Patent Publication Number: US-2018032967-A1

Title: Calendar management for recommending availability of an invitee

Description:
BACKGROUND 
     The present invention relates to systems and methods of a calendar management system, and more specifically to embodiments of a calendar management system and method that takes into account a series of metrics of an invitee to recommend an availability of the invitee. 
     Calendar management programs can be used to schedule meetings with one or more invitees. Availability data may be used to determine whether or not a meeting is possible. For example, a user may suggest a meeting time to an invitee with the knowledge that this person has no other scheduled meeting at that time. Additional tools have become available to yield a more effective meeting proposal, such as minimizing travel distance for the invitee, and updating real-time changes in time slot availability. 
     SUMMARY 
     An embodiment of the present invention relates to a method, and associated computer system and computer program product, for determining an availability of an invitee. A processor of a computing system receives data from one or more sensors associated with the invitee, the one or more sensors communicatively coupled to the computing system, wherein the data received by the one or more sensors provides a plurality of metrics of the invitee based on a plurality of factors. A weighting factor is assigned, by the processor, to each metric of the plurality of metrics. A total score is calculated, by the processor, based on an aggregate of the weighted plurality of metrics. A recommendation is provided, by the processor, as to the availability of the invitee based on the total score. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  depicts a block diagram of a calendar management system, in accordance with embodiments of the present invention. 
         FIG. 2  depicts a block diagram of a metrics module of the calendar management system of  FIG. 1 , in accordance with embodiments of the present invention. 
       FIG,  3  depicts a flow chart of a method for determining an availability recommendation, in accordance with embodiments of the present invention. 
         FIG. 4  depicts a flow chart of a step of the method of  FIG. 3  for providing an availability recommendation based on a total score, in accordance with embodiments of the present invention. 
         FIG. 5  depicts a flow chart of a step of the method of  FIG. 3  for performing an update to the availability recommendation, in accordance with embodiments of the present invention. 
         FIG. 6  illustrates a block diagram of a computer system for the calendar management system  FIG. 1 , capable of implementing methods for providing an availability recommendation of  FIG. 3 , in accordance with embodiments of the present disclosure. 
         FIG. 7  depicts a cloud computing environment, in accordance with embodiments of the present invention. 
         FIG. 8  depicts abstraction model layers, in accordance with embodiments of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Current methods for calendar management ignore the invitee&#39;s preferences, or whether a proposed meeting time is an ideal time to have the meeting. A person can be stressed, overworked, tired, under pressure, or too busy to take a meeting despite having a free time slot. In such cases, the meeting may suffer due to the welfare of the invitee. Information regarding the welfare of the invitee is unavailable to the person who scheduled the meeting because current systems look only to the binary case of yes/no availability. Further, invitees may be unwilling or unable to openly discuss the invitees&#39; stress level and workload with others, especially in a time-pressured scenario. 
     Thus, a need exists for a calendar management system and method that allows a person to schedule a meeting with an invitee at an ideal time based on the welfare of the invitee, without enquiring too much into their personal life. 
     Referring to the drawings,  FIG. 1  depicts a block diagram of a calendar management system, in accordance with embodiments of the present invention. Embodiments of a calendar management system  100 , which may be described as a welfare-based calendar management system that takes an invitee&#39;s preferences and well-being into account to provide, suggest, display, or otherwise deliver a recommendation as to an availability of the invitee. The availability of the invitee as recommended by the calendar management system  100  may encompass both whether the invitee is available during a particular date and time, and whether the proposed meeting time is an optimal, ideal, recommended, efficient, convenient, etc. date and time for the invitee. Embodiments of the calendar management system  100  may provide a granular level of availability of the invitee, which can be represented as a score or numerical rating. The score may be determined by data collected by a plurality of sensors and input devices to provide a plurality of invitee metrics based on a plurality of factors related to the invitee and the invitee&#39;s behavior/actions. For instance, the series of invitee metrics may be based on a historical learning system or real-time data relating to a person&#39;s individual characteristics. The plurality of factors may be customized so that a different score or rating may be created depending on which factors are relevant or important given the type of meeting requested. In some embodiments, the calendar management system  100  may be a customizable system that may enable a user/third party to query an invitee&#39;s availability based on a granular system that considers, for creating a meeting, a current situation or welfare of the invitee, wherein the availability can be represented as a score determined by the system  100 . The third party meeting creator may be informed what the invitee(s) would prefer or would be more ideal/optimal when the invitee(s) is/are technically “available” (i.e. no conflicting meeting scheduled in same time slot) over multiple time slots. The third party meeting creator may be informed via various notifications and/or a color coded calendar, wherein a color is associated with a particular availability recommendation. The availability recommendation may be updated as the meeting approaches based on data being collected by the sensors and the input devices, in the event the provided availability recommendation changes based on events and circumstances surrounding the invitee. 
     Embodiment of calendar management system  100  may comprise one or more sensors  110   a,    110   b,    110   c,    110   d , . . .  110   n  (referred to collectively as “sensors  110 ”) communicatively coupled to a computing system  120  via an I/O interface  150  and/or over a network  107 . For instance, some or all of the sensors  110  may be connected via an I/O interface  150  to computer system  120 . The number of sensors  110  connecting to computer system  120  via data bus lines  155   a,    155   b  (referred to collectively as “data bus lines  155 ) and/or over network  107  may vary from embodiment to embodiment, depending on the number of sensors  110  present in the calendar management system  100 . The reference numbers with sub-letters and/or ellipses, for example describing sensors as  110   a,    110   b ,  110   c,    110   d  . . .  110   n  or the data bus lines as  155   a,    155   b , may signify that the embodiments are not limited only to the amount of elements actually shown in the drawings, but rather, the ellipses between the letters and the n th  element indicate a variable number of similar elements of a similar type. For instance, with regard to the sensors  110  depicted in  FIG. 1 , any number of a plurality of sensors  110  may be present including sensor  110   a,  sensor  110   b,  and a plurality of additional sensors up to the n th  number of sensors  110   i  wherein the variable “n” may represent the last element in a sequence of similar elements shown in the drawing. 
     As shown in  FIG. 1 , a number of sensors  110  may transmit data about the invitee or invitee&#39;s actions (e.g. “invitee data”) received from the sensor  110  by connecting to computing system  120  via the data bus lines  155  to an  110  interface  150 . An  110  interface  150  may refer to any communication process performed between the computer system  120  and the environment outside of the computer system  120 , for example, the sensors  110 . Input to the computing system  120  may refer to the signals or instructions sent to the computing system  120 , for example the data collected by the sensors  110 , while output may refer to the signals sent out from the computer system  120  to the sensors  110 . 
     Some or all of the sensors  110  may transmit data about the invitee or invitee&#39;s actions (e.g. “invitee data”) received from the sensor  110  and/or input device  111  by connecting to computing system  120  over the network  107 . A network  107  may refer to a group of two or more computer systems linked together. Network  107  may be any type of computer network known by individuals skilled in the art. Examples of computer networks  107  may include a LAN, WAN, campus area networks (CAN), home area networks (HAN), metropolitan area networks (MAN), an enterprise network, cloud computing network (either physical or virtual) e.g. the Internet, a cellular communication network such as GSM or CDMA network or a mobile communications data network. The architecture of the computer network  107  may be a peer-to-peer network in some embodiments, wherein in other embodiments, the network  107  may be organized as a client/server architecture. 
     In some embodiments, the network  107  may further comprise, in addition to the computer system  120 , and sensors  110 , a connection to one or more network accessible knowledge bases containing information of one or more users, network repositories  114  or other systems connected to the network  107  that may be considered nodes of the network  107 . In some embodiments, where the computing system  120  or network repositories  114  allocate resources to be used by the other nodes of the network  107 , the computer system  120  and network repository  114  may be referred to as servers. 
     The network repository  114  may be a data collection area on the network  107  which may back up and save all the data transmitted back and forth between the nodes of the network  107 . For example, the network repository  114  may be a data center saving and cataloging invitee data sent by one or more of the sensors  110  to generate both historical and predictive reports regarding a particular invitee. In some embodiments, a data collection center housing the network repository  114  may include an analytic module capable of analyzing each piece of data. being stored by the network repository  114 . Further, the computer system  120  may be integrated with or as a part of the data collection center housing the network repository  114 . In some alternative embodiments, the network repository  114  may be a local repository (not shown) that is connected to the computer system  120 . 
     Referring still to  FIG. 1 , embodiments of the computing system  120  may receive the invitee data from one or more sensors  110  which may be positioned within an environment shared by the invitee, worn by the invitee, or otherwise disposed in a location that can result in obtaining invitee data. Sensors  110  may be a sensor, an input device, or any input mechanism. For example, sensor  110  may be a biometric sensor, a wearable sensor, an environmental sensor, a camera, a camcorder, a microphone, a peripheral device, a computing device, a mobile computing device, such as a smartphone or tablet, facial recognition sensor, voice capture device, and the like. Embodiments of sensors  110  may also include a heart rate monitor used to track a current or historical average heart rate of the invitee; wireless-enabled wearable technology, such as an activity tracker or smartwatch that tracks a heart rate, an activity level (e.g. number of calories burned, total steps in a day, etc , a quality of sleep, a diet, a number of calories burned; a robotic therapeutic sensor; a blood pressure monitor; a perspiration sensor; and other wearable sensor hardware. Embodiments of sensors  110  may further include environmental sensors either worn or placed in an invitee environment, such as an office or study, that can measure air quality, temperature, pressure, NO 2  levels, humidity, and the like, which may be helpful in suggesting a location of a meeting or to gauge a comfort level of an invitee. Further embodiments of sensor  110  not specifically listed herein may be utilized to collect data about the invitee or invitee behavior or conditions surrounding the invitee environment, 
     Further embodiments of sensors  110  may include one or more input devices or input mechanisms, including one or more cameras positioned proximate the invitee or within an environment shared by the invitee. The one or more cameras may capture image data or video data of an invitee, including a posture, facial expressions, perspiration, muscle activity, gestures, etc. Embodiments of the sensors  110  may also include one or more microphones positioned nearby the invitee to collect audio relating to the invitee, a keystroke logger that may measure a rate of typing, and other hardware input devices, such as an audio conversion device, digital camera or camcorder, voice recognition devices, graphics tablet, a webcam, VR equipment, mouse, touchpad, stylus, and the like, which may help gauge a work intensity or work output of an invitee. Further embodiments of sensors  110  may include a mobile computing device, such as a smartphone or tablet device, which may run various applications that contain data about the invitee. For example, an invitee&#39;s smartphone may include a sleep tracking application that may send sleep data to the computing system  120 , or may send relevant social media information to the computing system  120 . The mobile computing device as used as sensor may also utilize the device&#39;s camera, microphone, and other embedded sensors to send information to the computing system  120 . Moreover, embodiments of sensors  110  may encompass other input mechanisms, such as a user computer that may send information to the computing system  120 , wherein the user computer may be loaded with software programs that are designed to track a productivity or work output level. 
     Embodiments of the computer system  120  may he equipped with a memory device which may store the invitee data generated and transmitted as data by the sensors  110 . 
     Furthermore, embodiments of the one or more sensors  110  may be in communication with each other. The sensors  110  may interact with each other for collecting comprehensive, accurate, timely, and organized data, and sending to computing system  120 . A first sensor of the one or more sensors  110  may request help from another sensor of the one or more sensors  110  to confirm a condition of the invitee or a data result from the first sensor. For example, a facial recognition sensor may communicatively interact with a perspiration sensor to confirm whether the invitee is indeed sweating, and may additionally communicate with a thermal sensor to determine whether the invitee is possibly sweating based on a temperature of the invitee&#39; environment. Additionally, data received by the computing system  120  that is collected by a first sensor of the one or more sensors  110  may be dependent on another sensor of the one or more sensors  110 . For instance, a camera sensor for measuring a posture of the invitee may rely on pressure sensors located within the invitee&#39;s chair to send data on pressure points of the invitee&#39;s chair. Further, embodiments of the sensors  110  may be synchronized with each other to provide accurate and timely data in combination to the computing system  120 . As an example, a heart rate monitor worn by the invitee may be synchronized with the keystroke logger to cohesively report a work intensity of the invitee to the computing system  120 . Any sensor may communicate with the other sensors. The interactive communication between the sensors  110  may modify, update, augment, bolster, confirm, reference, etc. data received and/or collected by the sensor, as well as improve the accuracy and efficiency of the data. 
       FIG. 2  depicts a block diagram of a metrics module  131  of the calendar management system  100  of  FIG. 1 , in accordance with embodiments of the present invention. Embodiments of computer system  120  may include a metrics module  131 . A “module” may refer to a hardware based module, software based module or a module may be a combination of hardware and software. Embodiments of hardware based modules may include self-contained components such as chipsets, specialized circuitry and one or more memory devices, while a software-based module may be part of a program code or linked to the program code containing specific programmed instructions, which may be loaded in the memory device of the computer system  120 . A module (whether hardware, software, or a combination thereof) may be designed to implement or execute one or more particular functions or routines. 
     Embodiments of the metrics module  131  may include one or more components of hardware and/or software program code for receiving, analyzing, interpreting and reporting data based on the invitee data collected by the sensors  110 . Embodiments of the metrics module  131  may generate a series of invitee metrics based on a plurality of factors, including, preferences, tendencies, time since last meeting, rate of activity, general health, stress levels, work intensity, frustration levels, tiredness, work load, pain level, social status, personal/social activity schedule, general welfare, mental health, etc. The series of invitee metrics may be output as a numerical value, such as a metric score or rating. Moreover, embodiments of the metrics module  131  may include a profile module  131   a,  an analytics module  131   b,  and/or a reporting module  131   c.    
     Embodiments of the profile module  131   a  of the metrics module  131  may create, store and organize user profiles and may create and store data received by the computer system  120  from the sensors  110 , by associating the data with one or more fields. The profile module  1311   a  may create, store, and maintain profiles for users of the computing system  120  and/or may register identifying information about users of the computer system  120  as well as coworkers, clients, business contacts, and the like, who may frequently request meetings with or assign tasks to the user/invitee. For instance, offices or business equipped with or operating the calendar management system  100  may obtain personalized information of the invitee without the need for a third party to directly ask the invitee such invasive questions. 
     Embodiments of the metrics module  131  may also include an analytics module  131   b . Embodiments of the analytics module  131   b  may refer to configurations of hardware, software program code, or combinations of hardware and software programs, capable of analyzing data from one or more sensors  110  and applying one or more data models to discover, identify, interpret and communicate patterns or trends in the invitee data. The analytics module  131   b  may rely on applications of statistics, computer programming, and the like, of the data collected and received by the analytics module  131   b  in order to discover, interpret and report patterns in the invitee data. Embodiments of the analytics module  131   b  may receive invitee data from the sensors  110  and assist the generation of a plurality of invitee metrics based on customizable factors. In further embodiments, the analytics module  131   b  may receive the data from the sensors  110  and compare the data with other invitees using the calendar management system  100 , use the data to generate various reports, such as job performance reports, mental health reports, etc., and predict and/or track tendencies of the invitee. 
     In some embodiments, the metrics module  131  may also include a reporting module  131   c.  Embodiments of the reporting module  131   c  may be hardware, software programs loaded in a memory device or a combination of hardware components and software programs which may provide users, third parties looking to schedule the invitee, and remotely accessible computer systems with information and analytical results of the analytics module  131   b,  or the metrics module  131  generally. The reporting module  131   c  may output to the computer system  120  and/or other modules of the computing system  120  results, conclusions, raw data, data, figures, statistics, patterns, and the like, obtained from the sensors  110 , that can be used to develop a series of metrics of an invitee. 
     With continued reference to  FIG. 1 , embodiments of the computing system  120  of the calendar management system  100  may include a weighting module  132 . Embodiments of the weighting module  132  may include one or more components of hardware and/or software program code for assigning a weighting factor to the plurality of invitee metrics generated by the metrics module  131 . For example, the weighting module  132  may apply a weighting factor to the metric score generated by the metrics module  131  for a given factor. The weighting factor may be a numerical value applied to the metric score representing a key factor to determine a total score calculated by a scoring module  133 . The weighting factor may differ based on which key factors—as measured by the sensors  110 , are more meaningful or important to determining an optimal meeting time of an invitee. Exemplary key factors may include invitee preferences, tendencies, time since last meeting, rate of activity, general health, stress levels, work intensity, frustration levels, tiredness, work load, pain level, social status, personal/social activity schedule, general welfare, mental health, etc, As an example, the weighting module  132  may assign a weighting factor, expressed as a multiple of a “weighting” constant, as follows: preferences (weighting×1), tendencies (weighting×1), time since last meeting (weighting×3), rate of activity (weighting×3), general health (weighting×1), stress levels (weighting×1), work intensity (weighting×2), frustration levels (weighting×1), tiredness (weighting×work load (weighting×1), pain level (weighting×1), social status (weighting×1), personal/social activity schedule (weighting×1), general welfare (weighting×1), mental health (weighting×2). The determination of which factors may be used in determining what invitee metrics are provided, and the weighting factor for each factor may be configured and customized by the user, may be out-of-the-box default, may be selectable by the third party meeting creator, or may be automatically determined by the weighting module  133  based on information provided by meeting creator. 
     Moreover, the weighting module  132  may consider a type of meeting or task requested by a third party when determining a weighting factor to be assigned to a particular metric score associated with a particular factor. For example, if a third party would like to schedule an invitee for a direct customer engagement-type meeting, the selection of factors may be more relevant to a tiredness of the invitee and/or time since last meeting to allow for adequate preparation. In this example, the weighting factor may be higher for the metric score associated with key factors such as tiredness and time since last meeting. 
     Embodiments of the computing system  120  of the calendar management system  100  may include a scoring module  133 . Embodiments of the scoring module  133  may include one or more components of hardware and/or software program code for calculating a total score, or total metric score. The scoring module  133  may first calculate a weighted metric score for each factor, and then may calculate a total score to be used by the recommendation module  134  of the computing system  120 . The total score may be represented by a numerical value, which may be the aggregate or sum of all of the weighted metric scores based on each of the plurality of factors. Accordingly, an availability of the user may be outputted as a score or rating by the scoring module  133  (e.g., based on the total score) to be used by the recommendation module  134  to provide an availability recommendation. 
     With continued reference to  FIG. 1 , embodiments of the computing system  120  of the calendar management system  100  may include a recommendation module  134 . Embodiments of the recommendation module  134  may include one or more components of hardware and/or software program code for providing a recommendation as to an availability of the invitee. Embodiments of the recommendation module  134  may compare the total score calculated by the scoring module  133  with a plurality of predetermined score thresholds. The plurality of predetermined score thresholds may be a range of numerical values associated with a particular recommendation as to the availability of the invitee that takes into account the plurality of key factors as measured by the sensors  110 . Exemplary score thresholds may be associated with an optimal availability, a sub-optimal availability, a not recommended but available availability, and an unavailable availability. The recommendation module  134  may respond to a third party query or request to schedule a meeting with a recommendation, suggestion, conclusion, etc. as to an availability or an ideal/optimal availability of the invitee. Moreover, embodiments of the recommendation module  134  may associate the recommendations with a color, wherein each recommendation that indicates a particular score threshold may have a unique color. Thus, a third party may view a color coded invitee schedule, wherein open time slots may be color coded based on characteristics of the invitee to indicate whether a particular time slot is preferred or more optimal than others. 
     Embodiments of the computing system  120  of the calendar management system  100  may further include a real-time update module  135 . Embodiments of the real-time update module  135  may include one or more components of hardware and/or software program code for performing a live data reinforcement of the availability recommendation provided by the recommendation module  134 . For example, prior to the accepted meeting, the real-time update module  135  may determine if the availability recommendation has changed or has been affected. Certain factors may affect or change the weighted metric scores for one or more factors, which can change the total score. The real-time update module  135  may determine if such a change has occurred and may either confirm the initial availability recommendation, or may determine that a new total score now exceeds the current predetermined score threshold which changes the recommendation. The real-time update module  135  may notify the third party of the change, and may provide a new recommendation. 
     Embodiments of the computing system  120  of the calendar management system  100  may include a calendar module  141  and a task module  142 . The calendar module  141  and the task module  142  may include one or more components of hardware and/or software program code for performing normal calendar and task operations and functions. Furthermore, various tasks and specific functions of the modules of the computing system  120  may be performed by additional modules, or may be combined into other module(s) to reduce the number of modules. 
     Referring now to  FIG. 3 , which depicts a flow chart of a method  200  for determining an availability recommendation, in accordance with embodiments of the present invention. One embodiment of a method  200  or algorithm that may be implemented for determining an availability recommendation in accordance with the calendar management system  100  described in  FIGS. 1-2  using one or more computer systems as defined generically in  FIG. 6  below, and more specifically by the specific embodiments of  FIGS. 1-2 . 
     Embodiments of the method  200  for determining an availability recommendation may begin at step  201  wherein a plurality of factors, which may be customizable, are configured and/or selected, unless such factors are designated as default settings In some embodiments, the factors are not configured or are only suggested factors until a third party meeting creator selects the factors. The plurality of factors may relate to an invitee or an invitee&#39;s actions or welfare. Exemplary factors may include invitee preferences, tendencies, time since last meeting, rate of activity, general health, stress levels, work intensity, frustration levels, tiredness, work load, pain level, social status, personal/social activity schedule, general welfare, mental health, etc. The invitee may decide which factors should be taken into account when generating a plurality of metrics based on the factors. Alternatively, a third party, such as a meeting creator, may select which factors to be considered, which may be useful because the meeting creator knows which type of meeting is sought. 
     Data is received by computing system  120  from the sensors HO over network  107  or via I/O interface  150  at step  202 . The sensors  110  may continuously collect data and transmit the data to the computing system  120 , or may transmit data in response to a query or request by a. third party. The various types of sensors  110  may provide invitee data used to determine a plurality of invitee metrics, wherein the invitee metrics are based on the plurality of selected factors, which may occur at step  203 . 
     At step  204 , a query may be received from a third party. Step  204  may be performed at any time before or after step  205 . For example, a third party query may be a formal request, or may be accessing and viewing, by the third party, the invitee&#39;s schedule looking for an ideal time to request a meeting or assign a task. The query may further involve receiving information from the third party, such as meeting type, location, time, duration, required deliverables, and the like. In some embodiments, the third party query may also include a selection of key factors to be used to return specific, customized invitee metrics. For example, the method  200  may utilize a customized system to enable a third party user to query an invitee&#39;s availability based on a granular system based on the invitee&#39;s current situation and/or overall welfare. Even if a third party query is not received, method  200  may still perform the steps to provide an availability recommendation for each time slot of an invitee&#39;s schedule. 
     At step  205 , a weighting factor is assigned to the invitee metrics. The weighting factor may be applied to the metric score for each factor configured in step  201  and/or selected by the third party as part of the third party query. The weighting factor may be applied after a query is received from a third party, or may be preset by the user prior to receiving a query, or selected by the third party requester at the time of formulating a query. The weighting factor may also be assigned as a function of the type of meeting requested by the third party. The weighting factor may vary for each factor depending on which factor(s)/metric(s) are more important or relevant to determining availability of the invitee, which may result in more customized and relevant scores to determine ideal meeting times and availability of the invitee. The weighting factor may be a numerical value that can multiply the metric score for each factor. 
     A total metric score is calculated at step  206 , given the weighted metric scores for each key factor. Each of the weighted metric scores may be totaled so that the availability of the user is represented as a score or rating. The total score may be the sum or aggregate of the weighted metric scores for each factor, for each time of a plurality of specified times of the day. Based on the calculated total score, the method  200  provides a recommendation as to the availability of the invitee in step  207 . The recommendation may then be delivered to the third party requester as a response to the third party query, or output as a color coded schedule accessible by third parties. 
       FIG. 4  depicts a flow chart of step  207  of the method of  FIG. 3  for providing an availability recommendation based on a total score, in accordance with embodiments of the present invention. At step  301 , the total score is calculated as described above. To determine a recommendation as to the availability of the invitee, a comparison may be made between the total score and a plurality of predetermined score thresholds. The plurality of predetermined score thresholds may be a range of numerical values associated with a particular recommendation as to the availability of the invitee that takes into account the plurality of key factors as measured by the sensors  110 . Exemplary score thresholds may be associated with an optimal availability, a sub-optimal availability, a not recommended but available availability, and an unavailable availability. Thus, at step  302  determines whether the total score exceeds an “optimal” score threshold. If the total score does not exceed the optimal score threshold, then step  303  determines that the invitee is not only available, but the proposed meeting time is optimal, If the total score does exceed the “optimal” score threshold, then step  304  determines whether the total score exceeds a “sub-optimal” score threshold. If the total score does not exceed the sub-optimal threshold, the invitee is available, then step  305  determines that the proposed meeting time is sub-optimal. If the total score does exceed the “sub-optimal” score threshold, then step  306  determines whether the total score exceeds a “not recommended” score threshold. If the total score does not exceed the “not recommended” threshold, then step  307  determines that the invitee is available, but the proposed meeting time is not recommended. If the total score exceeds the “not recommended” threshold, then step  308  determines that the invitee is unavailable. It should be understood that additional or fewer predetermined score thresholds may be used to further define an availability of the invitee. 
     Referring back to  FIG. 3 , embodiments of method  200  may include a step  208  of performing a real-time update to the recommended availability of the invitee, The real-time update step  208  may serve as live-data reinforcement of the availability recommendation provided at step  207 . For example, prior to the accepted meeting, method  200  may determine if the availability recommendation has changed or has been affected since the proposed meeting time has been accepted by the invitee. Performing this step may either confirm the initial availability recommendation, or may determine that a new total score now exceeds the current predetermined score threshold which changes the recommendation. Step  209  determines whether a notification should be sent to the third party meeting creator (and other attendees) regarding a change in the availability recommendation provided in step  207 . 
       FIG. 5  depicts a flow chart of step  208  of the method  100  of FIG,  3  for performing an update to the availability recommendation, in accordance with embodiments of the present invention. At step  401 , an availability recommendation has initially been provided/determined, and a meeting has been scheduled with the invitee. Step  402  analyzes real-time invitee metrics prior to the meeting, which are supplied via the sensors  110 . Step  403  calculates a new total score by totaling the updated weighted scores. Step  404  determines whether the new total score is different (or has changed) from the initial total score. If the total score has not changed, then the method  200  may continue to analyze the real-time invitee metrics by returning to step  402 . In some embodiments, step  402  is performed at least once prior to the meeting, or at predetermined times before the meeting (e.g. 3 days before meeting, 24 hours before meeting, hour before meeting, etc.). In further embodiments, the method  200  may continuously perform the updating based on new data collected by the sensors  110 . 
     If the total score has changed, step  405  determines whether the new total score changes or affects the previous availability recommendation. If the total score has not changed or affected the previous availability recommendation, then the method  200  may continue to analyze the real-time invitee metrics by returning to step  402 . However, if the new total score representing the invitee&#39;s availability changes or affects the availability recommendation, then step  406  determines that step  207  in  FIG. 3  may be repeated. At step  407  (similar to step  FIG. 3 ) the third party may be notified of the change in the recommendation, and may provide a new availability recommendation. 
     The following scenario is described for exemplary purposes to show an embodiment of the implementation of method  200 :
         An executive would like to schedule a project manager for 11:00 AM on Tuesday of the following week to deliver a progress report to a customer. The project manager has a meeting from 9:00 AM to 10:45 AM on the Tuesday. The executive views the project manager&#39;s schedule, which is color coded to indicate a recommended availability based on default selections of various key factors. Because the executive needs the project manager to be fully alert and charismatic in front of the customer, the executive selects the following factors—time since last meeting, rate of activity, and tiredness. Because the project manager must deliver a progress report with enthusiasm to the customer, a weighting factor is applied to each of the factors as follows—time since last meeting (×2), rate of activity (×1), and tiredness (×3). A high metric score of 7 is determined for time since last meeting because this leaves only fifteen minutes between meetings, and the project manager likely will have little time to switch contexts. The weighting factor is applied as follows (7)(×2)=14 as the weighted metric score for this factor. The system has determined that last week&#39;s rate of activity (e.g. typing rate and stress level) has been lower than usual, indicating that the project manager is likely to continue being a little more relaxed next week as well. Thus, the metric score for the rate of activity factor is a 3. The weighting factor is applied (3)(×1)=3 as the weighted metric score for this factor. The system has also determined that towards the beginning of the week, a sleeping/alertness application on the project manager&#39;s smartphone indicates that he has been getting adequate sleep, and historically is well-rested on Tuesday mornings. Therefore, the metric score is 0. The weighting factor is applied: (0)(×3)=0. The total score is calculated by adding up each weighted metric score for each factor: 14+3+0=17. The total score is compared with the following predetermined score thresholds and associated recommendations:   0-10—Available/Optimal   11-20—Available/Sub-Optimal   21-30—Available/Not Recommended   31+—Unavailable.       

     The total score is 17, which exceeds the predetermined score threshold for optimal (10), but does not exceed the predetermined score threshold for the sub-optimal range (20). Thus, the determination is that the proposed meeting time is a sub-optimal time to schedule a meeting with the invitee, even though he is available. Despite the sub-optimal warning, the executive schedules the meeting with the project manager. 
     On the day of the meeting (Tuesday), the project manager receives a call at 8:00 AM that a critical situation involving a Problem Management Report (PMR) has come up. As the project manager works hard to resolve the PMR, the sensors (e.g. heart rate monitor, sleep application on mobile phone, and key stroke logger) report the data to the calendar management system, and new values for the metrics associated with tiredness and rate of activity are now higher than usual. The new metric score for rate of activity is 7. The weighting factor is applied to determine the weighted metric score for rate of activity: (7)(×1)=7. The new metric score for tiredness is now a 3. The weighting factor is applied to determine the new weighted score for tiredness: (3)(×3)=9, Time since last meeting weighted score remains unchanged at 14. The total score is calculated by adding up each weighted metric score for each factor: 14+7+9=30. The total score is compared with the following predetermined score thresholds:
         0-10—Available/Optimal   11-20—Available/Sub-Optimal   21-30—Available/Not Recommended   31+—Unavailable.       

     The new total score is 30, which exceeds the predetermined score threshold for optimal (10) and sub-optimal (20), but does not exceed the predetermined score threshold for the available/not recommended range (30). Thus, the determination is that the project manager is available, but the previously accepted meeting time is now not recommended, even though the project manager is available. This change in recommendation is forwarded to the executive, and the executive cancels the meeting and initiates a new query to schedule a meeting on a different day and time due to the importance that the project manager be well rested, prepared, and not overworked for the meeting. 
     Accordingly, embodiments of method  200  may provide a granular level availability, which is represented by a score, which also allows for a customized list of factors about the invitee or invitee&#39;s behavior that may lead to a different score. This may be referred to as a non-binary system and method because the method  200  determines not only whether the invitee is available or not available, but also provides a recommendation on whether the proposed meeting time is optimal, sub-optimal, etc. based upon a plurality of factors, such as the welfare of the invitee. The availability recommendations based on the total score may be displayed as a color coded schedule accessible by others so that meeting creators may view the invitee&#39;s schedule and know what meeting times are recommended and which ones are not, without having to ask the invitee invasive and personal health related questions. 
       FIG. 6  illustrates a block diagram of a computer system  500  that may be included in the system of  FIGS. 1-2  and for implementing the methods of  FIGS. 3-5  in accordance with the embodiments of the present disclosure. The computer system  500  may generally comprise a processor  591 , an input device  592  coupled to the processor  591 , an output device  593  coupled to the processor  591 , and memory devices  594  and  595  each coupled to the processor  591 . The input device  592 , output device  593  and memory devices  594 ,  595  may each be coupled to the processor  591  via a bus. Processor  591  may perform computations and control the functions of computer  500 , including executing instructions included in the computer code  597  for the tools and programs capable of implementing a method for determining an availability recommendation, in the manner prescribed by the embodiments of  FIGS. 3-5  using the calendar management system  FIGS. 4-5 , wherein the instructions of the computer code  597  may be executed by processor  591  via memory device  595 . The computer code  597  may include software or program instructions that may implement one or more algorithms for implementing the methods of providing a recommendation as to an availability of an invitee, as described in detail above. The processor  591  executes the computer code  597 . Processor  591  may include a single processing unit, or may be distributed across one or more processing units in one or more locations (e.g., on a client and server). 
     The memory device  594  may include input data  596 . The input data  596  includes any inputs required by the computer code  597 . The output device  593  displays output from the computer code  597 . Either or both memory devices  594  and  595  may be used as a computer usable storage medium (or program storage device) having a computer readable program embodied therein and/or having other data stored therein, wherein the computer readable program comprises the computer code  597 . Generally, a computer program product (or, alternatively, an article of manufacture) of the computer system  500  may comprise said computer usable storage medium (or said program storage device). 
     Memory devices  594 ,  595  include any known computer readable storage medium, including those described in detail below. In one embodiment, cache memory elements of memory devices  594 ,  595  may provide temporary storage of at least some program code (e.g., computer code  597 ) in order to reduce the number of times code must be retrieved from bulk storage while instructions of the computer code  597  are executed. Moreover, similar to processor  591 , memory devices  594 ,  595  may reside at a single physical location, including one or more types of data storage, or be distributed across a plurality of physical systems in various forms. Further, memory devices  594 ,  595  can include data distributed across, for example, a local area network (LAN) or a wide area network (WAN). Further, memory devices  594 ,  595  may include an operating system (not shown) and may include other systems not shown in  FIG. 6 . 
     In some embodiments, the computer system  500  may further be coupled to an Input/output ( 110 ) interface and a computer data storage unit. An I/O interface may include any system for exchanging information to or from an input device  592  or output device  593 . The input device  592  may be, inter alia, a keyboard, a mouse, etc. or in some embodiments the sensors  110 . The output device  593  may be, inter alia, a printer, a plotter, a display device (such as a computer screen), a magnetic tape, a removable hard disk, a floppy disk, etc. The memory devices  594  and  595  may be, inter alia, a hard disk, a floppy disk, a magnetic tape, an optical storage such as a compact disc (CD) or a digital video disc (DVD), a dynamic random access memory (DRAM), a read-only memory (ROM), etc. The bus may provide a communication link between each of the components in computer  500 , and may include any type of transmission link, including electrical, optical, wireless, etc. 
     An I/O interface may allow computer system  500  to store information (e.g., data or program instructions such as program code  597 ) on and retrieve the information from computer data storage unit (not shown). Computer data storage unit includes a known computer-readable storage medium, which is described below. In one embodiment, computer data storage unit may be a non-volatile data storage device, such as a magnetic disk drive (i.e., hard disk drive) or an optical disc drive (e.g., a CD-ROM drive which receives a CD-ROM disk). In other embodiments, the data storage unit may include a knowledge base or data repository  125  as shown in  FIG. 1 . 
     As will be appreciated by one skilled in the art, in a first embodiment, the present invention may be a method; in a second embodiment, the present invention may be a system; and in a third embodiment, the present invention may be a computer program product. Any of the components of the embodiments of the present invention can be deployed, managed, serviced, etc. by a service provider that offers to deploy or integrate computing infrastructure with respect to calendar management systems and methods. Thus, an embodiment of the present invention discloses a process for supporting computer infrastructure, where the process includes providing at least one support service for at least one of integrating, hosting, maintaining and deploying computer-readable code (e.g., program code  597 ) in a computer system (e.g., computer  500 ) including one or more processor(s)  591 , wherein the processor(s) carry out instructions contained in the computer code  597  causing the computer system to provide an availability recommendation using a plurality of metrics of an invitee based on a plurality of factors. Another embodiment discloses a process for supporting computer infrastructure. There the process includes integrating computer-readable program code into a computer system including a processor. 
     The step of integrating includes storing the program code in a computer-readable storage device of the computer system through use of the processor. The program code, upon being executed by the processor, implements a method of providing an availability recommendation. Thus, the present invention discloses a process for supporting, deploying and/or integrating computer infrastructure, integrating, hosting, maintaining, and deploying computer-readable code into the computer system  500 , wherein the code in combination with the computer system  500  is capable of performing a method for providing an availability recommendation. 
     A computer program product of the present invention comprises one or more computer readable hardware storage devices having computer readable program code stored therein, said program code containing instructions executable by one or more processors of a computer system to implement the methods of the present invention. 
     A computer system of the present invention comprises one or more processors, one or more memories, and one or more computer readable hardware storage devices, said one or more hardware storage devices containing program code executable by the one or more processors via the one or more memories to implement the methods of the present invention. 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing, device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer nay be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention e described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     It is to be understood 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 devices through a thin client interface such as a web browser 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 Service (PaaS): the capability provided to the consumer is to 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 that includes a network of interconnected nodes. 
     Referring now to  FIG. 7 , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  includes one or more cloud computing nodes  10  with which local computing devices 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 device. It is understood that the types of computing devices  54 A,  54 B,  54 C and  54 N shown in  FIG. 7  are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device 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  (see  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 devices  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 include 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 calendar management for determining availability of an invitee  96 . 
     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