strongly related terms “router”, “Ethernet”, “IPv4”, “fiber optic”, and “NIC”. Such a method expands a short piece of text into a conceptually much longer one. Another possibility is to require people to be more precise about their agenda  150 . This could be done, for example, by pointing to a draft document, presentation, Wikipedia page, web site of an academic group, etc. in the invitation and then “scraping” these auxiliary sources for additional related concepts. Still another method would be to require an elaborated speech description at the start of every meeting to reiterate the agenda and topics to be discussed. This would provide more agenda text to work with, but from the speech transcript rather than a written pre-meeting invitation. 
     Also, the tracking and identifying circuit  104  compares expected words of the meeting against the recognized words during the meeting by the behavior capturing circuit  103 . 
     Additionally, the tracking and identifying circuit  104  can track and identify the location of a person who is speaking. This can be done by using a phone line location, associating the device in a room with the location of the person speaking, using surroundings of a captured image by the sensor device  140 , GPS location tracking, etc. For example, if a user calls into a meeting from a home phone, the tracking and identifying circuit  104  identifies that the user is in a different location. 
     Based on the data of the tracking and identifying circuit  104 , the difference determination circuit  105  determines if a difference between the behavior captured by the behavior capturing circuit  103  as associated with a person speaking and a target of the meeting based on the agenda  150  is greater than a predetermined threshold. For example, if a person is discussing elephants when the meeting is about profit margins, the difference determination circuit  105  determines that the difference between the behavior of the person discussing elements and the target of the meeting for profit margins is greater than a threshold. 
     The difference determination circuit  105  can determine if an incorrect person is speaking. For example, as exemplarily shown in  FIG. 4 , names  330  (i.e., “Yunfeng”) are associated with topic  1  as seen in  FIG. 3  and keywords  320  (i.e., “demo”) are also associated with topic  1 . Similarly, as shown in  FIG. 5 , names  330  and keywords  320  are each associated with topic  3 . Therefore, the difference determination circuit  105  determines that there is no digression (i.e., an incorrect person is not speaking) between who is speaking and who should be speaking when discussing a topic. However, as further shown in  FIG. 4 , the difference determination circuit  105  determines that when “Jon” and “Alan” speak that “who” is speaking is different than what is stated on the agenda  150 . Thus, the difference determination circuit  105  determines a difference from the agenda  150  based on who is speaking. 
     Also, even if the “correct” person is speaking about a topic based on comparing the names  330  and the keywords  320 , the difference determination circuit  105  can determine if there is an excessive dwell time on a topic or if the person is off-topic. For example, if there is a sequence of words uncorrelated with an extracted topic (i.e., not keywords  320 ) captured by the behavior capturing circuit  103 , the difference determination circuit  105  determines that there exists a “digression” (i.e., difference from the agenda  150 ) and the meeting participants are signaled about this event (as described later). The difference determination circuit  105  determines the degree of the difference or digression. That is, if “Yunfeng” begins to speak words completely unrelated to the keyword  320  “demo”, the difference determination circuit  105  determines that even though the correct user is speaking, that the user has digressed from the topic and the agenda is off-topic. 
     The sequence of inferred topics over time is compared with the initial agenda  150  to determine which topics have and have not been covered up to the current time. This can be used to roll-over uncovered topics to future meetings. It can also be used to suggest moving on to a different topic if it looks likely that all the agenda items will not be covered in the time allotted. 
     “Target” as referenced above can be, for example, a point in time during the meeting when an assessment is done. For example, after 15 minutes into an hour long meeting everyone should be assembled and starting to discuss the first topic. Also, “difference” can be, for example, a long sequence of words uncorrelated with an agenda  150  topic, a participant who is discussing a later topic, a participant not on the agenda  150  who is talking, and interruptions. Further, “threshold” can be based on a factor of a time (i.e., if a meeting diverges for more than 5 minutes from schedule), a topic (i.e., meeting is more than 50% off-topic sentences), participants (i.e., more than two people not on the invite list), and a location (i.e., meeting has started in wrong location). 
     The difference determination circuit  105  can identify a person in the meeting who caused the difference (i.e., digression) between the meeting and the agenda  150  and issue a notification. Further, when the difference determination circuit  105  determines the meeting as digressed, and depending on the energy and excitement levels of participants, the difference determination circuit  105  can signal discussion interrupt. 
     It is noted that the determination of being off-topic can be done on approximately a paragraph&#39;s worth of text at a time. Thus, the chunks of text used to obtain Bayesian priors would be approximately a paragraph in length, though these paragraphs should not always be taken at paragraph or even sentence boundaries since in speech transcription there are no clear paragraph demarcations (or even, generally, sentence demarcations). The frequency of occurrence of words in these pseudo-paragraphs should be compared with the words in the remainder of the document and the words in all other documents fetched from a web search using the topic description. 
     The difference determination circuit  105  can continuously learn, based on previous meetings, particular users, topics, or other items that cause a meeting to digress. In this manner, the difference determination circuit  105  acts as a cognitive unit. Based on the repeat occurrences that derail a meeting, the difference determination circuit  105  can pre-emptively signal the system that the meeting will derail before it occurs. 
     Also conceivable, if difference determination circuit  105  determines that the meeting has a difference from the agenda  150 , the difference determination circuit  105  can also gauge the meeting participant&#39;s dissatisfaction with this fact. For example, if it is determined by the difference determination device  105  that one person going off topic for a long time and no one else talking then it is likely that the flagging circuit  106  should flag the meeting. However, if the meeting has gone off topic but the participants are energized (as determined by using speech and vocal clues), the difference determination circuit  105  will not issue a signal to the flagging circuit  106  since the meeting being off topic seems to improve the meeting. 
     Based on the difference determined by the difference determination circuit  105 , the flagging circuit  106  flags the difference. Flagging by the flagging circuit  106  can include notifying the leader of the meeting, announcing the difference between the behavior and the target using a generated voice that can be heard by the participants, notating difference from the meeting agenda  150 , control of a lighting sequence at the meeting&#39;s location, etc. 
     That is, based on a difference between who is speaking and who should be speaking, who is speaking and what they are speaking about as it is related to the topic, who is speaking and where they are speaking as related to where they should be speaking from, the topic being discussed, and the time the topic is being discussed for, the difference determination circuit  105  determines the difference and causes the flagging circuit  106  to flag the meeting that a difference is occurring. 
     The difference determined by the difference determination circuit  105  can cause the flagging circuit  106  to utilize a facilitation unit  120  as an intelligent meeting facilitator, which, based on the flagging circuit  106 , triggers appropriate facilitation statements output by the facilitation unit  120  to guide the meeting back on track. 
     An alternative user interface as the facilitation unit  120  can be a visualization of the agenda topics that have been covered and what has yet to be covered. Another example is to show, for the given topic how on/off topic the group is currently deemed to be with a view meter. The meter may indicated “on topic” by a needle pointing into the middle of a green region and “off topic” by the needle pointing into a red region. 
     Yet another alternative for the facilitation unit  120  based on the flagging circuit  106  flagging the meeting can be to privately prompt individual participants to change the meeting topic, that it was time to stop talking about the current topic, or to start talking about one of the following topics that had yet to be covered. 
     If a reason that the flagging circuit  106  is flagging the meeting is due to a digression caused by the same user(s) multiple times, the facilitation unit  120  can be configured to output a notification directly to the user(s) or to the user(s) supervisor(s). 
     The facilitation unit  120  can also split a meeting into multiple parts based on the flagging by the flagging circuit  106 . 
     Further based on the meeting being flagged by the flagging circuit  106 , prompts and/or suggestions for future meeting topics or agenda items can be provided after the meeting by the facilitation unit  120  based on analysis of agenda items that had not been covered, items that had been covered, metrics such as an amount of time devoted to an agenda topic and the people that had participated in the conversation about an item. 
     Also, the system  100  can include prompts and/or suggestions for future meetings topics or agenda items which are provided after the meeting based on analysis of agenda items that had not been covered, items that had been covered, metrics such as amount of time devoted to agenda topic and who had participated in the conversation about an item. This can be done by the flagging circuit  106 . 
     Also, if the meeting has gone off topic, to gauge the group&#39;s dissatisfaction with this fact. For example if it is one person going off topic for a long time and no one else talking then the chances are it&#39;s a problem and the automated facilitator should step in. On the other hand, if the meeting has gone off topic but the participants seem energized (using speech and vocal clues), it may be that the automated facilitator should just indicate that the meeting seems to have veered off topic but not interrupt. In other words, the flagging circuit  106  can either flag or not flag based on a conduct of the meeting. 
     Utterances from non-participants can be filtered by including audio just from certain microphones, using speaker identify verification and checking against the meeting invite list, using active badges, or other means as detected by the sensor device  104 . 
       FIG. 2  shows a high level flow chart for a method  200  of meeting management. 
     Step  201  receives an agenda  150  and pre-processes the agenda by extracting a number of conceptual topics. A number of expected words are associated with each topic. For example, as exemplarily shown in  FIG. 3 , the agenda  150  includes keywords  320  and names  330  which are analyzed in Step  201 . Step  201  associates “who” should be talking during a meeting based on the text of the agenda  150  (i.e., the names  330 ), the topics, and the keywords  320 . For example, for a topic like “ion implantation progress” it would be natural to assume that, Joe, the manager of the ion implantation lab (as determined by a database look up) would be speaking if no one else was called out explicitly. 
     Step  201  can determine a location associated with the topics of the agenda  150  and names  330  in the agenda  150 . That is, as shown in  FIG. 3 , Step  201  can identify that “Astor” is a specific location in which “Rachel”, “David M.”, “Andy”, and “Yedendra” should be located during that portion of the agenda  150  (i.e., at the “Astor” building”). Conversely, from a given set of people a default location could be inferred. For example, Lior and Zeva are both from the Israel research lab (as determined from the personnel database) and hence are likely to be calling in from the Haifa building. 
     Step  202  determines the target of the agenda  150  by determining a set of target topics, who should be talking, the location of the meeting, etc. 
     Step  203  captures the behavior of the participants of the meeting. Step  203  can capture the audio of the users of the meeting and compare the audio sample with a sample pre-recorded and stored in a database  130  to confirm the identity of the user. Also, step  203  captures an image of the speaker using a sensor device  140 . The identity of remote participants can often be inferred from the phone number they are calling from (e.g. Tom&#39;s office means it is likely Tom speaking). Similarly, the location of a remote user can often also be determined by phone number (e.g. Tom&#39;s office is in Yorktown, N.Y.). The location of physical participants can be inferred from the known location of the sensing device used to identify them. For example, Kevin is speaking on microphone 7 which is known to be in room 12-001 so Kevin must also be in room 12-001. 
     Step  204  continuously tracks the behavior of the meeting and identifies who is speaking. It may also identify topic categories based on the behavior of step  203 . Determining which participant is speaking might be as simple as checking the audio volume on some particular input line. Alternatively, a visual analysis circuit might use sensing device  140  to detect motion in the mouth region of a participant&#39;s face. 
     Based on the data of the tracking and identifying in step  204 , step  205  determines if a difference between the behavior captured by step  203  and a target of the meeting based on the agenda  150  is above a predetermined threshold. This could be done by, for example, noticing that the current speaker is unexpected, even in the absence of speech transcription data 
     If the difference of step  205  is above the threshold, step  206  flags the meeting that the meeting is off topic and remedial action can be taken to, for example, put the meeting back on-track. 
     That is, Step  206  can use an interface for displaying and flagging the difference in step  206 . A visualization of the agenda topic shows what topics have been covered and what has yet to be covered. There is a meter for the current topic that shows how on-topic or off-topic the group is. On-topic is indicated by a needle pointing into the middle of a green region, and when off topic the needle points into a red region. 
     Another important bit of information is who is taking the meeting off-track. There is a threshold for the number of times a given individual can be the one who takes the meeting off-track. If participant-specific threshold is above a minimum, a messages is delivered to the guilty party or parties privately. Step  206  can be configured to send a mobile text message (i.e., a flag) to the parties or to display a message on a graphical user interface on the private computer of the individual or both. The message can include the portion of the “difference” that is due to the individual. For example, indicating the agenda topic and who is supposed to be speaking and showing how long the “offending” individual has been speaking. 
     Note that a private message can also be sent to the person responsible for the next meeting topic to implement the step  206  of putting the meeting back on track. 
     Exemplary Hardware Aspects, Using a Cloud Computing Environment 
     It is understood in advance that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client circuits through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure comprising a network of interconnected nodes. 
     Referring now to  FIG. 6 , a schematic of an example of a cloud computing node is shown. Cloud computing node  10  is only one example of a suitable cloud computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the invention described herein. Regardless, cloud computing node  10  is capable of being implemented and/or performing any of the functionality set forth hereinabove. 
     In cloud computing node  10 , there is a computer system/server  12 , which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server  12  include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop circuits, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or circuits, and the like. 
     Computer system/server  12  may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system/server  12  may be practiced in distributed cloud computing environments where tasks are performed by remote processing circuits that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage circuits. 
     As shown in  FIG. 6 , computer system/server  12  in cloud computing node  10  is shown in the form of a general-purpose computing circuit. The components of computer system/server  12  may include, but are not limited to, one or more processors or processing units  16 , a system memory  28 , and a bus  18  that couples various system components including system memory  28  to processor  16 . 
     Bus  18  represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or 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 Interconnects (PCI) bus. 
     Computer system/server  12  typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server  12 , and it includes both volatile and non-volatile media, removable and non-removable media. 
     System memory  28  can include computer system readable media in the form of volatile memory, such as random access memory (RAM)  30  and/or cache memory  32 . Computer system/server  12  may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system  34  can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus  18  by one or more data media interfaces. As will be further depicted and described below, memory  28  may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention. 
     Program/utility  40 , having a set (at least one) of program modules  42 , may be stored in memory  28  by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules  42  generally carry out the functions and/or methodologies of embodiments of the invention as described herein. 
     Computer system/server  12  may also communicate with one or more external circuits  14  such as a keyboard, a pointing circuit, a display  24 , etc.; one or more circuits that enable a user to interact with computer system/server  12 ; and/or any circuits (e.g., network card, modem, etc.) that enable computer system/server  12  to communicate with one or more other computing circuits. Such communication can occur via Input/Output (I/O) interfaces  22 . Still yet, computer system/server  12  can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter  20 . As depicted, network adapter  20  communicates with the other components of computer system/server  12  via bus  18 . It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server  12 . Examples, include, but are not limited to: microcode, circuit drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc. 
     Referring now to  FIG. 7 , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  comprises one or more cloud computing nodes  10  with which local computing circuits used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing circuit. It is understood that the types of computing circuits  54 A-N shown in  FIG. 8  are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized circuit over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG. 8 , a set of functional abstraction layers provided by cloud computing environment  50  ( FIG. 7 ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG. 8  are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage circuits  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and, more particularly relative to the present invention, the meeting management system  100  described herein. 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 
     Further, Applicant&#39;s intent is to encompass the equivalents of all claim elements, and no amendment to any claim of the present application should be construed as a disclaimer of any interest in or right to an equivalent of any element or feature of the amended claim.