JP2012043056A - Information processor and information processing program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an information processor configured to output a date or date/time suited for an activity to be performed in a region according to the number of persons being present in the object region in the future date or date/time.SOLUTION: Reception means of this information processor receives type information indicating a type of the scheduled activity; number-of-persons estimation means estimates the number of persons being present in the object region in the future date or date/time based on behavior information, or records of past behavior of the respective persons; output means outputs, when the type of information indicated by the type information received by the reception means is the activity to be performed in the region, the date or the date/time when the number of persons estimated by the number-of-persons estimation means is more than the predetermined threshold, as the date or date/time suited for performing the activity.

Description

  The present invention relates to an information processing apparatus and an information processing program.

  Patent Document 1 describes the attributes of members in offices such as titles, with the goal of making it possible to negotiate changes and cancellations of previously reserved schedules when adjusting the schedule. By using the user evaluation knowledge, it has a bargaining means for negotiating new jobs and job changes with other users, thereby adjusting the circumstances of individuals and groups. Furthermore, the negotiation means can make adjustments such as responding to negotiations when the convenience of conference participants other than itself has already been adjusted, by repeatedly negotiating with the negotiating agent. In addition, each agent is inquired about the schedule, that is, the negotiating means selects the negotiation destination and the content of the negotiation using the assertion evaluation means that calculates the ease of negotiation, which means the ease of negotiation from this answer, so-called It is disclosed that adjustments based on rooting can be made.

  Further, in Patent Document 2, it is an object to plan a user's schedule in consideration of the user's activity pattern and various items related to the activity, and to provide a travel route according to the planned schedule. The generating device analyzes the input information and extracts an information collection unit that extracts work to be added to the first schedule, and pattern recognition that extracts a user's behavior pattern by referring to a past movement route and past work information A second schedule is generated by applying the behavior pattern to at least one work included in the first schedule and the extracted work, and a position of each work included in the second schedule A first movement path between work places where the work is performed is generated with reference to the information and time information, and the time required to move between the work places and the time actually required to perform the work are referred to. Separate Work is disclosed that includes a generator for generating a second feed line for to generate the third schedule added to perform the tasks included in about the third day.

  Further, Patent Document 3 has an object of providing a movement information presentation device, a schedule management system, and a movement information presentation program that can easily determine movement information. Acquire event conditions such as location, refer to route information of transportation, determine the means of transportation from the departure point to the destination, refer to the selection history, acquire the time characteristics of the user, and Referring to the information, the departure time of the departure place and the arrival time of the destination when using the means of transportation are set according to the acquired time characteristics so that the arrival time is before the start time of the event. It is disclosed to determine, output the determined movement schedule candidates, obtain the movement schedule selected by the user, and register the time characteristics of the selected movement schedule in the selection history There.

  In addition, in Patent Document 4, each participant has a time zone in which a regular schedule such as a meeting can be executed without actually having each participant gather or making individual contacts and adjustments. In a schedule adjustment device that presents candidates for a time zone for holding a conference for participants of a conference that is regularly executed, the task is to provide a schedule adjustment device that can be recognized in advance by a person, A recording unit for storing each action information indicating an action related to a confirmed schedule in each participant, an operation unit used to input conference specifications, each schedule corresponding to each participant scheduled, A CPU that generates a scheduled frequency table or the like indicating a time zone having a possibility of holding a meeting using the input holding specifications and extracts candidates from the generated scheduled frequency table or the like A display for notifying the extracted candidate, be provided with disclosed.

JP 10-207953 A JP 2008-171423 A JP 2009-98781 A JP 2009-116781 A

  The present invention relates to an information processing apparatus and an information processing program for outputting a date or date suitable for performing an activity performed in an area according to the number of persons in the target area on a future date or date and time. The purpose is to provide.

The gist of the present invention for achieving the object lies in the inventions of the following items.
The invention of claim 1 includes a receiving means for receiving type information indicating a type of an activity to be scheduled, and action information that is a record of each person's past action, and within a target area on a future date or date and time. In the case of an activity in which the type of activity indicated by the type information received by the receiving unit and the type information received by the receiving unit is an activity performed within a region, the number of people predicted by the number of people predicting unit is a predetermined threshold value An information processing apparatus comprising output means for outputting more days or dates as dates or dates suitable for performing the activity.

  According to a second aspect of the present invention, in the case where the activity type indicated by the type information received by the receiving unit is an activity performed outside the area, the number of people predicted by the number of people predicting unit is predetermined. The information processing apparatus according to claim 1, wherein a date or date and time less than a threshold value is output as a date or date and time suitable for performing the activity.

  According to a third aspect of the present invention, when the type of activity indicated by the type information received by the receiving unit is an activity performed by participation of a plurality of persons, the output unit records a past action of the person who participates. Is compared with information transmission information that is a record of the person's past information transmission, predicts the date or date when the person can participate in the activity, and performs the activity on the predicted date or date The information processing apparatus according to claim 1, wherein the information is output as a suitable date or date.

  The invention of claim 4 is a past performance, the time when the activity is extended, the participant information indicating the participant of the activity, the type information indicating the type of the activity, and the restriction that the activity is extended Past record information storage means for storing extended record information corresponding to the extension constraint information indicating the presence or absence of a factor to perform, a person who is scheduled to participate in the first activity, and the start time of the first activity The past performance information on the elapsed time from the end time of the second activity before the second activity, the person who participates in the second activity, the type of the second activity, and the presence or absence of the extension restriction of the second activity And a calculating means for calculating the number of persons who cannot attend at the start time of the first activity due to the extension of the second activity, based on the extension result information stored in the storage means. The calculation means is received by the reception means. The number of persons who cannot attend at the start time of the first activity is calculated using the activity of the type information as the first activity, and the output means determines in advance the number of people calculated by the calculation. The information processing apparatus according to claim 3, wherein when the number of persons is larger than the number of persons, the information indicating that the schedule of the activity is to be changed is output.

  The invention of claim 5 is a past performance, the time when the activity is extended, the participant information indicating the participant of the activity, the type information indicating the type of the activity, and the restriction that the activity is extended Past record information storage means for storing extended record information corresponding to extension constraint information indicating the presence or absence of a factor to perform, participant information indicating a person who is scheduled to participate in the first target activity, and the first The accepting means for accepting the start time of the activity, and the person who is scheduled to participate in the first activity indicated by the participant information accepted by the accepting means is before the start time of the first activity It is stored in the past performance information storage means for the elapsed time from the end time in the second activity, the person who participates in the second activity, the type of the second activity, and the presence or absence of the extension restriction of the second activity. Based on the extended performance information Calculated by the calculating means based on the number of persons who are unable to attend at the start time of the first activity due to the extension of the activity, and the number of persons scheduled to participate in the first activity An information processing apparatus comprising: a participant number calculating means for calculating the number of persons who can participate in the first activity by reducing the number of persons.

  The invention according to claim 6 sets the computer as a target at a future date or time based on accepting means for accepting type information indicating the type of activity to be scheduled and behavior information that is a record of past behavior of each person. In the case where the activity type indicated by the type information received by the receiving unit and the type of activity indicated by the type information received by the receiving unit are activities performed in the region, the number of people predicted by the number of people predicting unit is determined in advance. It is an information processing program for functioning as an output means for outputting a date or date and time that is greater than a set threshold value as a date or date and time suitable for performing the activity.

  According to the seventh aspect of the present invention, the computer is a past performance, the time when the activity is extended, the participant information indicating the participant of the activity, the type information indicating the type of the activity, and the activity is extended. Past record information storage means for storing extended record information corresponding to extension constraint information indicating the presence or absence of a factor that restricts this, participant information indicating a person who is scheduled to participate in the first activity of interest, and An accepting means for accepting a start time of the first activity, and a person who is scheduled to participate in the first activity indicated by the participant information accepted by the accepting means, and the start time of the first activity The past performance information storage means for the elapsed time from the end time of the previous second activity, the person who participates in the second activity, the type of the second activity, and the presence or absence of the extension restriction of the second activity To the extension record information memorized in Accordingly, a calculation means for calculating the number of persons who cannot attend at the start time of the first activity due to the extension of the second activity, and the number of persons scheduled to participate in the first activity. An information processing program for functioning as a participant number calculating means for calculating the number of persons who can participate in the first activity by reducing the number of persons calculated by the calculating means.

  According to the information processing apparatus of claim 1, it is possible to output a date or date suitable for performing an activity performed in the area according to the number of persons in the target area on a future date or date and time. .

  According to the information processing apparatus of claim 2, it is possible to output a date or date suitable for performing an activity performed outside the area according to the number of persons in the target area on a future date or date and time. .

  According to the information processing apparatus of claim 3, the behavior information that is a record of the past behavior of the person who participates in the activity is compared with the information transmission information that is a record of the past information transmission of the person, and the person It is possible to predict the date or date when the activity can be participated.

  According to the information processing apparatus of claim 4, the second activity is extended even though the second activity before the start time of the first activity may be extended. No, the number of persons who can participate in the first activity is calculated, and it is possible to prevent outputting a date or date and time that is not suitable for performing the first activity.

  According to the information processing apparatus of claim 5, the second activity is extended even though the second activity before the start time of the first activity may be extended. It is possible to prevent the number of persons who can participate in the first activity from being calculated.

  According to the information processing program of claim 6, it is possible to output a date or date suitable for performing an activity performed in the area according to the number of persons in the target area on a future date or date and time. .

  According to the information processing program of claim 7, the second activity is extended even though the second activity before the start time of the first activity may be extended. It is possible to prevent the number of persons who can participate in the first activity from being calculated.

It is a conceptual module block diagram about the structural example of 1st Embodiment. It is explanatory drawing which shows the example by which activity was extended. It is explanatory drawing which shows the example of a data structure of an extension performance data table. It is a flowchart which shows the production | generation process example of the extension performance data by the prospective attendee reachability prediction module of 1st Embodiment. It is a conceptual module block diagram about the structural example of 2nd Embodiment. It is explanatory drawing which shows the system configuration example at the time of embodying 2nd Embodiment. It is explanatory drawing which shows the example of a data structure of an indoor action log table. It is explanatory drawing which shows the data structure example of a communication log table. It is explanatory drawing which shows the example of a data structure of a schedule data table. It is explanatory drawing which shows the example of a data structure of a determination result table. It is explanatory drawing which shows the example of a data structure of a prediction result table. It is a flowchart which shows the production process example of the performance data by 2nd Embodiment. It is explanatory drawing which shows the example of a data structure of a predicted number of employees table. It is a flowchart which shows the example of a process by the in-company number prediction module of 2nd Embodiment. It is explanatory drawing which shows the example of a data structure of a schedule empty possibility table. It is a flowchart which shows the process example by the meeting attendance prospective reachability prediction module of 2nd Embodiment. It is explanatory drawing which shows the process example by the meeting attendance prospective reachability prediction module of 2nd Embodiment. It is a flowchart which shows the process example by the schedule adjustment module of 2nd Embodiment. It is a flowchart which shows the process example by the schedule adjustment module of 2nd Embodiment. It is a flowchart which shows the process example by the schedule adjustment module of 2nd Embodiment. It is a flowchart which shows the process example by the schedule adjustment module of 2nd Embodiment. It is a flowchart which shows the process example by the schedule adjustment module of 2nd Embodiment. It is explanatory drawing which shows the process example by the schedule request reception module of 2nd Embodiment, and a schedule adjustment result notification module. It is explanatory drawing which shows the process example by the schedule request reception module of 2nd Embodiment, and a schedule adjustment result notification module. It is a flowchart which shows the process example by the schedule adjustment module of 2nd Embodiment. It is explanatory drawing which shows the process example by the schedule request reception module of 2nd Embodiment, and a schedule adjustment result notification module. It is a block diagram which shows the hardware structural example of the computer which implement | achieves this Embodiment.

Hereinafter, examples of various preferred embodiments for realizing the present invention will be described with reference to the drawings.
FIG. 1 is a conceptual module configuration diagram of a configuration example according to the first embodiment.
The module generally refers to components such as software (computer program) and hardware that can be logically separated. Therefore, the module in the present embodiment indicates not only a module in a computer program but also a module in a hardware configuration. Therefore, the present embodiment is a computer program for causing these modules to function (a program for causing a computer to execute each procedure, a program for causing a computer to function as each means, and a function for each computer. This also serves as an explanation of the program and system and method for realizing the above. However, for the sake of explanation, the words “store”, “store”, and equivalents thereof are used. However, when the embodiment is a computer program, these words are stored in a storage device or stored in memory. It is the control to be stored in the device. Modules may correspond to functions one-to-one, but in mounting, one module may be configured by one program, or a plurality of modules may be configured by one program, and conversely, one module May be composed of a plurality of programs. The plurality of modules may be executed by one computer, or one module may be executed by a plurality of computers in a distributed or parallel environment. Note that one module may include other modules. Hereinafter, “connection” is used not only for physical connection but also for logical connection (data exchange, instruction, reference relationship between data, etc.). “Predetermined” means that the process is determined before the target process, and not only before the process according to this embodiment starts but also after the process according to this embodiment starts. In addition, if it is before the target processing, it is used in accordance with the situation / state at that time or with the intention to be decided according to the situation / state up to that point.
In addition, the system or device is configured by connecting a plurality of computers, hardware, devices, and the like by communication means such as a network (including one-to-one correspondence communication connection), etc., and one computer, hardware, device. The case where it implement | achieves by etc. is also included. “Apparatus” and “system” are used as synonymous terms. Of course, the “system” does not include a social “mechanism” (social system) that is an artificial arrangement.
In addition, when performing a plurality of processes in each module or in each module, the target information is read from the storage device for each process, and the processing result is written to the storage device after performing the processing. is there. Therefore, description of reading from the storage device before processing and writing to the storage device after processing may be omitted. Here, the storage device may include a hard disk, a RAM (Random Access Memory), an external storage medium, a storage device via a communication line, a register in a CPU (Central Processing Unit), and the like.

  The information processing apparatus 100 according to the present embodiment includes a prospective attendee reachability prediction module 150, a schedule adjustment module 160, a schedule reception module 170, and a schedule display module 180, and includes an indoor behavior log data storage module 110, The outdoor action log data storage module 120, the organization data storage module 130, and the schedule data / facility data storage module 140 are connected.

  The indoor behavior log data storage module 110 is connected to the attendee prospective reachability prediction module 150. The indoor action log data storage module 110 stores log data that is a record of past actions of each person, particularly log data that captures the results of indoor actions. For example, the indoor action log table 700 illustrated in FIG. 7 is stored. FIG. 7 is an explanatory diagram showing an example of the data structure of the indoor action log table 700. The indoor action log table 700 has a start time column 710, an end time column 720, an employee ID column 730, and an area column 740. The start time column 710 stores the date and time when the user in the employee ID column 730 enters the room in the area column 740. The end time column 720 stores the date and time when the user of the employee ID column 730 leaves the room of the area column 740. The employee ID column 730 stores user information indicating a target user. For example, an employee ID (IDentification). The area column 740 stores area information indicating the target area (location). For example, the conference room name. Users are called employees, people, participants, workers, etc., depending on the context.

The outdoor action log data storage module 120 is connected to the attendee reachability reachability prediction module 150. The outdoor action log data storage module 120 stores log data that captures the results of outdoor actions such as GPS data. For example, the location information (for example, longitude information and latitude information) detected by GPS or the like is stored together with the date and time information and personal information indicating the person. Further, as in the indoor behavior log table 700 illustrated in FIG. 7, the position information may be the name of the position, etc., and the date and time information may be the entry time and the entry to the position (for example, an area such as a room). It is good also as time.
The organization data storage module 130 is connected to the prospective attendee reachability prediction module 150. The organization data storage module 130 stores data describing the organization, job rank, and job type to which each individual belongs. For example, person information indicating each person, organization information indicating the organization to which the person belongs, position information indicating the position of the person, position information indicating the position of the person, and the like are stored in association with each other.

  The schedule data / facility data storage module 140 is connected to the prospective attendee reachability prediction module 150 and the schedule adjustment module 160. The schedule data / facility data storage module 140 stores data of a set schedule and a place profile (for example, the number of people accommodated in the conference room and facilities). For example, as schedule data, the person information indicating each person, the name of the activity scheduled by the person, the type of activity, the participant information indicating the person participating in the activity, the start time and end time of the activity, Memorize the places where activities are performed. For example, the schedule data table 900 illustrated in FIG. 9 is stored. FIG. 9 is an explanatory diagram showing an example of the data structure of the schedule data table 900. The schedule data table 900 has a start time column 910, an end time column 920, a location column 930, a conference name column 940, and an employee ID set column 950. The start time column 910 stores the start time of the activity. The end time column 920 stores the end time of the activity. The place column 930 stores place information indicating a place where the activity is performed. For example, the conference room name. The meeting name column 940 stores the name of the activity. The name of the activity may represent the type of the activity, or information indicating the type of activity may be stored. The employee ID set column 950 stores participant information indicating a person who participates in the activity. A plurality of participant information may be stored.

The prospective attendee reachability prediction module 150 is connected to the indoor behavior log data storage module 110, the outdoor behavior log data storage module 120, the organization data storage module 130, the schedule data / facility data storage module 140, and the schedule adjustment module 160. Yes. The prospective attendee reachability prediction module 150 predicts the number of people who can attend according to the schedule based on the indoor behavior log data and the schedule data. Update forecasts as circumstances change. Then, the schedule adjustment module 160 is notified each time it is updated. Also, outdoor action log data may be used together.
The attendee reachability prediction module 150 is a past performance, the time when the activity is extended, the participant information indicating the participant of the activity, the type information indicating the type of the activity, and the activity is extended. The extension record information corresponding to the extension constraint information indicating the presence or absence of a factor that restricts the storage is stored. Of course, this extended performance information is stored in a storage device accessible to the prospective attendee reachability prediction module 150. And the participant information which shows the person who plans to participate in the 1st activity made into object, and the start time of the 1st activity are received. And the person who is going to participate in the first activity indicated by the accepted participant information, the elapsed time from the end time in the second activity before the start time of the first activity, The second activity is extended by the extension of the second activity based on the extension result information regarding the person who participates in the second activity, the type of the second activity, and the presence or absence of the extension restriction of the second activity. The number of persons who cannot attend at the start time of 1 activity is calculated. Then, the number of persons who can participate in the first activity is calculated by subtracting the calculated number of persons from the number of persons scheduled to participate in the first activity.

In the calculation process in the prospective attendee reachability prediction module 150, the extended activity information in which at least one of the participating person, the type of activity, and the presence or absence of extension restrictions in the second activity is matched is acquired. In the extended record information, a probability value may be calculated in which the extended time is equal to or longer than the elapsed time from the end time of the second activity that is before the start time of the first activity.
Note that the participant information indicating the participant in the activity of the above-described extended performance information may be the responsible person information indicating the person in charge of the activity. The participant information may include responsible person information for distinguishing responsible person information indicating the responsible person of the activity.

  The schedule adjustment module 160 is connected to the schedule data / facility data storage module 140, the prospective attendee reachability prediction module 150, the schedule reception module 170, and the schedule display module 180. In response to the schedule setting request received by the schedule receiving module 170, the schedule adjustment module 160 uses the information obtained from the prospective attendee reachability prediction module 150 to ensure that the schedule setting is valid from the viewpoint of securing prospective attendees. Is evaluated and output to the schedule display module 180. When the predicted data is updated from the prospective attendee reachability prediction module 150, an adjustment plan is calculated from the viewpoint of securing the prospective attendee or the environmental load, and the relevant user is notified through the schedule display module 180. For example, if it is found that only two people are likely to come to a conference booked by eight people, another date or time that can be secured by a prospective attendee or a smaller conference at the same date and time Show the adjustment plan to change the location in the room.

The schedule reception module 170 is connected to the schedule adjustment module 160. The schedule reception module 170 receives a request regarding schedule setting based on a user's operation of a keyboard, a mouse, and the like.
The schedule display module 180 is connected to the schedule adjustment module 160. The schedule display module 180 displays (including notification) the output of the schedule adjustment module 160 to the user. For example, the information may be displayed on a display device provided in the information processing apparatus 100, or an e-mail may be transmitted to a user related to the schedule.

Processing performed by the prospective attendee reachability prediction module 150 will be described in detail.
The prospective attendee reachability prediction module 150 includes the schedule data in the schedule data / facility data storage module 140 and the indoor action log data storage module 110 or the outdoor action log data storage module 120 (indoor action log data storage module 110, outdoor action The action log data in one or both of the log data storage modules 120) is referred to, and the end time of the activity schedule included in the schedule data is compared with the actual detection result included in the action log data. If the activity is actually finished by the scheduled end time, the extension time is set to 0. If the activity is actually extended from the scheduled end time, the extended time is set as the extension time. "Actual data" is generated.

FIG. 2 is an explanatory diagram showing an example in which the activity is extended.
In the schedule data in the schedule data / facility data storage module 140, in a certain conference room, there are three participants in the conference (A, B, C, and the person responsible for the conference is A). Starting and scheduled until time t30. From the action log data in the indoor action log data storage module 110 or the outdoor action log data storage module 120, that is, from the action log data of A, B, C in the same conference room as the schedule data, the three Indicates that the meeting room was gathered at time t0, A was in the meeting room until time t35, and B and C were in the meeting room until time t34. In this case, the prospective attendee reachability prediction module 150 extracts 5 minutes as an extension time for A and 4 minutes as an extension time for B and C.

  The prospective attendee reachability prediction module 150 generates, for example, an extended record data table 300 as the extended record data. The extension result data table 300 is stored in a storage device that can be accessed by the prospective attendee reachability prediction module 150. FIG. 3 is an explanatory diagram showing an example of the data structure of the extension record data table 300. The extension record data table 300 has an extension time column 310, an employee ID column 320, an activity type column 330, and an extension restriction presence / absence column 340. The extension time column 310 stores the aforementioned extension time. The employee ID column 320 stores personal information (for example, an employee ID) indicating the target person. The activity type column 330 stores the type of activity performed by the person. For example, there are meetings, desk work, experiments, going out, etc. In the extension restriction presence / absence column 340, the presence / absence of restriction on the extension of the activity (for example, the place where the activity is performed (conference room in the above example) is used by another person within the predetermined period after the end time. If there is a plan to be recorded, etc., “Yes” is stored. The employee ID column 320 may store personal information indicating the person responsible for the implementation of the activity. The extended record data table 300 may include a column for storing personal information indicating the person responsible for the activity. Alternatively, the person may be provided with a field for storing responsible person corresponding information indicating whether the person corresponds to the person in charge of implementation. Thus, for example, for each combination of three data stored in the employee ID column 320, the activity type column 330, and the extension restriction presence / absence column 340, the extension time stored in the extension time column 310 is set to reach the prospective attendee. The possibility prediction module 150 may extract.

FIG. 4 is a flowchart illustrating an example of a process for generating extended performance data by the prospective attendee reachability prediction module 150 according to the first embodiment. Here, the scheduled activity is a schedule s.
In step S402, a set of unprocessed schedule data is substituted into S, and a set X of newly generated extended record data is made an empty set.
In step S404, it is determined whether or not the set S is empty. If the set S is empty, the process proceeds to step S420. Otherwise, the process proceeds to step S406.
In step S406, the element schedule s is extracted from the set S, and the schedule s is deleted from the set S.
In step S408, the schedule s determines whether or not a plurality of persons are involved. If a plurality of persons are involved, the process proceeds to step S418. Otherwise, the process proceeds to step S410.

In step S410, the person who executes the schedule s is set as the job execution manager m.
In step S412, the type k of the schedule s is obtained, the indoor action log corresponding to the type k is compared with the schedule s, and the extended time T is obtained.
In step S414, the presence or absence of a schedule setting subsequent to the schedule s is set as an extension constraint c.
In step S416, the extension record data <T, m, k, c> is added to the set X. Then, the process returns to step S404.
In step S418, the person in charge of the schedule s is checked against the organization information, and the task execution manager m is estimated. Then, the process proceeds to step S412.
In step S420, each element of the set X is added to the extended performance data set.

The prospective attendee reachability prediction module 150 uses the extended performance data to predict the number of people who can participate in the scheduled activity for the scheduled activity that will be set or has been set.
A process in which the prospective attendee reachability prediction module 150 determines an activity type to be stored in the activity type column 330 from the action log data in the indoor action log data storage module 110 or the outdoor action log data storage module 120 will be described. Correlate the place where the activity was performed with the activity type. For example, if activity log data is detected in a conference room, the activity type is conference, if there is detection in the seat, the activity type is desk work, if it is detected in the laboratory, the activity type is experiment, and the indoor activity log If there is no indoor detection in the data storage module 110, the activity type is set to go out.
In addition, when a plurality of people are involved in the activity, the person in charge of the activity estimates the person most likely to be the person responsible in the captured action log data. For example, the data in the organization data storage module 130 is used to extract the person who has the highest job rank or the person who is explicitly set as the facilitator on the schedule data in the schedule data / facility data storage module 140. .

ここで、Ｘは当該予定の活動に参加する人の集合、ｔは当該予定内の時刻、ＰｒｅＳｃｈｅｄｕｌｅ（Ｘ，ｔ）はＸの各参加者に予定されている時刻ｔに対する直近の先行予定の活動の集合、Ｋ（ｓ）は予定の活動ｓの種類、Ｔ_Ｅ（ｓ）は予定の活動ｓの終了時刻、Ｃ（ｓ）は予定の活動ｓの延長制約の有無、Ｍ（ｓ）は予定の活動ｓに参加する人の集合を表す。
（１）式の最も内側の点線で囲んだ矩形内の部分（ｍａｘの項）は、予定の活動ｓに複数の人が参加する場合、最も高い延長の確率値Ｐを採用することを示している。いわゆる悲観的に評価した場合である。
（１）式の２番目の点線で囲んだ矩形内の部分（シグマの項）は、時刻ｔに人の集合Ｘの予定を設定しようとした場合に、それら人の直近の先行予定が時刻ｔ（対象としている活動の開始時刻以降の時刻）まで延長される確率値Ｐの和は、時刻ｔの時点で遅れる人数の期待値となる。
（１）式の外側の点線で囲んだ矩形内の部分（（１）式そのもの）は、予定参加者数Ｘから遅れると見込まれる人数を減算すれば、予定通りに来られる人の人数を見積もれることを示している。 The attendee reachability prediction module 150 is a set of data (elapsed time from the end time of the activity scheduled to be preceded, participants, the type of activity scheduled to be preceded, and the extension constraint of the activity scheduled to be preceded) On the other hand, the probability P that the activity scheduled ahead is extended is defined based on the corresponding extension result data. For example, in an activity that is scheduled to be advanced, acquire extended record data that matches at least one of the participants, the type of activity, and whether or not there is an extension restriction, and the extended time in the acquired extended record data Then, a probability value P that is equal to or longer than the elapsed time from the end time in the activity that is scheduled to precede is calculated.
Using this probability P, when setting a certain scheduled activity, the sum of the probabilities P for the set of people participating in the scheduled schedule is the expected value of “number of people who are late for the scheduled scheduled activity start time” Represents. From this, the estimate of the number of people who can reach the schedule at a certain time in the schedule is given by the following equation (1).

Here, X is a set of people who participate in the scheduled activity, t is a time within the schedule, PreSchedule (X, t) is an activity of the latest preceding schedule for the time t scheduled for each participant of X , K (s) is the type of the scheduled activity s, T _E (s) is the end time of the scheduled activity s, C (s) is whether there is an extension constraint on the scheduled activity s, and M (s) is the scheduled Represents a set of people participating in the activity s.
The part in the rectangle enclosed by the innermost dotted line (the term of max) in equation (1) indicates that when a plurality of people participate in the scheduled activity s, the probability value P of the highest extension is adopted. Yes. This is a case of so-called pessimistic evaluation.
The part (sigma term) in the rectangle surrounded by the second dotted line in the equation (1) indicates that when the schedule of the set X of people is set at time t, the preceding preceding schedule of those people is the time t The sum of the probability values P extended until (the time after the start time of the target activity) becomes the expected value of the number of people who are delayed at the time t.
The part in the rectangle enclosed by the dotted line outside the equation (1) (the equation (1) itself) can be estimated by subtracting the number of people expected to be delayed from the planned number of participants X. Which indicates that.

  If the scheduled activity s is in the future, that is, if it is not yet implemented, M (s) is set from the schedule data in the schedule data / facility data storage module 140. If the scheduled activity s is current, that is, if it is in progress, M (s) is the real time of the action log in the indoor action log data storage module 110 or the outdoor action log data storage module 120 (ie, at that time) Set by using the detection result.

  Further, the prospective attendee reachability prediction module 150 may estimate the probability that the schedule will be extended from the relationship with the most recent subsequent schedule for the target scheduled activity. That is, the above-mentioned latest activity is set as a target activity, and the above-described target activity is set as a subsequent activity. Using this, for example, it may be estimated that “how many members will remain until the extended time if this conference is extended”.

  FIG. 5 is a conceptual module configuration diagram of a configuration example according to the second embodiment. The information processing apparatus 500 according to the present embodiment includes an individual-specific behavior pattern prediction module 540, an in-company number prediction module 545, a meeting attendee reachability prediction module 550, a schedule adjustment module 555, a schedule request reception module 560, a schedule. It has an adjustment result notification module 570, and includes a communication log data storage module 505, an indoor behavior log data storage module 510, an outdoor behavior log data storage module 515, an external prediction data storage module 520, an organization data storage module 525, and personal profile data. The storage module 530 is connected to the schedule data / facility data storage module 535.

  The communication log data storage module 505 is connected to the individual behavior pattern prediction module 540. The communication log data storage module 505 stores communication log data that is a record of past information transmission of each person, for example, log data that captures the performance of communication by e-mail or the like. For example, the communication log table 800 illustrated in FIG. 8 is stored. FIG. 8 is an explanatory diagram showing an example of the data structure of the communication log table 800. The communication log table 800 has a transmission time column 810, a transmission employee ID column 820, a reception employee ID collection column 830, and a size column 840. The transmission time column 810 stores the date and time when the electronic mail is transmitted. The outgoing employee ID column 820 stores user information indicating the user who sent the electronic mail. For example, an employee ID. The reception employee ID set column 830 stores user information indicating a user who has received the electronic mail. A plurality of user information may be stored. The size column 840 stores the size (capacity) of the e-mail.

The indoor behavior log data storage module 510 is connected to the individual behavior pattern prediction module 540, the in-company number prediction module 545, and the conference attendee reachability prediction module 550. The indoor behavior log data storage module 510 stores behavior log data that captures the results of indoor behavior. This corresponds to the indoor behavior log data storage module 110 of the first embodiment described above.
The outdoor activity log data storage module 515 is connected to the meeting attendee reachability prediction module 550. The outdoor action log data storage module 515 stores communication log data that captures the results of outdoor actions such as GPS data. This corresponds to the outdoor action log data storage module 120 of the first embodiment described above.

The external prediction data storage module 520 is connected to the meeting attendee reachability prediction module 550 and the schedule adjustment module 555. The external prediction data storage module 520 stores prediction data provided from the outside such as weather forecasts and railway operation information.
The organization data storage module 525 is connected to the individual-specific behavior pattern prediction module 540, the in-company number prediction module 545, and the meeting attendee reachability prediction module 550. The organization data storage module 525 stores data in which the organization to which each individual belongs, the person's job rank, and the person's job type are associated with each other. This corresponds to the organization data storage module 130 of the first embodiment described above.
The personal profile data storage module 530 is connected to the schedule adjustment module 555. The personal profile data storage module 530 stores data that provides auxiliary information for schedule setting.
The schedule data / facility data storage module 535 is connected to the meeting attendance reachability prediction module 550 and the schedule adjustment module 555. The schedule data / facility data storage module 535 stores set schedule data and location profile data (such as the number of conference rooms and facilities). This corresponds to the schedule data / facility data storage module 140 of the first embodiment described above.

  The individual behavior pattern prediction module 540 is connected to the communication log data storage module 505, the indoor behavior log data storage module 510, the organization data storage module 525, and the schedule adjustment module 555. The individual behavior pattern prediction module 540 receives human information indicating the target person from the schedule adjustment module 555, and the indoor action log data or the outdoor action log data stored in the indoor action log data storage module 510 for the person. The outdoor action log data stored in the storage module 515 and the communication log data stored in the communication log data storage module 505 are compared to predict the date or date when the person can participate in the activity.

  The in-company number prediction module 545 is connected to the indoor behavior log data storage module 510, the organization data storage module 525, and the schedule adjustment module 555. Based on the indoor behavior log data stored in the indoor behavior log data storage module 510, the number-of-people presence prediction module 545 predicts the number of people in the target area on a future date or date. The area here refers to a building, a room, etc., for example, a plurality of buildings, rooms, etc. possessed by a certain company, or a predetermined building, room in a certain company. There may be. Further, the number of people in the area will be described mainly by exemplifying the number of people in the company.

  The meeting attendee reachability prediction module 550 includes an indoor behavior log data storage module 510, an outdoor behavior log data storage module 515, an external prediction data storage module 520, an organization data storage module 525, a schedule data / facility data storage module 535, A schedule adjustment module 555 is connected. The prospective attendee reachability prediction module 550 predicts who can attend the meeting as scheduled based on the indoor behavior log data and the schedule data. This corresponds to the attendee reachability prediction module 150 of the first embodiment described above. In other words, it is the past results, the time when the activity was extended, the participant information indicating the participant of the activity, the type information indicating the type of the activity, and the presence or absence of the factors that restrict the extension of the activity The extension record information corresponding to the extension constraint information shown is stored. Of course, this extended performance information is stored in a storage device accessible by the meeting attendee reachability prediction module 550. A person who is scheduled to participate in the first activity participates in the second activity, the elapsed time from the end time in the second activity that is before the start time of the first activity, and participates in the second activity. The start time of the first activity as a result of the extension of the second activity based on the extension result information on the type of the second activity and the presence or absence of the extension restriction of the second activity. Calculate the number of people who cannot attend. Then, the activity of the type information received by the schedule request receiving module 560 is set as the first activity, and the number of persons who cannot attend at the start time of the first activity is calculated. In addition, you may make it perform the process by the attendance prospective reachability prediction module 150 other than these processes.

The schedule adjustment module 555 includes an external prediction data storage module 520, a personal profile data storage module 530, a schedule data / facility data storage module 535, an individual behavior pattern prediction module 540, an in-company number prediction module 545, and a meeting attendee reachable. Are connected to a sex prediction module 550, a schedule request reception module 560, and a schedule adjustment result notification module 570. The schedule adjustment module 555 is based on a request input from the schedule request reception module 560 and information obtained from the individual behavior pattern prediction module 540, the in-company number prediction module 545, and the meeting attendee reachability prediction module 550. Adjust and set the schedule. This corresponds to the schedule adjustment module 160 of the first embodiment described above.
The schedule adjustment module 555 causes the number-of-offices prediction module 545 to predict the number of people in the company. If the type of activity indicated by the type information received by the schedule request reception module 560 is an activity performed in the area, the number of people in the company The schedule adjustment result notification module 570 is made to output the date or date and time when the number of people predicted by the prediction module 545 is larger than a predetermined threshold as the date or date and time suitable for performing the activity.
In addition, the schedule adjustment module 555 determines the number of people predicted by the in-company number prediction module 545 in a case where the activity type indicated by the type information received by the schedule request reception module 560 is an activity performed outside the area. Alternatively, the schedule adjustment result notification module 570 may output a date or date smaller than the threshold as a date or date suitable for performing the activity.
In addition, the schedule adjustment module 555, when the type of activity indicated by the type information received by the schedule request receiving module 560 is an activity performed by the participation of a plurality of people, Have them predict the days or dates when they can participate in the activity. The predicted date or date may be output to the schedule adjustment result notification module 570 as a date or date suitable for performing the activity.

The schedule request reception module 560 is connected to the schedule adjustment module 555. The schedule request receiving module 560 receives a request regarding schedule setting based on a user's operation such as a keyboard and a mouse. For example, type information indicating the type of activity to be scheduled is received and passed to the schedule adjustment module 555.
The schedule adjustment result notification module 570 is connected to the schedule adjustment module 555. The schedule adjustment result notification module 570 notifies the related user of the schedule adjusted by the schedule adjustment module 555 (including display). For example, the information may be displayed on a display device provided in the information processing apparatus 500, or an e-mail may be transmitted to a user related to the schedule.

FIG. 6 is an explanatory diagram illustrating an example of a system configuration when the second embodiment is implemented.
Log DB server host 575, schedule server host 580, location information server host 585, schedule client host 590, GPS data provider 595, weather forecast data provider 620A, railway operation information data provider 620B, communication line 699A, Internet 699B, router 698 Are connected to each other.
The log DB server host 575 is connected to the communication log DB 605, the indoor action log DB 610, and the outdoor action log DB 615. Compared with the module configuration illustrated in FIG. 5, the communication log DB 605 corresponds to the communication log data storage module 505, the indoor action log DB 610 corresponds to the indoor action log data storage module 510, and the outdoor action log DB 615 corresponds to the outdoor action log data. Corresponding to the storage module 515, the organization data DB 625 corresponds to the organization data storage module 525, and the schedule data / facility data DB 635 corresponds to the schedule data / facility data storage module 535. Also, the schedule request reception module 560 and the schedule adjustment result notification module 570 in the information processing apparatus 500 receive an instruction operation or the like from the user 601 to the schedule client host 590, and display the schedule client host 590 and output a notification. Go to inform the user 601 of the schedule and the like.
Further, the outdoor GPS data of each person stored in the GPS data provider 595 can be outdoor action log data stored in the outdoor action log DB 615. Data handled by the weather forecast data provider 620A, the railway operation information data provider 620B, and the like correspond to data stored in the external prediction data storage module 520.

  A user 601 has a tag 602. The tag 602 is detected by the position sensors 603A to 603H. Each position sensor reads user information (for example, employee ID) indicating the user stored in the tag 602 and stores it in the position information server host 585 together with the detected date and time and the position of the position sensor itself. For example, when a position sensor is attached to each room, it is collected by the position information server host 585 as action log data indicating who is in which room and when. Then, the collected action log data is transferred to the log DB server host 575, and the indoor action log table 700 illustrated in FIG. 7 is stored in the indoor action log DB 610.

In addition, the user 601 uses the schedule client host 590 and the like to send and receive electronic mail. The log DB server host 575 collects the log of transmission / reception of the e-mail, and stores the communication log table 800 illustrated in FIG. 8 in the communication log DB 605. The transmission / reception log may be acquired from a mail server.
The schedule client host 590 receives an operation instruction from the user 601, passes the operation instruction to the schedule server host 580, and causes the information processing apparatus 500 to evaluate the schedule. The information processing apparatus 500 evaluates the schedule using data in the communication log DB 605, the indoor action log DB 610, the outdoor action log DB 615, and the like, and displays the evaluation result of the schedule on the schedule client host 590 via the schedule server host 580. , Notify the user 601.

Processing of the individual behavior pattern prediction module 540 will be described.
First, the determination process by the individual behavior pattern prediction module 540 will be described.
The probability of detection by an indoor position sensor is obtained for each successive time period and used as a vector. This vector is called a “detection probability vector”. This represents an individual's behavior pattern. The time zone here means a period from A to B, for example, every 1 hour, every 2 hours, every 30 minutes, or from 9 am to 5 pm Between is every 30 minutes, and every other hour may be between. Hereinafter, every hour starting from midnight is illustrated as a continuous time zone.
An average value of detection probability vectors on business days within a predetermined period in the past is calculated for each individual and used as a determination result. The determination result is, for example, a determination result table 1000. FIG. 10 is an explanatory diagram showing an example of the data structure of the determination result table 1000. The determination result table 1000 has an employee ID column 1010 and a detection probability vector column 1020. The employee ID column 1010 stores an employee ID indicating an employee. The detection probability vector column 1020 stores the detection probability vector of the employee.

The determination process will be described in more detail.
A detection probability vector P (ID, d) is defined as follows for the detection results in indoor behavior on the date d of the employee indicated by the employee ID, and this is defined as an individual behavior pattern.
Detection probability vector P (ID, d) = [P (ID, d, 0), P (ID, d, 1), ..., P (ID, d, 23)]
The determination of the action pattern here generates a detection probability vector P.
Each element P (ID, d, t) in this definition formula represents the detection probability at the time t at the date d of the employee indicated by the employee ID. The time zone here is every hour. That is, when the number of seconds detected indoors between time ts and time te on the date d of the employee indicated by the employee ID is represented by T (ID, d, ts, te), each element P of the detection probability vector is ( 2) It is defined like the formula. In addition, when te is 24, it represents 0 o'clock on the next day of date d. 3600 is the number of seconds for one hour.

T is generated from the indoor action log table 700.
The detection probability vector for a specific area type may be defined by limiting the detection range of T to a specific area type such as a conference room instead of the entire indoor area.

Next, the prediction process by the individual behavior pattern prediction module 540 will be described. Here, the prediction is to predict a day when a person does not use a seat. As a day when the seat is not used, an example of going out will be mainly described.
Using the communication log table 800, measure the amount of change in the amount of mail with customers and employees at other business locations, and show the distribution of the lag (number of days difference) between the peak time and the “outing judgment date” that appears after that. Calculate in advance. The “outing determination date” is extracted by comparing the value of a predetermined time zone of the detection probability vector with a predetermined threshold value. The predetermined time zone corresponds to a working time zone, for example. The comparison with the threshold value is a determination as to whether or not the value of the detection probability vector is less than the threshold value.

When it is determined that a mail volume peak has occurred, a predetermined range centered on a pre-calculated lag is predicted as “a day when going out is likely to occur”, and detection probabilities observed at that lag in the past The average of vectors is output as a predicted value.
When the prediction is established, the prediction result is passed to the schedule adjustment module 555. The prediction result is, for example, a prediction result table 1100. FIG. 11 is an explanatory diagram illustrating a data structure example of the prediction result table 1100. The prediction result table 1100 has an employee ID column 1110, a detection probability vector column 1120, and a prediction date column 1130. The employee ID column 1110 stores an employee ID indicating an employee. The detection probability vector column 1120 stores “an average of detection probability vectors observed at the past lag” of the employee. The predicted date column 1130 stores “the date on which the employee is likely to go out” of the employee.

The prediction process will be described in more detail. Here, a process for predicting a case where an employee who does not normally go out only for a certain period will be described as an example.
First, based on past communication logs (communication log table 800 in communication log data storage module 505) and indoor action logs (indoor action log table 700 in indoor action log data storage module 510), Create “actual data”.
“Result data” is created as follows.
After the e-mail transmission of the employee indicated by the employee ID to the customer domain (for example, the domain portion of each customer's e-mail address stored in the personal profile data storage module 530) has reached its peak, The distribution of the lag (days difference) from the day of “day” is obtained, and the ternary set <ID, P ^* , δ consisting of the employee ID, the mode lag δ ^* , and the average value P ^* of the detection probability vectors in the mode lag. ^* > Is the actual data.

FIG. 12 is a flowchart illustrating an example of processing for creating actual data by the individual-specific behavior pattern prediction module 540 according to the second embodiment.
In step S1202, a set D of customer domains corresponding to the employee ID is acquired from the organization information.
In step S1204, a unit period in which the amount of mail to be transmitted within a unit period is maximized for any domain in the set D is extracted, and the center date of each unit period is substituted into the set A.
In step S1206, a date d in which the detection probability of the “daytime period” in the detection probability vector P (ID, d) is below a certain threshold is extracted and substituted into set B.
In step S1208, the result list X is emptied.

In step S1210, it is determined whether or not the set A is empty. If it is empty, the process proceeds to step S1216. Otherwise, the process proceeds to step S1212.
In step S1212, an arbitrary date d is extracted from the set A and deleted from the set A.
In step S1214, if a date after date d exists in set B, date d ′ closest to date d is taken out from set B, and a binary set <d′−d, P (ID, d ′)> is obtained. Add to achievement list X.
In step S1216, in the result list X, the number of binomial groups <δ, P> is counted for each value of δ, and an average P ^* of P is obtained for δ ^{* having the} largest number of cases, and <ID, P ^* , δ ^* > Is the actual value.

The actual data <ID, P ^* , δ ^* > is “The employee indicated by the employee ID is most likely to go out after δ ^* days after sending many emails to the customer, and the behavior pattern of the day is closest to P ^*. ".
In the future, if there is a date d when the employee is detected to have peaked email transmission to the customer domain in the future, refer to the actual data, and the most frequent lag δ ^* days after that date d Predict that it is likely to occur, and output a prediction result <ID, P ^* , d + δ ^* > (prediction result table 1100).

You may make it estimate that going out to other establishments will increase based on the mail volume to the employee who works at another establishment. The “amount of mail to employees working at other offices” is detected by the presence of the employee ID of “employees working at other offices” in the received employee ID set column 830 of the communication log table 800. The prediction process may be performed.
Further, it may be predicted that the use of the conference room will increase based on the amount of mail to people who are not usually involved in business, but are sometimes related. For example, a person who “does not usually have a business relationship” is a person who has sent an e-mail to a domain other than the above-mentioned customer, or the frequency of the received employee ID set column 830 of the communication log table 800 is predetermined. It is determined by extracting those who are less than the threshold value, and the above-described prediction process may be performed.

Next, the processing of the in-company number prediction module 545 will be described.
Predict the number of employees in the company based on the periodicity that appears in the indoor activity log data. The method for predicting the number of employees in the company will be described later with reference to FIG.
When there is a request from a predetermined period (for example, every day, every week, every month, etc.) or from the schedule adjustment module 555, the prediction result is passed to the schedule adjustment module 555.
The output data of the in-company number prediction module 545 is, for example, a predicted in-company number table 1300. FIG. 13 is an explanatory diagram showing an example of the data structure of the predicted number of employees table 1300. The predicted number of employees table 1300 has a date column 1310 and a predicted number of employees 1320. The date column 1310 stores the date. The predicted number of employees 1320 stores the prediction result of the number of employees at the date.

FIG. 14 is a flowchart illustrating an example of processing performed by the in-company presence prediction module 545 according to the second embodiment.
In step S1402, the organization data 1450 in the organization data storage module 525 is acquired.
In step S1404, the indoor action log data 1455 in the indoor action log data storage module 510 is acquired.
In step S1406, the attendance average value for each predetermined period (day of the week, time, month, etc.) is calculated for each job type (for example, sales, researcher, etc.) and each job rank (for example, manager, general employee, etc.). The tendency to be in the company often differs depending on the job type and job level, so the calculation is made for each job type and job level. Specifically, human information indicating the target job type and job level is extracted from the organization data. Then, using the indoor action log data, the attendance time for each period of the person indicated by the person information is extracted, the extracted attendance times are totaled, and the average attendance value is calculated using the time for each period as the denominator. .
In step S 1408, the attendance average value of each person is stored in the database in the meeting attendee reachability prediction module 550 as the predicted number of employees. For example, there is a predicted number of employees table 1300 as a data format of data to be accumulated. In addition, the predicted number of employees table 1300 may be provided with columns for storing job types, job type information indicating job levels, and job level information.

Next, the process of the meeting attendee reachability prediction module 550 will be described.
From the indoor behavior log in the indoor behavior log data storage module 510 and the schedule data in the schedule data / facility data storage module 535, the possibility that a person who attended the conference can attend another schedule after one conference Predict.
While a certain meeting is scheduled, a prediction is made on the assumption that everyone has attended, and at the time when the meeting is held, a prediction is made based on the person who is actually attending.
The prediction result is passed to the schedule adjustment module 555. The prediction result is, for example, a schedule availability table 1500. FIG. 15 is an explanatory diagram showing an example of the data structure of the schedule availability table 1500. The schedule availability table 1500 includes an employee ID column 1510, a time column 1520, and a schedule availability column 1530. The employee ID column 1510 stores an employee ID indicating an employee. The time column 1520 stores the time (the time after the scheduled end time of the conference). The schedule availability possibility column 1530 stores the possibility that the person's schedule becomes empty at that time (that is, the possibility (probability) that the person can participate in the next meeting at that time).

FIG. 16 is a flowchart illustrating an example of processing performed by the meeting attendee reachability prediction module 550 according to the second embodiment.
In step S1602, the current time is set to Tn. Get information (location, time, participants) of the target meeting schedule from the schedule data. A place registered in the schedule (hereinafter also referred to as a registered place) is a target position (Pe), and a scheduled start time is a target arrival time (Te).
In step S1604, the schedule immediately before the target meeting schedule of the employee X who is scheduled to attend the target meeting is acquired from the schedule data. In the case before the start of the immediately preceding schedule, the registered place is the action start place (Ps), and the scheduled end time is the action start time (Ts). In the case of the previous schedule, the current position is the action start location (Ps) and the scheduled end time is the action start time (Ts). When the immediately preceding schedule ends, the current position is the action start location (Ps) and the current time is the action start time (Ts).

In step S1606, the situation Sx of the employee X is predicted from the schedule data and the action log. The situation Sx includes, for example, a meeting, a living room, and the outside.
In step S1608, the average end time difference dTμ is (ΣdTi) from the information of the difference (dTi, i = 1, 2,..., N) between the scheduled end time and the actual end time in the past N situations Sx. The variance dTσ ² of the end time difference is calculated from (Σ (dTi−dTμ) ² ) / N. The behavior start time distribution Ts ′ in consideration of the situation Sx of the employee X is given by Ts ′ = Ts + N (dTμ, dTσ ² ). Here, N (dTμ, dTσ ² ) is a normal distribution having an average dTμ and a variance dTσ ² (see FIGS. 17A and 17B).

In step S1610, D is calculated as the moving distance between Pe and Ps, and the reference moving time Tb is calculated. The travel time distribution T is calculated from Ts ′ and Te (see FIG. 17C).
In step S1612, Pr = F (T, Tb) is calculated. That is, the reachability Pr of the employee X who is scheduled to attend the meeting is calculated from T and Tb (see FIG. 17D).
In step S1614, the prediction result is transmitted to the schedule adjustment module 555. The combination of the data of <X, Tn, Pr> is assumed to be a prediction result <employee ID, time, probability of existing at the meeting place Pe at time Te> (data in the schedule availability table 1500).

Next, processing of the schedule adjustment module 555, the schedule request reception module 560, and the schedule adjustment result notification module 570 will be described.
Processing examples in the following two scenes will be described.
(1) Setting a schedule When a user tries to set a schedule, the schedule is displayed on the scheduler so that the date / time zone with a low environmental load and the possibility that the schedule can be realized are more or less likely to be realized. .
A description will be given of the user interface for supporting the adjustment of the schedule among a plurality of users and the specific contents of the operation.
(2) Scenes that should be changed in response to a change in the situation. For example, when the number of people who are scheduled to attend a scheduled meeting decreases, the system will automatically respond to those who can attend. Notify the reschedule proposal. For example, the number of people who can attend can select a room with less environmental load, or the schedule can be postponed.

(1) Scene for setting a schedule This will be described with reference to FIGS.
FIG. 18 is a flowchart illustrating an example of processing performed by the schedule adjustment module 555 according to the second embodiment.
In step S1802, the schedule request receiving module 560 receives the schedule conditions in accordance with the operation of the keyboard, mouse, and the like by the user. For example, it receives type information indicating the type of schedule (type of vacation, outing, personal work, meeting, etc.) and a period for setting the schedule.
In step S1804, the schedule adjustment module 555 acquires the schedule data 1850 of the user operated in step S1802 from the schedule data / facility data storage module 535.
In step S1806, the schedule adjustment module 555 acquires the in-company number prediction information 1855 (the predicted in-company number table 1300 illustrated in FIG. 13) from the in-company number prediction module 545.

In step S 1808, the schedule adjustment module 555 acquires conference room / room data as facility data 1860 from the schedule data / facility data storage module 535.
In step S1810, the schedule adjustment module 555 calculates a schedule setting recommendation level based on the prediction result of the individual behavior pattern prediction module 540 or the in-company number prediction module 545, and displays it on the display device in the schedule adjustment result notification module 570. Let The schedule setting recommended level will be described later with reference to FIGS.
In step S1812, the schedule request receiving module 560 selects a specific candidate (date or date) according to the operation of the keyboard, mouse, and the like by the user.

In step S1814, the schedule adjustment module 555 extracts details about the specific candidate selected in step S1812 from the schedule data / facility data storage module 535, and causes the schedule adjustment result notification module 570 to display the details on the display device.
In step S1816, the schedule request receiving module 560 receives time and schedule details in accordance with the operation of the keyboard, mouse, and the like by the user.
In step S1818, the schedule adjustment result notification module 570 displays a confirmation screen.

In step S1820, the schedule request receiving module 560 receives an instruction as to whether or not to register a schedule according to the operation of the keyboard, mouse, and the like by the user, and the schedule adjustment module 555 determines whether or not the instruction registers a schedule. If it is registered, the process proceeds to step S1822. Otherwise, the process ends (step S1899).
In step S1822, the schedule adjustment module 555 changes the schedule data 1865 in the schedule data / facility data storage module 535.
In step S1824, the schedule adjustment module 555 changes the conference room / room reservation data, which is the facility data 1870 in the schedule data / facility data storage module 535.
In step S1826, the schedule adjustment module 555 causes the schedule adjustment result notification module 570 to display a registration completion screen on the display device.

FIG. 19 is a flowchart illustrating an example of processing performed by the schedule adjustment module 555 according to the second embodiment. 19 shows a detailed processing example of the flowchart illustrated in FIG. 18.
In step S1902, the schedule request reception module 560 displays a condition reception screen. For example, the display screen 2300 shown in FIG. Then, a month schedule 2302 and a new schedule button 2304 are displayed in the display screen 2300. When the new schedule button 2304 is selected by the user's operation of the keyboard, mouse, etc., a condition acceptance setting screen 2310 illustrated in FIG. 23B is displayed. In the condition reception setting screen 2310, a schedule type setting area 2312 and a period selection area 2314 are displayed so that a schedule type and a period for setting the schedule can be selected.

In step S1904, the schedule request receiving module 560 selects a schedule type and receives a period in accordance with the operation of the keyboard, mouse, and the like by the user. This is a case where the search button 2316 is selected after the selection operation for the scheduled type setting area 2312 and the period selection area 2314 in the condition reception setting screen 2310 illustrated in FIG.
In step S1906, the schedule adjustment module 555 acquires the target user's schedule data 1950 from the schedule data / facility data storage module 535.

In step S 1908, the schedule adjustment module 555 acquires the in-company number prediction information 1955 from the in-company number prediction module 545.
In step S1910, the schedule adjustment module 555 determines what the type of schedule (the type selected in the schedule type setting area 2312) is, and if it is closed / half-leaved, outing, etc. (that is, not in the company) ) Goes to step S1912 (the flowchart illustrated in FIG. 20), and in the case of personal work (when adjustment with another person is unnecessary, as in the type setting area 2312 scheduled in FIG. 23B) If personal work is selected, the process proceeds to step S1914 (flowchart illustrated in FIG. 21), and if it is a meeting (if adjustment with another person is required, the type setting of the schedule illustrated in FIG. 24B). If a conference is selected as in the area 2412), the process proceeds to step S1916 (the flowchart illustrated in FIG. 22).

FIG. 20 is a flowchart illustrating an example of processing performed by the schedule adjustment module 555 according to the second embodiment.
In step S2002, the schedule adjustment module 555 calculates a recommended schedule setting level based on the in-company number prediction information 1955. Here, since it is closed / half-closed, outing, etc., a schedule setting recommended level is calculated so that a day or date with a low presence rate is given priority. For example, as compared with a predetermined threshold value, a day or a date when the in-company rate is lower than the threshold value is extracted as a date or a date when setting a holiday / half-time holiday, going out, or the like.
In step S2004, the schedule adjustment result notification module 570 displays the level-specific schedule. For example, the days on which to set holidays / half holidays and going out are displayed differently from other days.
In step S2006, the schedule request receiving module 560 selects a specific candidate (day) according to the operation of the keyboard, mouse, and the like by the user.

In step S2008, the schedule request receiving module 560 displays the schedule by time level for the specific candidate (day). For example, the date / time for setting a holiday / half-day off, going out, etc. is displayed differently from other dates / times.
In step S2010, the schedule request receiving module 560 selects a specific candidate (hour) in accordance with the operation of the keyboard, mouse, and the like by the user.
In step S2012, the schedule request reception module 560 displays a time and schedule details reception screen.
In step S2014, the schedule request receiving module 560 receives time and schedule details in accordance with the operation of the keyboard, mouse, and the like by the user.

In step S2016, the schedule request reception module 560 displays a confirmation screen.
In step S2018, the schedule request receiving module 560 receives an instruction as to whether or not to register a schedule according to the operation of the keyboard, mouse, and the like by the user, and the schedule adjustment module 555 determines whether or not the instruction registers a schedule. If it is registered, the process proceeds to step S2020. Otherwise, the process ends (step S2099).
In step S2020, the schedule adjustment module 555 changes the corresponding schedule data 2050 in the schedule data / facility data storage module 535.
In step S2022, the schedule adjustment module 555 causes the schedule adjustment result notification module 570 to display a registration completion screen on the display device.

FIG. 21 is a flowchart illustrating an example of processing performed by the schedule adjustment module 555 according to the second embodiment.
In step S <b> 2102, the schedule adjustment module 555 acquires the room information as the facility data 2150 from the schedule data / facility data storage module 535.
In step S2104, the schedule adjustment module 555 calculates the recommended schedule setting level based on the number-of-offices prediction information 1955 for each facility (that is, the prediction of the number of people in each facility (room, etc.)). Here, since it is a personal work, a schedule setting recommendation level is calculated so that a day or a date with a high in-company rate is given priority. For example, the date or date and time when the in-company rate is higher than the threshold value compared with a predetermined threshold value is extracted as the date or date and time when personal work should be set. Here, the schedule setting recommended level for the equipment (room, etc.) is also calculated so that one equipment (room, etc.) can be used by a larger number of people. Here, there are three types of threshold values, and examples of dividing into four levels are given.

In step S2106, the schedule adjustment result notification module 570 displays the level-specific schedule. For example, the day on which personal work should be set is displayed differently from other days. As a specific example, a display screen 2320 shown in FIG. This is an example of display divided into four levels for each day, indicating that March 10th, 15th, etc. are days suitable for personal work. Here, “suitable for personal work” means being suitable from the viewpoint of environmental load.
In step S2108, the schedule request receiving module 560 selects a specific candidate (day) according to the operation of the keyboard, mouse, and the like by the user. As a specific example, a day in the month schedule 2322 shown in FIG. 23C is selected, and a new schedule button 2324 is selected.
In step S2110, schedule adjustment result notification module 570 displays a schedule by room / time level. For example, the time when personal work should be set is displayed differently from other times. As a specific example, it is displayed as a display screen 2340 shown in FIG. This is an example in which each facility (room) is divided into four levels and displayed, and the period from 10:00 to 12:00, from 13:00 to 15:00, etc. is the day suitable for personal work. It shows that there is.

In step S2112, the schedule request receiving module 560 selects a room in accordance with the operation of the keyboard, mouse, and the like by the user. As a specific example, a facility (room) in the day schedule 2342 shown in FIG.
In step S2114, schedule adjustment result notification module 570 displays the room information of the selected room.
In step S <b> 2116, the schedule request receiving module 560 selects a specific candidate (hour) according to the operation of the keyboard, mouse, and the like by the user.
In step S2118, schedule request reception module 560 displays a time and schedule details reception screen.
In step S2120, the schedule request receiving module 560 receives time and schedule details according to the operation of the keyboard, mouse, and the like by the user.

In step S2122, the schedule request reception module 560 displays a confirmation screen.
In step S2124, the schedule request receiving module 560 receives an instruction as to whether or not to register a schedule according to the operation of the keyboard, mouse, and the like by the user, and the schedule adjustment module 555 determines whether or not the instruction registers a schedule. If it is registered, the process proceeds to step S2126. Otherwise, the process ends (step S2199).
In step S2126, the schedule adjustment module 555 changes the corresponding schedule data 2165 in the schedule data / facility data storage module 535.
In step S2128, the schedule adjustment module 555 changes the room reservation data, which is the corresponding equipment data 2170 in the schedule data / facility data storage module 535.
In step S2130, the schedule adjustment module 555 causes the schedule adjustment result notification module 570 to display a registration completion screen on the display device.

FIG. 22 is a flowchart illustrating a processing example by the schedule adjustment module 555 according to the second embodiment.
In step S2202, the schedule adjustment module 555 acquires the organization data 2250 from the organization data storage module 525, and the schedule request reception module 560 displays the participation number reception and member search / selection screen. As a specific example, a number setting area 2422 and a member setting area 2424 are displayed in a new scheduled meeting number / member selection screen 2420 shown in FIG.
In step S2204, the schedule request receiving module 560 receives the number of participants and selects a member (conference participant) according to the operation of the keyboard, mouse, and the like by the user. As a specific example, the number of participants is received in the number setting area 2422 shown in FIG. 24C, and members are selected in the member setting area 2424.

In step S2206, the schedule adjustment module 555 acquires the conference room information as the equipment data 2255 and the reservation information about the conference room from the schedule data / facility data storage module 535.
In step S 2208, the schedule adjustment module 555 acquires the member schedule data 2260 from the schedule data / facility data storage module 535.
In step S <b> 2210, the schedule adjustment module 555 obtains member individual behavior pattern prediction information 2265 from the individual behavior pattern prediction module 540.

In step S2212, the schedule adjustment module 555 calculates a schedule setting recommendation level based on the number of people in the company prediction information 1955 and the individual behavior pattern prediction information 2265. Here, since it is a meeting, a schedule setting recommendation level is calculated so that a day or a date when the member's working rate is high is given priority. For example, the date or date and time when the in-company rate is higher than the threshold value compared with a predetermined threshold value is extracted as the date or date and time when personal work should be set. Here, there are three types of threshold values, and examples of dividing into four levels are given.
In step S2214, the schedule adjustment result notification module 570 displays the level-specific schedule. For example, the date on which the conference is to be set is displayed differently from other days. As a specific example, it is displayed as a display screen 2430 shown in FIG. This is an example of display divided into four levels for each day, indicating that March 10th, 15th, etc. are days suitable for the conference. Here, “suitable for a meeting” means that it is suitable from the viewpoint of environmental load and schedule adjustment.

In step S2216, the schedule request receiving module 560 selects a specific candidate (day) according to the operation of the keyboard, mouse, and the like by the user.
In step S2218, the schedule adjustment result notification module 570 displays a conference room / time level schedule. For example, the time when the conference should be set is displayed differently from other times. As a specific example, it is displayed as a display screen 2440 shown in FIG. This is an example in which each meeting room is divided into four levels, and in the A meeting room, the period from 12:00 to 13:00, the period from 17:00 to 19:00, etc. are days that are suitable for the meeting. Is shown.

In step S2220, schedule request reception module 560 selects a conference room in accordance with the operation of the keyboard, mouse, and the like by the user. As a specific example, a conference room in the day schedule 2442 shown in FIG.
In step S2222, the schedule adjustment result notification module 570 displays the conference room information of the selected conference room.
In step S2224, the schedule request receiving module 560 selects a specific candidate (hour) according to the operation of the keyboard, mouse, and the like by the user.
In step S2226, schedule request reception module 560 displays a time and schedule details reception screen.
In step S2228, the schedule request receiving module 560 receives time and schedule details according to the operation of the keyboard, mouse, and the like by the user.

In step S2230, schedule request receiving module 560 displays a confirmation screen.
In step S2232, the schedule request receiving module 560 receives an instruction as to whether or not to register a schedule according to the operation of the keyboard, mouse, and the like by the user, and the schedule adjustment module 555 determines whether or not the instruction registers a schedule. If it is registered, the process proceeds to step S2234. Otherwise, the process ends (step S2299).
In step S2234, the schedule adjustment module 555 changes the corresponding schedule data 2270 in the schedule data / facility data storage module 535.
In step S2236, the schedule adjustment module 555 changes the conference room reservation data, which is the corresponding equipment data 2275 in the schedule data / facility data storage module 535.
In step S2238, the schedule adjustment module 555 causes the schedule adjustment result notification module 570 to display a registration completion screen on the display device.

(2) A scene that should be changed in response to a change in the situation, which has already been set. This will be described with reference to FIGS.
FIG. 25 is a flowchart illustrating an example of processing performed by the schedule adjustment module 555 according to the second embodiment.
In step S2502, the schedule adjustment module 555 acquires schedule data from the schedule data / facility data storage module 535.
In step S2504, the schedule adjustment module 555 acquires meeting attendee information from the schedule data / facility data storage module 535, and predicts the reachability of each member (conference participant) from the meeting attendee reachability prediction module 550. Get the result.
In step S2506, the schedule adjustment module 555 determines whether or not the predicted arrival number is within the allowable range of the reserved conference room attribute. If it is within the allowable range, the process is terminated (step S2599). It progresses to step S2508.

In step S2508, the schedule adjustment module 555 notifies the schedule adjustment result notification module 570 of a meeting rescheduling recommendation to a member whose meeting reachability is higher than a predetermined threshold value, and presents a reschedule plan. For example, the display screen 2600 shown in FIG. In the column of March 3 in the month schedule 2602, a display indicating that there is a re-schedule recommended meeting (“!” Mark in FIG. 26A) is performed. When the March 3rd column is selected according to the operation of the keyboard, mouse, etc. by the member, a reschedule recommendation notification screen 2610 shown in FIG. 26B is displayed. The reschedule recommendation notification screen 2610 includes an update button 2612 and a reschedule candidate display table 2614 in the latest information. The reschedule candidate display table 2614 includes a schedule name field 2616, a start time field 2618, a place field 2620, a scheduled participant field 2622, a participant prediction result field 2624, a schedule change field 2626, and a conference room change field 2628. ing. A schedule change selection button 2630 is displayed in the schedule change column 2626, and a conference room change selection button 2632 is displayed in the conference room change column 2628.
In step S2510, the schedule request receiving module 560 receives an instruction as to whether or not to postpone the meeting schedule in accordance with the operation of the keyboard, mouse, and the like by the user. Then, the schedule adjustment module 555 determines whether or not the instruction is to postpone the schedule of the meeting. If so, the process proceeds to step S2516; otherwise, the process proceeds to step S2512.

In step S2512, the schedule adjustment result notification module 570 notifies the member of the recommended change of the conference room and the recommended empty conference room. For example, the schedule change selection screen 2640 shown in FIG. The schedule change selection screen 2640 has a schedule change candidate display table 2642. The schedule change candidate display table 2642 includes a date field 2644, a start time field 2646, a place field 2648, a participant prediction result field 2650, and a selection field 2652. In the selection field 2652, OK buttons 2654 and 2656 are displayed.
In step S <b> 2514, the schedule request receiving module 560 receives an instruction as to whether or not to change the conference room in accordance with the operation of the keyboard, mouse, and the like by the user. Then, the schedule adjustment module 555 determines whether or not the instruction changes the conference room. If so, the process proceeds to step S2516; otherwise, the process ends (step S2599).
In step S2516, the schedule adjustment module 555 updates the schedule data in the schedule data / facility data storage module 535.
In step S1518, the schedule adjustment result notification module 570 notifies the meeting members of the schedule update.

  The hardware configuration of the computer on which the program according to the above-described embodiment is executed is a general computer, specifically a personal computer, a computer that can be a server, or the like as illustrated in FIG. . That is, as a specific example, a CPU 2701 is used as a processing unit (calculation unit), and a RAM 2702, a ROM 2703, and an HD 2704 are used as storage devices. For example, a hard disk may be used as the HD 2704. Expected attendee reachability prediction module 150, schedule adjustment module 160, individual behavior pattern prediction module 540, in-company number prediction module 545, meeting attendee reachability reachability prediction module 550, schedule adjustment module 555, etc. CPU 2701, RAM 2702 for storing the program and data, ROM 2703 for storing a program for starting up the computer, HD 2704 as an auxiliary storage device, and user operations on a keyboard, a mouse, a touch panel, etc. Receiving device 2706 that accepts data based on the above, an output device 2705 such as a CRT or a liquid crystal display, and a communication line interface for connecting to a communication network such as a network interface card 707, and, and a bus 2708 for exchanging data by connecting them. A plurality of these computers may be connected to each other via a network.

Among the above-described embodiments, the computer program is a computer program that reads the computer program, which is software, in the hardware configuration system, and the software and hardware resources cooperate with each other. Is realized.
The hardware configuration shown in FIG. 27 shows one configuration example, and the above-described embodiment is not limited to the configuration shown in FIG. 27, and the modules described in the above-described embodiment can be executed. Any configuration may be used. For example, some modules may be configured by dedicated hardware (for example, ASIC), and some modules may be in an external system and connected via a communication line. A plurality of systems shown in FIG. 5 may be connected to each other via communication lines so as to cooperate with each other. In particular, in addition to personal computers, information appliances, copiers, fax machines, scanners, printers, and multifunction machines (image processing apparatuses having two or more functions of scanners, printers, copiers, fax machines, etc.) Etc. may be incorporated.

In the above-described embodiment, indoor action log data is used. However, outdoor action log data may be used. For example, it may be determined whether the position of the meeting room is indoors or outdoors, and if it is outdoors, the outdoor action log data may be used.
When selecting a facility such as a room or a conference room, the weather forecast data provided by the weather forecast data provider 620A is acquired, and the facility can be selected according to the attribute of the facility based on the weather forecast data. It may be. For example, if the scheduled date of the meeting is sunny, a meeting room with a window may be preferentially selected.
Further, the behavior pattern for each individual stored in the personal profile data storage module 530 may be acquired, and the priority order in the case of schedule setting may be determined based on the behavior pattern. For example, the behavior pattern indicating whether or not there are many attendances before 9 am is obtained, and if there are many attendances before 9 am, output the date and time that will be activity in the morning, otherwise , It may be possible to output a date and time that becomes an activity from the afternoon.

The program described above may be provided by being stored in a recording medium, or the program may be provided by communication means. In that case, for example, the above-described program may be regarded as an invention of a “computer-readable recording medium recording the program”.
The “computer-readable recording medium on which a program is recorded” refers to a computer-readable recording medium on which a program is recorded, which is used for program installation, execution, program distribution, and the like.
The recording medium is, for example, a digital versatile disc (DVD), which is a standard established by the DVD Forum, such as “DVD-R, DVD-RW, DVD-RAM,” and DVD + RW. Standard “DVD + R, DVD + RW, etc.”, compact disc (CD), read-only memory (CD-ROM), CD recordable (CD-R), CD rewritable (CD-RW), Blu-ray disc ( Blu-ray Disc (registered trademark), magneto-optical disk (MO), flexible disk (FD), magnetic tape, hard disk, read-only memory (ROM), electrically erasable and rewritable read-only memory (EEPROM), flash Includes memory, random access memory (RAM), etc. .
The program or a part of the program may be recorded on the recording medium for storage or distribution. Also, by communication, for example, a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a wired network used for the Internet, an intranet, an extranet, etc., or wireless communication It may be transmitted using a transmission medium such as a network or a combination of these, or may be carried on a carrier wave.
Furthermore, the program may be a part of another program, or may be recorded on a recording medium together with a separate program. Moreover, it may be divided and recorded on a plurality of recording media. Further, it may be recorded in any manner as long as it can be restored, such as compression or encryption.

DESCRIPTION OF SYMBOLS 100 ... Information processing apparatus 110 ... Indoor action log data storage module 120 ... Outdoor action log data storage module 130 ... Organization data storage module 140 ... Schedule data / facility data storage module 150 ... Expected attendee reachability prediction module 160 ... Schedule adjustment Module 170 ... Schedule reception module 180 ... Schedule display module 500 ... Information processing device 505 ... Communication log data storage module 510 ... Indoor action log data storage module 515 ... Outdoor action log data storage module 520 ... External prediction data storage module 525 ... Organization data Storage module 530 ... Personal profile data storage module 535 ... Schedule data / facility data storage module 540 ... Individual Action pattern prediction module 545 ... Zaisha persons prediction module 550 ... conference attendees reachability prediction module 555 ... scheduling module 560 ... schedule request receiving module 570 ... scheduling result notification module

Claims (7)

A receiving means for receiving type information indicating the type of activity to be scheduled;
Based on behavior information that is a record of each person's past behavior, a number prediction means for predicting the number of people in the target area on a future date or date and time,
In the case where the activity type indicated by the type information received by the reception means is an activity performed in the area, the day or date when the number of persons predicted by the number prediction means is greater than a predetermined threshold is selected. An information processing apparatus comprising output means for outputting as a date or a date and time suitable for performing When the activity type indicated by the type information received by the receiving unit is an activity performed outside the area, the output unit is a day when the number of people predicted by the number of people prediction unit is less than a predetermined threshold or The information processing apparatus according to claim 1, wherein the date and time is output as a date or a date and time suitable for performing the activity. When the type of activity indicated by the type information received by the receiving unit is an activity performed by participation of a plurality of persons, the output unit includes action information that is a record of past actions of the person who participates and the person Comparing the information transfer information, which is a record of the past information transfer, predicting the date or date when the person can participate in the activity, and the date or date suitable for performing the activity The information processing apparatus according to claim 1, wherein the information processing apparatus outputs the date and time. It is a past performance, the time when the activity is extended, the participant information indicating the participant of the activity, the type information indicating the type of the activity, and the extension indicating whether there is a factor that restricts the extension of the activity Past record information storage means for storing extended record information corresponding to the constraint information;
A person who is scheduled to participate in the first activity, and an elapsed time from the end time in the second activity before the start time of the first activity, a person who participates in the second activity, The second activity is extended based on the extension result information stored in the past result information storage means with respect to the type of the second activity and the presence or absence of the extension restriction of the second activity. A calculation means for calculating the number of persons who cannot attend at the start time of the activity of 1,
The calculating means calculates the number of persons who cannot attend at the start time of the first activity, with the activity of the type information received by the receiving means as the first activity,
The information processing apparatus according to claim 3, wherein the output unit outputs that the schedule of the activity is changed when the number of persons calculated by the calculation is larger than a predetermined number of persons. It is a past performance, the time when the activity is extended, the participant information indicating the participant of the activity, the type information indicating the type of the activity, and the extension indicating whether there is a factor that restricts the extension of the activity Past record information storage means for storing extended record information corresponding to the constraint information;
Participant information indicating a person who is scheduled to participate in the target first activity, and a receiving unit that receives the start time of the first activity;
A person who is scheduled to participate in the first activity indicated by the participant information received by the receiving means, and has elapsed since the end time of the second activity that is before the start time of the first activity Based on the extended performance information stored in the past performance information storage means for the time, the person participating in the second activity, the type of the second activity, the presence or absence of the extension restriction of the second activity, Calculating means for calculating the number of persons who are unable to attend at the start time of the first activity by extending the second activity;
A number-of-participants calculating means for calculating the number of persons who can participate in the first activity by subtracting the number of persons calculated by the calculating means from the number of persons scheduled to participate in the first activity; An information processing apparatus characterized by the above. Computer
A receiving means for receiving type information indicating the type of activity to be scheduled;
Based on behavior information that is a record of each person's past behavior, a number prediction means for predicting the number of people in the target area on a future date or date and time,
In the case where the activity type indicated by the type information received by the reception means is an activity performed in the area, the day or date when the number of persons predicted by the number prediction means is greater than a predetermined threshold is selected. An information processing program for functioning as an output means for outputting as a date or a date and time suitable for performing. Computer
It is a past performance, the time when the activity is extended, the participant information indicating the participant of the activity, the type information indicating the type of the activity, and the extension indicating whether there is a factor that restricts the extension of the activity Past record information storage means for storing extended record information corresponding to the constraint information;
Participant information indicating a person who is scheduled to participate in the target first activity, and a receiving unit that receives the start time of the first activity;
A person who is scheduled to participate in the first activity indicated by the participant information received by the receiving means, and has elapsed since the end time of the second activity that is before the start time of the first activity Based on the extended performance information stored in the past performance information storage means for the time, the person participating in the second activity, the type of the second activity, the presence or absence of the extension restriction of the second activity, Calculating means for calculating the number of persons who are unable to attend at the start time of the first activity by extending the second activity;
To function as a participant number calculating means for calculating the number of persons who can participate in the first activity by subtracting the number of persons calculated by the calculating means from the number of persons scheduled to participate in the first activity. Information processing program.

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