Seat recommendation server, seat recommendation system and seat recommendation method

Abstract

To provide a technique capable of presenting a seat capable of reducing stress received from other employees as much as possible in facilities in which a free address system is introduced.SOLUTION: A CPU 41 determines a recommended seat to be recommended to an employee. An I / F device 44 communicates with a terminal 60 capable of displaying the recommended seat and inputting evaluation information on an employee related to the seat. A storage device 45 stores an evaluation DB 81 for accumulating evaluation information acquired from the terminal 60. The CPU 41 acquires position information on the employee sitting on the seat. The CPU 41 determines the recommended seat based on accumulated evaluation information and position information. The I / F device 44 transmits the determined recommended seat to the terminal 60.SELECTED DRAWING: Figure 16

Description

[Technical Field]

[0001] The present disclosure relates to a seat recommendation server, a seat recommendation system, and a seat recommendation method that recommend seats for employees working in a facility to work at on the day. [Background technology]

[0002] In offices and other facilities, there are cases where employees sitting nearby cause stress. There are various causes of stress from other employees, such as the smell of cigarettes from the employee sitting nearby, the loud keyboard typing, or the gaze of the employee sitting across from you. However, in order to avoid worsening interpersonal relationships in the workplace, these facts are rarely communicated to the other employee face to face.

[0003] On the other hand, in recent years, some offices have been introducing free address systems, where employees are not assigned fixed seats and can use any seat they like, with the aim of improving employee productivity. Under a free address system, employees can choose their own seat to work in, so they are not exposed to stress from the same employee for a long period of time, but there is still a risk of being stressed by an employee who happens to be sitting nearby.

[0004] Patent Publication No. 2022-79348 (Patent Document 1) discloses an information processing device that identifies employee preference trends in offices where such a free address system has been introduced, and prioritizes recommending seats with high preference trends. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2022-79348 Summary of the Invention [Problem to be solved by the invention]

[0006] However, the information processing device does not take into consideration the actual situation, such as the type of stress that occurs in relation to the distance between employees, and there is still room for improvement in determining the recommended seating.

[0007] The present disclosure has been made to solve the above-mentioned problems, and its purpose is to provide technology that can present seating that minimizes stress experienced by other employees in facilities that have introduced a free address system. [Means for solving the problem]

[0008] The seat recommendation server according to the present disclosure is a server that recommends seats for employees working in a facility to work in on the day. The seat recommendation server includes a processing device, a communication device, and a storage device. The processing device determines a recommended seat to recommend to the employee. The communication device communicates with a terminal device that can display the recommended seats and that can input employee evaluation information regarding the seats. The storage device stores an evaluation database that accumulates evaluation information acquired from the terminal device. The processing device acquires location information of the employee sitting in the seat. The processing device determines a recommended seat based on the accumulated evaluation information and location information. The communication device transmits the determined recommended seat to the terminal device.

[0009] The seat recommendation system according to the present disclosure includes a seat recommendation server and a terminal device.

[0010] The seat recommendation method according to the present disclosure is a method for recommending a seat for an employee working in a facility to work in on a given day. The seat recommendation method includes a step of determining a seat recommended to the employee, a step of communicating with a terminal device capable of displaying the recommended seat and allowing the employee to input evaluation information about the seat, and a step of storing the evaluation information acquired from the terminal device in an evaluation database. The determining step includes a step of acquiring position information of the employee sitting in the seat, and a step of determining a recommended seat based on the accumulated evaluation information and position information. The communicating step includes a step of transmitting the determined recommended seat to the terminal device. [Effects of the Invention]

[0011] According to the present disclosure, in facilities that have adopted a free address system, it is possible to provide seating that minimizes stress experienced by employees from other employees. Reducing stress contributes to improving employee productivity. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a diagram showing the configuration of a seat recommendation system according to a first embodiment. [Figure 2] FIG. 2 is a diagram illustrating a hardware configuration of a seat recommendation server, etc. [Figure 3] 10 is an example of a user table. [Figure 4] 10 is an example of a device table. [Figure 5] 10 is a display example of an input screen for evaluation information. [Figure 6] FIG. 10 is a diagram for explaining an evaluation DB. [Figure 7] 10 is an example of a coefficient table. [Figure 8] FIG. 10 is a diagram for explaining the determination of recommended seat candidates. [Figure 9] FIG. 10 is a diagram for explaining the determination of recommended seat candidates. [Figure 10] FIG. 10 is a diagram for explaining the determination of recommended seat candidates. [Figure 11] FIG. 10 is a diagram for explaining the determination of recommended seat candidates. [Figure 12] FIG. 10 is a diagram for explaining the determination of recommended seat candidates. [Figure 13] FIG. 10 is a diagram for explaining the determination of recommended seat candidates. [Figure 14] 10 is a flowchart of evaluation information processing. [Figure 15] 10 is a flowchart of a recommended seat process. [Figure 16] 10 is a flowchart of a recommended seat determination process. [Figure 17] 10 is a flowchart of a proximity determination process. [Figure 18] 10 is a flowchart of a redetermining process. [Figure 19] FIG. 10 is a diagram for explaining an evaluation DB according to the second embodiment. [Figure 20] 10 is a flowchart of a proximity determination process according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, embodiments will be described with reference to the drawings. In the following description, the same components are denoted by the same reference numerals. The names and functions of these components are also the same. Therefore, detailed descriptions thereof will not be repeated.

[0014] First Embodiment Fig. 1 is a diagram showing the configuration of a seat recommendation system 100 according to the first embodiment. Fig. 2 is a diagram showing the hardware configuration of a seat recommendation server 50 and the like.

[0015] As shown in FIG. 1, the seat recommendation system 100 includes a seat recommendation server 50, a terminal 60 carried by an employee 10 (hereinafter also referred to as a "user") working in the facility, an equipment management device 40, and a plurality of air conditioners (air conditioning indoor units) 31.

[0016] In this embodiment, it is assumed that a free address system is adopted within the facility, where employees 10 (users) are not assigned fixed seats and can use any seat they like. The seat recommendation server 50 has a seat recommendation function that reflects the evaluations of employees, and determines the recommended seat for the employee 10 to work on that day as the recommended seat.

[0017] The recommended seat determined can be displayed on the terminal 60 carried by the employee 10. This allows the employee 10 to sit in a seat away from employees who have caused stress in the past, such as employees who type loudly on keyboards or employees who have a strong cigarette smell.

[0018] 2, the terminal 60 is provided with a wireless communication device 11. The wireless communication device 11 outputs a signal that enables detection of the position of the terminal 60, using a communication method that complies with the BLE (Bluetooth Low Energy, "Bluetooth" is a registered trademark) communication standard. Instead of the BLE communication standard, a communication method that complies with the UWB (Ultra Wide Band) communication standard may be used.

[0019] Each of the multiple air conditioners 31 is provided with a wireless communication device 30. Returning to FIG. 1 , the multiple air conditioners 31 are installed, for example, at an appropriate distance from each other on the ceiling 35. Each wireless communication device 30 receives a signal transmitted from the terminal 60 and detects the reception strength thereof using a communication method that complies with the same communication standard as the wireless communication device 11 of the terminal 60. The position of the terminal 60 within the facility can be detected from the reception strength at each wireless communication device 30. Each wireless communication device 30 outputs the reception strength of the signal received from the terminal 60 to the equipment management device 40.

[0020] The equipment management device 40 controls equipment such as air conditioners 31 and wireless communication devices 30 installed in the facility. The equipment management device 40 receives the reception strength of signals received by the wireless communication devices 30 from the wireless communication devices 30, and detects the position of the terminal 60 within the facility from the reception strength of each wireless communication device 30.

[0021] The equipment management device 40 is connected to a communication network NW. The equipment management device 40, a seat recommendation server 50, and a terminal 60, which is a communication terminal, are connected to the communication network NW. The seat recommendation server 50 can acquire location information of the terminal 60 via the equipment management device 40.

[0022] The terminal 60 is, for example, a mobile terminal such as a smartphone or a tablet. The terminal 60 may also be a notebook computer, a personal computer, or the like. The terminal 60 includes a display unit 61 that displays various information. The display unit 61 is, for example, a liquid crystal display unit or a display of the mobile terminal. The employee 10 (user) using the terminal 60 can check the information about the recommended seat determined by the seat recommendation server 50 on the display unit 61.

[0023] Dedicated software may be installed in advance on the terminal 60, and this software may communicate with the seat recommendation server 50 and display information about recommended seats on the display unit 61. Alternatively, a browser may be started on the terminal 60, and communication may be established with the seat recommendation server 50 via the browser, causing information about recommended seats to be displayed on the browser.

[0024] 2, the seat recommendation server 50 includes a CPU (Central Processing Unit) 41 as a processing device, an I / F (Interface) device 44 as a communication device, and a RAM (Random Access Memory) 42, a ROM (Read Only Memory) 43, and a storage device 45 as storage devices. The CPU 41, RAM 42, ROM 43, I / F device 44, and storage device 45 exchange various data via a communication bus 46.

[0025] The CPU 41 loads a program stored in the ROM 43 into the RAM 42 and executes it. The program stored in the ROM 43 describes the processing to be executed by the seat recommendation server 50.

[0026] The I / F device 44 communicates with the equipment management device 40 and the terminal 60. The I / F device 44 is an input / output device for exchanging signals and data with the equipment management device 40 and the terminal 60. The seat recommendation server 50 receives, via the I / F device 44, the location information and the like of the terminal 60 detected by the equipment management device 40.

[0027] The storage device 45 is a storage for storing various types of information, and stores a user table 71, a device table 72, a coefficient table 73, an evaluation DB (database) 81, and the like, which will be described later.

[0028] Although not shown, the equipment management device 40 and the terminal 60 are configured to include a CPU, a RAM, a ROM, an I / F device, and a storage device, similar to the seat recommendation server 50.

[0029] 3 is an example of the user table 71. As described above, the storage device 45 stores the user table 71. The user table 71 is a table that records information about employees (users) who work in the facility.

[0030] The user table 71 records the user IDs assigned to employees (users), the user names corresponding to the user IDs, the departments to which the employees belong within the organization, etc. For example, the user with user ID "ID0001" is "User A," the user with user ID "ID0002" is "User B," and the user with user ID "ID0003" is "User C."

[0031] 4 is an example of the equipment table 72. As described above, the storage device 45 stores the equipment table 72. The equipment table 72 is a table that records information about the equipment installed in the facility.

[0032] The device table 72 records device IDs assigned to devices installed in the facility, and the device names, types, and installation locations of the devices corresponding to the device IDs.

[0033] For example, the device name of device ID "ID0001" is "Air Conditioner A," the type is "Air Conditioner," and the installation location is "Room A." The device name of device ID "ID0005" is "Air Conditioner E," the type is "Air Conditioner," and the installation location is "Room B." Although not shown, it is assumed that the device table 72 can be used to identify where each device is installed in the room.

[0034] Fig. 5 is a display example of an input screen for evaluation information. Evaluation information of a user (employee) regarding a seat can be input on terminal 60. Image 91 shown in Fig. 5 is an input screen for inputting evaluation information, displayed on display unit 61 of terminal 60 used by user A.

[0035] The user inputs the date, time period, target (other person), and stress factor when they felt stressed as evaluation information. As shown in Figure 5, User A inputs the date "2022 / 9 / 30" and the time period "13:15".

[0036] With this input, an image showing the seat status at 1:15 pm on September 30, 2022 will be displayed on the screen. If you do not enter a "date" or "time zone," it is sufficient to display an image showing the seat status at the current time.

[0037] The part of the image labeled "Target" shows a floor plan of a floor in the facility. Within the floor, desks S11 to S16, S21 to S26, S31 to S36, and S41 to S46 are provided that can be used by employees (users).

[0038] The seat corresponding to each desk is indicated by a circle. The gray circle (self) indicates the seating status of the user using terminal 60. In this example, it is shown that user A using terminal 60 is sitting in the seat corresponding to desk S24 at the specified date and time.

[0039] White circles indicate the seating status of users (other users) other than the user using terminal 60. In this example, users other than user A are shown sitting at the seats corresponding to desks S12, S14, S15, S21, S23, S25, S41, S43, and S45. The other seats are vacant.

[0040] This input screen does not display information that can identify the user (user's personal information). For example, the person sitting at desk S24 is user A, and the person sitting at desk S25 is user B. However, only a circle is displayed on the screen, so it is not possible to identify who is sitting at the seat. No personal information is displayed for the other seats either.

[0041] In this example, assume that user A (evaluation user) sitting at desk S24 felt stressed by user B (evaluation target user) sitting at desk S25 at 1:15 PM on September 30, 2022. User B made a loud noise (keystroke sound) when typing on a computer keyboard, and user A felt stressed by the keystroke sound of user B.

[0042] User A can designate (input) user B as a subject who causes stress by clicking on the white circle seat corresponding to desk S25 in image 91.

[0043] Furthermore, the user selects the cause of stress from "Cause." The causes can be selected from "cigarettes," "perfume," "keystroke sounds," "chewing sounds," and "gaze." If the user wants to input another cause, they can enter the cause in the "Other" field. In this example, User A selects "keystroke sounds."

[0044] "Tobacco" indicates that the rating user is stressed by the tobacco smell of the user being rated. "Perfume" indicates that the rating user is stressed by the perfume smell of the user being rated. "Keystroke sound" indicates that the rating user is stressed by the sound of the user being rated typing on the keyboard of the terminal of the user being rated.

[0045] "Chewing sound" indicates that the evaluator feels stressed by the chewing sound of the user being evaluated while eating at their seat. "Gaze" indicates that the evaluator feels stressed by the gaze of the user being evaluated (such as making eye contact).

[0046] When the "Send" button is clicked on the screen, the evaluation information is transmitted from the terminal 60 to the seat recommendation server 50. In this example, the evaluation information transmitted from the terminal 60 to the seat recommendation server 50 includes the date "2022 / 9 / 30", the time period "13:15", the evaluating user "User A", the user to be evaluated "User B", and the factor "keystroke sound".

[0047] The seat recommendation server 50, which has received the evaluation information, calculates the distance between the evaluating user and the user to be evaluated (hereinafter referred to as the "evaluation distance") from the floor layout information. In this example, it is assumed that the evaluation distance between user A (S24) and user B (S25) is calculated to be "1.4 m."

[0048] 6 is a diagram illustrating the evaluation DB 81. The evaluation DB 81 is a database that stores evaluation information acquired from the terminal 60. The evaluation information includes factors that cause stress felt by the employee being evaluated (evaluation user) performing the evaluation toward the employee to be evaluated (evaluation target user). The factors include olfactory information, auditory information, visual information, etc. Specifically, the factors include at least one of the following: cigarette odor, perfume odor, keystroke sounds, chewing sounds, and gaze.

[0049] The seat recommendation server 50 accumulates the evaluation information received from the terminal 60 in the evaluation DB 81. The evaluation DB 81 records the evaluating user, the user to be evaluated, the factors, and the evaluation distance for each date and time period.

[0050] In the above example, the evaluation information recorded in the evaluation DB81 includes the date "2022 / 9 / 30", the time period "13:15", the evaluating user "User A", the user to be evaluated "User B", the factor "keystroke sound", and the evaluation distance (unit: m) "1.4".

[0051] In the example of Figure 6, other evaluation information recorded in the evaluation DB81 includes the date "2022 / 10 / 3", time period "15:20", evaluating user "User C", user to be evaluated "User D", factor "keystroke sound", and evaluation distance "2.0".

[0052] In addition, the evaluation information recorded in the evaluation DB81 includes the date "2022 / 10 / 5", the time period "12:30", the evaluating user "User E", the user to be evaluated "User F", the factor "chewing sound", and the evaluation distance "1.4".

[0053] Thus, in the example of Figure 6, it is shown that User A feels stressed by the keystroke sound of User B, User C feels stressed by the keystroke sound of User D, and User E feels stressed by the keystroke sound of User F.

[0054] In the first embodiment, the seat recommendation server 50 first acquires location information of the user (employee) sitting in the seat. Then, the seat recommendation server 50 is configured to determine a recommended seat based on the evaluation information stored in the evaluation DB 81 and the location information of the user sitting in the seat. The I / F device 44 of the seat recommendation server 50 transmits the determined recommended seat to the terminal 60. The terminal 60 displays the recommended seat.

[0055] Specifically, recommended seats are determined so that users who have given negative ratings are not seated close to each other. In the above example, recommended seats are determined so that users A and B are not seated close to each other. Similarly, recommended seats are determined so that users C and D are not seated close to each other. Furthermore, recommended seats are determined so that users E and F are not seated close to each other.

[0056] More specifically, the recommended seats are determined so that the distance between the users is not equal to or less than the evaluation distance recorded in the evaluation DB multiplied by the weighting coefficient. The weighting coefficient (hereinafter simply referred to as the “coefficient”) is stored in the coefficient table 73.

[0057] 7 is an example of the coefficient table 73. As described above, the storage device 45 stores the coefficient table 73. The coefficients vary depending on the factors. The coefficient table 73 is a table that records coefficients corresponding to each factor.

[0058] For example, the coefficient of the factor "cigarettes" is "K1." The coefficient of the factor "perfume" is "K2." The coefficient of the factor "typing sounds" is "K3." The coefficient of the factor "chewing sounds" is "K4." The coefficient of the factor "gaze" is "K5."

[0059] 8 to 13 are diagrams for explaining how to determine recommended seat candidates. In the following example, an example of determining a recommended seat for user A and a recommended seat for user B will be described. It is assumed that neither user A nor user B has arrived at work yet.

[0060] 6, in the evaluation DB 81, user A gives a negative evaluation to user B regarding the factor "keystroke sound" when the distance between them is "1.4 m." In this case, the weighting coefficient "K3" for the factor "keystroke sound" recorded in the coefficient table 73 is used (FIG. 7).

[0061] In determining the recommended seats, the recommended seats for user A and user B are determined so that the distance between them is not less than weighting coefficient "K3" x distance "1.4 m." In this example, seats where the distance between them is less than weighting coefficient "K3" x distance "1.4 m" are assumed to be seats where user A and user B are seated next to each other or directly opposite each other.

[0062] For example, in the example of FIG. 5, if the recommended seat for user A is the seat at desk S24, the seats at adjacent desks S23 and S24 and the seat directly in front of it at desk S14 are not determined as recommended seats for user B.

[0063] In Fig. 8, recommended seat candidates for user A are selected. In this case, multiple seats (five in this example) are randomly selected from all available seats. As shown in image 92 in Fig. 8, seats at desks S14, S22, S24, S43, and S46 are selected as recommended seat candidates for user A.

[0064] In Figure 9, recommended seat candidates for user B are selected. In this case, multiple seats (five) are randomly selected from all available seats. As shown in image 93 in Figure 9, seats at desks S21, S23, S31, S33, and S44 are selected as recommended seat candidates for user B.

[0065] In the example shown in Figures 8 and 9, user B candidates S21 and S23 are selected next to user A candidate S22, user B candidate S23 is selected next to user A candidate S24, user B candidate S44 is selected next to user A candidate S43, and user B candidate S33 is selected directly in front of user A candidate S43.

[0066] In this way, since the candidates for user A and user B are next to each other or directly opposite each other, the selection of the recommended seat candidates for user A and the selection of the recommended seat candidates for user B are redone.

[0067] After multiple selections of recommended seat candidates are performed, the recommended candidates for user A and user B that are finally determined are as shown in FIGS.

[0068] As shown in image 94 of Fig. 10, seats at desks S11, S15, S24, S41, and S43 have been determined as recommended seats for user A. On the other hand, as shown in image 95 of Fig. 11, seats at desks S13, S22, S26, S36, and S45 have been determined as recommended seats for user B. In the finally determined recommended seats, the recommended seats for user A and the recommended seats for user B are not adjacent to each other or directly opposite each other.

[0069] In this way, when the evaluation information recorded in the evaluation DB81 is an evaluation made by a first evaluating user (e.g., user A) on a first user to be evaluated (e.g., user B), the seat recommendation server 50 determines recommended seats for the first evaluating user (user A) and the first user to be evaluated (user B) so that the distance between the first evaluating user (user A) and the first user to be evaluated (user B) is not less than a predetermined distance (e.g., weighting coefficient "K3" x evaluation distance "1.4 m").

[0070] The seat recommendation server 50 determines the predetermined distance (K3 x 1.4 m) based on the evaluation distance (1.4 m), which is the distance between the first evaluating user (user A) and the first user to be evaluated (user B) when the first evaluating user (user A) evaluated the first user to be evaluated (user B).

[0071] The predetermined distance (K3 × 1.4 m) is calculated by multiplying the evaluation distance (1.4 m) by a weighting coefficient (K3). The coefficient differs depending on whether the factor used when the first evaluating user (user A) evaluated the first evaluation target user (user B) was the first factor (e.g., keystroke sound) or the second factor (e.g., chewing sound).

[0072] For example, if keystroke sounds resonate more easily than chewing sounds in a facility, the coefficient for keystroke sounds, "K3," can be set greater than the coefficient for chewing sounds, "K4." By doing so, users who make louder keystroke sounds can be kept farther away than users who make louder chewing sounds. If odors propagate more easily than sound, the coefficients for cigarettes, "K1," and perfume, "K2," can be set larger. Furthermore, if line of sight is a factor, the distance from the user to be evaluated can also be determined taking into account the direction. For example, if line of sight is a factor, the recommended seat can be determined so that the distance between the user to be evaluated and the user is greater when the user is directly in front of the user to be evaluated than when the user is side-by-side with the user to be evaluated.

[0073] If the user specifies conditions for seats, seats are preferentially selected according to the specified conditions. For example, if user A specifies the use of a large monitor as a condition, seats can be preferentially selected from among seats at desks with large monitors. Other conditions that may be specified include the number of monitors and seat temperature (e.g., a warm seat is desired). Furthermore, the method for determining recommended seats is not limited to the method described above, and any algorithm may be used to determine recommended seats.

[0074] Next, a case where the user sits in a seat other than the recommended seat after the recommended seat has been determined as shown in FIGS. 10 and 11 will be described with reference to FIGS. 12 and 13. FIG.

[0075] It is possible that some users may not check the recommended seat or may ignore it and work in a location other than the recommended seat. Also, if users start work at different times due to staggered commute times, the system will reflect the location information of the user who arrived first and adjust the recommended seats for other users.

[0076] In this embodiment, the seat recommendation server 50 is configured to redetermine the recommended seat for a user who is not sitting in a seat when it detects that the user is sitting in a seat other than the recommended seat. The I / F device 44 of the seat recommendation server 50 notifies the terminal 60 used by the user whose recommended seat has been changed by the redetermining of the change in the recommended seat.

[0077] FIG. 12 shows the recommended seats for user A shown in FIG. 10. In this state, assume that user B sits at desk S23. The seat at desk S23 is not a recommended seat for user B shown in FIG. 11. As a result, if user A were to sit at desk S24, user A and user B would end up sitting next to each other (the distance between them would be equal to or less than weighting coefficient "K3" x distance "1.4 m").

[0078] To avoid this situation, the system re-determines the recommended seat for user A. In this case, the system randomly selects recommended seat candidates for user A again. The system repeats the selection of recommended seat candidates for user A so that the recommended seat for user A and the seat for user B are not adjacent to or directly opposite each other.

[0079] The final recommended seats for user A are shown in image 97 of Figure 13. The seats at desks S11, S15, S26, S41, and S43 have been determined as the recommended seats for user A. These seats are not adjacent to or directly opposite user B's seat at desk S23.

[0080] When the recommended seat is changed in this way, the user is notified of the change in the recommended seat. In this example, for example, a message 101 saying "The recommended seat has been changed" is sent to the terminal 60 used by user A.

[0081] The message 101 may be sent as an email to user A that can be received by terminal 60. Alternatively, some software capable of notifying information sent from seat recommendation server 50 in real time may be installed on terminal 60, and this software may display message 101 in real time on terminal 60. Upon seeing this notification, user A checks the newly determined recommended seat on terminal 60.

[0082] The processing executed by the seat recommendation system 100 will be explained below using a flowchart. FIG. 14 is a flowchart of the evaluation information processing. Hereinafter, "step" will also be simply referred to as "S." The evaluation information processing includes evaluation information processing (server) executed by the seat recommendation server 50 and evaluation information processing (terminal) executed by the terminal 60. Both processes may be started periodically (for example, every 100 msec).

[0083] When the evaluation information processing (terminal) starts, the terminal 60 determines in S201 whether or not there is a request to transmit evaluation information. If the determination in S201 is YES, the terminal 60 proceeds to the process in S202, and if the determination in S201 is NO, the evaluation information processing (terminal) ends.

[0084] In S202, the terminal 60 transmits the evaluation information to the seat recommendation server 50 and ends the evaluation information processing (terminal). When the "Send" button is pressed on the screen shown in Fig. 5, a request to transmit the evaluation information is generated (YES in S201), and as a result, the evaluation information set on the screen is transmitted to the seat recommendation server 50 (S202).

[0085] On the other hand, when the evaluation information processing (server) starts, the seat recommendation server 50 determines in S101 whether or not evaluation information has been received from the terminal 60. If the seat recommendation server 50 determines YES in S101, it proceeds to S102, and if the seat recommendation server 50 determines NO in S101, it proceeds to S103.

[0086] In S102, the seat recommendation server 50 updates the evaluation DB 81. As a result, the evaluation information set on the screen shown in FIG. 5 is added to the evaluation DB 81 shown in FIG.

[0087] In S103, the seat recommendation server 50 determines whether the number of accumulated evaluation information items exceeds a predetermined upper limit (for example, 1,000 items). If the determination in S103 is YES, the seat recommendation server 50 proceeds to S104, and if the determination in S103 is NO, the seat recommendation server 50 terminates the evaluation information processing (server). In S104, the seat recommendation server 50 deletes the oldest evaluation information item from the evaluation DB 81, and terminates the evaluation information processing (server).

[0088] In this way, when the number of pieces of evaluation information stored in the evaluation DB 81 exceeds a predetermined upper limit (1000 pieces), the seat recommendation server 50 deletes the oldest piece of evaluation information from the evaluation DB 81. For example, if 1000 pieces of evaluation information are currently recorded in the evaluation DB 81 and the number of pieces of evaluation information becomes 1001 by adding evaluation information, the oldest piece of evaluation information is deleted.

[0089] 15 is a flowchart of the recommended seat processing. The recommended seat processing includes a recommended seat processing (server) executed by the seat recommendation server 50 and a recommended seat processing (terminal) executed by the terminal 60. Both processes may be started periodically (for example, every 100 msec).

[0090] Although not shown, the user can use the terminal 60 to send a display request for recommended seats to the seat recommendation server 50, thereby displaying the recommended seats on the terminal 60.

[0091] When the recommended seat processing (terminal) starts, the terminal 60 determines in S401 whether or not there has been a request to display recommended seats. For example, when a "Display recommended seats" button displayed on the screen of the terminal 60 is clicked, a request to display recommended seats is generated.

[0092] If the determination in S401 is YES, the terminal 60 proceeds to the process in S402, and if the determination in S401 is NO, the terminal 60 proceeds to the process in S403. In S402, the terminal 60 transmits a display request for recommended seats to the seat recommendation server 50.

[0093] On the other hand, when the recommended seat processing (server) starts, the seat recommendation server 50 determines in S301 whether or not a display request for recommended seats has been received from the terminal 60. If the seat recommendation server 50 determines YES in S301, it proceeds to S302, and if the seat recommendation server 50 determines NO in S301, it proceeds to S303.

[0094] In S302, the seat recommendation server 50 executes the recommended seat determination process (see FIG. 16). As a result, recommended seat display information for the user who uses the terminal 60 that has made the display request is generated. In S303, the seat recommendation server 50 transmits the recommended seat display information to the terminal 60 that has made the display request, and ends the recommended seat process (server).

[0095] Meanwhile, in S403, the terminal 60 determines whether or not recommended seat display information has been received from the seat recommendation server 50. If the determination in S403 is YES, the terminal 60 proceeds to processing in S404, and if the determination in S403 is NO, the terminal 60 terminates the recommended seat processing (terminal).

[0096] In S404, the terminal 60 executes the display process of the recommended seat display information and ends the recommended seat process (terminal). As a result, the recommended seats as shown in FIGS.

[0097] 16 is a flowchart of the recommended seat determination process. When the recommended seat determination process starts, the seat recommendation server 50 acquires the position information of the user sitting in the seat in S701. For example, in the example of FIG. 8, if user X who has already arrived at work is sitting at desk S11, the position information of the seat at desk S11 where user X is sitting is acquired as the position information.

[0098] In S702, the seat recommendation server 50 acquires all the evaluation information from the evaluation DB 81. For example, in this example, it is assumed that evaluation information of user A on user B, evaluation information of user C on user D, evaluation information of user E on user F, etc. are acquired (see FIG. 6).

[0099] In S703, the seat recommendation server 50 extracts users who are not sitting in seats as determination target users. In the above example, users other than user X who is sitting in a seat (users A to F, etc.) are extracted from the user table 71 as determination target users.

[0100] In S704, the seat recommendation server 50 determines recommended seat candidates for the determination target user. For example, recommended seat candidates for user A are determined as shown in Fig. 8, and recommended seat candidates for user B are determined as shown in Fig. 9.

[0101] The seat recommendation server 50 executes proximity determination processing (see FIG. 17) in S705. In the above example, it is determined whether or not there are adjacent seats between the recommended seat candidates for user A and the recommended seat candidates for user B. In this example, it is also determined whether or not there are adjacent seats between the recommended seat candidates for user C and the recommended seat candidates for user D, and whether or not there are adjacent seats between the recommended seat candidates for user E and the recommended seat candidates for user F.

[0102] In S706, the seat recommendation server 50 determines whether or not there are adjacent seats. If the determination in S706 is YES, the seat recommendation server 50 proceeds to S707, and if the determination in S707 is NO, the process returns to S704.

[0103] In the example shown in FIGS. 8 and 9, for example, seat S22 for user A is an adjacent seat to seat S21 for user B. In this case, the processes of S704 to S706 are repeated until there are no more adjacent seats. As a result, as shown in the examples of FIGS. 10 and 11, it is assumed that there are no more adjacent seats between the recommended seat candidates for user A and the recommended seat candidates for user B. It is also assumed that there are no more adjacent seats between the recommended seat candidates for user C and the recommended seat candidates for user D, and between the recommended seat candidates for user E and the recommended seat candidates for user F. As a result, the process proceeds to S707.

[0104] In S707, the seat recommendation server 50 sets the recommended seat candidate as a recommended seat and ends the recommended seat determination process. In this example, the recommended seat for user A is determined as shown in Figure 10, and the recommended seat for user B is determined as shown in Figure 10.

[0105] 17 is a flowchart of the proximity determination process. When the proximity determination process starts, the seat recommendation server 50 selects one undetermined determination target user in S801. In this example, it is assumed that users A to F are selected in order as the undetermined determination target users.

[0106] In S802, the seat recommendation server 50 determines whether the selected user is recorded as a rating user in the rating DB 81. If the determination in S802 is YES, the seat recommendation server 50 proceeds to S803, and if the determination in S802 is NO, the seat recommendation server 50 proceeds to S806. For example, if user A is selected, the seat recommendation server 50 determines that user A is recorded as a rating user in the rating DB 81 (FIG. 6).

[0107] In S803, the seat recommendation server 50 extracts the corresponding user to be evaluated. When user A is selected, user B is recorded in the evaluation DB 81 as the user to be evaluated corresponding to user A (FIG. 6).

[0108] In S804, the seat recommendation server 50 determines whether or not there is a seat in the recommended seat candidates where the distance between the rating user and the user to be rated is equal to or less than the evaluation distance x weighting coefficient. If the seat recommendation server 50 determines YES in S804, it proceeds to S805, and if the seat recommendation server 50 determines NO in S804, it proceeds to S806.

[0109] In the above example, the factor for user A (evaluating user)'s evaluation of user B (evaluation target user) is "keystroke sound," and the evaluation distance is "1.4 m" (Fig. 6). The weighting coefficient for keystroke sound is "K3" (Fig. 7). In this case, it is determined whether or not there is a seat in the recommended seat candidate list where the distance between user A and user B is 1.4 m x K3 or less.

[0110] In S805, the seat recommendation server 50 determines that there are adjacent seats between the evaluating user and the user to be evaluated, and ends the proximity determination process. In this example, when the seats are adjacent to each other or directly opposite each other, the distance between user A and user B is 1.4 m x K3 or less. In the examples of Figures 8 and 9, it is determined that there are adjacent seats between user A and user B. In the examples of Figures 10 and 11, it is not determined that there are adjacent seats between user A and user B.

[0111] In S806, the seat recommendation server 50 determines whether all the users to be determined have been determined. If the determination in S806 is YES, the seat recommendation server 50 ends the proximity determination process, and if the determination in S806 is NO, the process returns to S801.

[0112] 18 is a flowchart of the redetermining process. The redetermining process includes a redetermining process (server) executed by the seat recommendation server 50 and a redetermining process (terminal) executed by the terminal 60. Either process may be started periodically (for example, every 100 msec).

[0113] When the redetermining process (server) starts, the seat recommendation server 50 determines in S501 whether or not there is a user sitting in a seat other than the recommended seat. For example, in the example of Fig. 12, user B is sitting in a seat other than the recommended seat.

[0114] If the seat recommendation server 50 determines YES in S501, it proceeds to S502, and if the seat recommendation server 50 determines NO in S501, it proceeds to S503. In S502, the seat recommendation server 50 executes a recommended seat determination process (FIG. 16).

[0115] In the above example, when user B sits in a seat other than the recommended seat, the recommended seat determination process is executed again, and on the assumption that user B sits at desk S23, at least the recommended seat for user A is redetermined. In this example, the recommended seat for user A is redetermined as shown in Figure 13. In S503, the seat recommendation server 50 notifies the user whose recommended seat has been changed of the recommended seat change information, and terminates the redetermining process (server).

[0116] On the other hand, when the redetermining process (terminal) starts, the terminal 60 determines in S601 whether or not recommended seat change information has been received. If the terminal 60 determines YES in S601, it proceeds to S602, and if the terminal 60 determines NO in S601, it terminates the redetermining process (terminal). In S602, the terminal 60 executes a display process for the recommended seat change information, and terminates the redetermining process (terminal).

[0117] In this example, the seat recommendation server 50 transmits a message 101 stating "recommended seat has been changed" to the terminal 60 of user A as recommended seat change information (see FIG. 13). User A receives the message 101 on the terminal 60. User A can then check the re-determined recommended seat on the terminal 60.

[0118] As described above, according to the first embodiment, the seat recommendation system 100 includes a seat recommendation server 50 and a terminal 60. The seat recommendation server 50 is a server that recommends a seat for an employee 10 (user) working in a facility to work in on the day. The seat recommendation server 50 includes a CPU 41 as a processing device, an I / F device 44 as a communication device, and a storage device 45. The CPU 41 determines a recommended seat to be recommended to the user. The I / F device 44 communicates with a terminal 60 that can display the recommended seat and that can input user evaluation information regarding the seat. The storage device 45 stores an evaluation DB 81 that accumulates evaluation information acquired from the terminal 60. The CPU 41 acquires location information of the user sitting in the seat. The CPU 41 determines a recommended seat based on the accumulated evaluation information and location information. The I / F device 44 transmits the determined recommended seat to the terminal 60.

[0119] In the first embodiment, evaluations between users (employees) are reflected only to the parties involved. Specifically, if user A gives a negative evaluation of user B, the recommended seat is determined so that user A's seat is not close to user B's seat. In this way, in a facility that has introduced a free address system, it is possible to present recommended seats that will minimize stress experienced by other users. Reducing stress contributes to improving user productivity.

[0120] The terminal 60 displays an input screen for inputting evaluation information. Information (personal information) that can identify the user is not displayed on the input screen. As a result, even if a user tries to input a negative evaluation of another user from the terminal 60, the content of the evaluation will not be known to a third party.

[0121] The evaluation information includes the causes of stress that the evaluating user (evaluated employee) who performs the evaluation felt toward the user to be evaluated (evaluated employee), which is the subject of evaluation. This makes it possible to keep users who feel stressed away.

[0122] The factors include at least one of the following: cigarette smell, perfume smell, typing sounds, chewing sounds, and gaze. This makes it possible to reliably reduce stress caused by each of the factors: cigarette smell, perfume smell, typing sounds, chewing sounds, and gaze.

[0123] When an evaluation made by user A (evaluating user) to user B (evaluation target user) is recorded in the evaluation DB 81 as evaluation information, the CPU 41 determines recommended seats for user A and user B so that the distance between user A and user B is not less than a predetermined distance (1.4 m×K3). This makes it possible to reliably keep away users who have previously experienced stress.

[0124] The CPU 41 determines the predetermined distance (1.4 m×K3) based on the evaluation distance (1.4 m) between user A (evaluating user) and user B when user A (evaluating user) evaluated user B (evaluation target user). This allows the CPU 41 to distance users who have felt stressed in the past, taking into consideration the distance at which they actually felt stressed in the past.

[0125] The predetermined distance (1.4 m x K3) is calculated by multiplying the evaluation distance (1.4 m) by a weighting coefficient (coefficient: K3). The weighting coefficient (K3) differs depending on whether the factor when user A (evaluating user) evaluates user B (evaluation target user) is the first factor (keystroke sound) or the second factor (chewing sound). This allows the distance to be appropriately adjusted depending on the factor.

[0126] When the CPU 41 detects an employee sitting in a seat other than the recommended seat, it re-determines the recommended seat for the employee who is not sitting in the seat. The I / F device 44 notifies the terminal 60 used by the employee whose recommended seat has been changed by the re-determination of the change in the recommended seat. This ensures that even if the recommended seat has been changed, the change can be reliably understood.

[0127] When the number of pieces of evaluation information stored in the evaluation DB 81 exceeds a predetermined upper limit, the CPU 41 deletes the oldest piece of evaluation information from the evaluation DB 81. This reduces the load on the storage capacity of the seat recommendation server 50.

[0128] Furthermore, in this embodiment, the user's location is measured by utilizing an air conditioner 31, which is an existing facility installed in the facility. This reduces the cost of installing the facility compared to installing a dedicated device for measuring the user's location in the facility. Note that any device that can measure the user's location may be used, not limited to an air conditioner.

[0129] Second Embodiment 19 is a diagram for explaining an evaluation DB 81 according to the second embodiment. In the following explanation, only the differences from the first embodiment will be explained, and explanations of the same points as in the first embodiment will be omitted. The configurations and modifications explained in the first embodiment can be applied to any of the configurations and modifications explained in the second embodiment.

[0130] The example of the evaluation DB 81 shown in Fig. 19 stores the same data as the example of the evaluation DB 81 shown in Fig. 6. As in the example of Fig. 6, it is shown that User A feels stressed by the keystroke sounds of User B, User C feels stressed by the keystroke sounds of User D, and User E feels stressed by the chewing sounds of User F.

[0131] In the first embodiment, recommended seats are determined so that users who have given negative ratings are not seated close to each other. In contrast, the second embodiment differs from the first embodiment in that the rating information of users who have given negative ratings is shared with other users.

[0132] In the second embodiment, when a first rating user's rating of a first user to be rated based on a first factor is recorded in the rating DB 81 and a second rating user's rating of a second user to be rated based on the first factor is recorded in the rating DB 81, the seat recommendation server 50 is configured to determine recommended seats for the first rating user, the first rating user, the second rating user, and the second rating user so that the distance between the first rating user and the first user to be rated is not equal to or less than the first distance, the distance between the second rating user and the second user to be rated is not equal to or less than the second distance, the distance between the first rating user and the second user to be rated is not equal to or less than the third distance, and the distance between the second rating user and the first user to be rated is not equal to or less than the fourth distance. This will be described in detail below.

[0133] In the first embodiment, the recommended seats for both the rating user recorded in the rating DB 81 and the rating target user who was rated by the rating user were determined taking into consideration the distance between them (so that the distance was not less than a certain distance). Specifically, the recommended seats for users A and B were determined taking into consideration the distance between users A and B, the recommended seats for users C and D were determined taking into consideration the distance between users C and D, and the recommended seats for users E and F were determined taking into consideration the distance between users E and F.

[0134] In contrast, in the second embodiment, when negative evaluations are made due to the same factor, this evaluation information is shared to determine a recommended seat. For example, in this example, the factor that causes stress that user A feels towards user B and the factor that causes stress that user C feels towards user D are both keystroke sounds. From this, it is possible that user A feels stressed by the keystroke sounds of user D, and user C feels stressed by the keystroke sounds of user B.

[0135] For this reason, in the second embodiment, the recommended seats for users A and D are determined taking into consideration the distance between users A and D, and the recommended seats for users C and B are determined taking into consideration the distance between users C and B. In this way, even if there is little evaluation information recorded in the evaluation DB 81, it is possible to suitably determine recommended seats that can reduce stress.

[0136] In the example of Figure 19, with regard to the factor "keystroke sound," the recommended seat is determined so that user A's seat is not closer than a certain distance to either user B or D's seat, and the recommended seat is determined so that user C's seat is not closer than a certain distance to either user B or D's seat.

[0137] 19, if the causes of stress are different, the evaluation information of users is not shared. For example, regarding chewing sounds, the recommended seats for users E and F are determined taking into consideration the distance between user E and user F. However, when determining the recommended seats for users E and F, the distance between them and users A to D, which have a different cause (keystroke sounds), is not taken into consideration.

[0138] The processing in the second embodiment will be described below using flowcharts. The processing in the flowcharts shown in Figures 14 to 16 and 18 is the same in the first and second embodiments. The flowchart of the proximity determination processing shown in Figure 17 is as follows in the second embodiment.

[0139] 20 is a flowchart of the proximity determination process according to the second embodiment. When the proximity determination process starts, the seat recommendation server 50 selects one undetermined determination target user in S901. In this example, it is assumed that users A to F are selected in order as the undetermined determination target users.

[0140] In S902, the seat recommendation server 50 determines whether the selected user is recorded as an evaluated user. If the determination in S902 is YES, the seat recommendation server 50 proceeds to S903, and if the determination in S902 is NO, the seat recommendation server 50 proceeds to S907.

[0141] For example, when user A is selected, the evaluation DB 81 records the factor "keystroke sound" and the evaluation distance "1.4 m" as the evaluation of user B (evaluation target user) by the selected user A (evaluation user) (FIG. 19). The weighting coefficient for the keystroke sound is "K3" (FIG. 7).

[0142] In S903, the seat recommendation server 50 extracts all evaluation information whose factors match the factors in the evaluation made by the selected user as the evaluating user. In this example, all evaluation information whose factor is "keystroke sound" is extracted. As a result, in the example of FIG. 19, the factor "keystroke sound" and evaluation distance "2.0 m" are further extracted as the evaluation of user C (evaluating user) for user D (user to be evaluated).

[0143] In S904, the seat recommendation server 50 extracts all users to be rated that correspond to the extracted evaluation information. In the above example, users B and D are extracted as users to be rated.

[0144] In S905, the seat recommendation server 50 determines whether there is a seat (recommended seat candidate) where the distance between the rating user and any one of all extracted users to be rated is equal to or less than the evaluation distance multiplied by the weighting coefficient.

[0145] In this example, it is determined whether there are any recommended seat candidates where the distance between user A (evaluating user) and user B (user to be evaluated) is 1.4 m (evaluation distance) × K3 (weighting coefficient) or less. Furthermore, it is determined whether there are any recommended seat candidates where the distance between user A (evaluating user) and user D (user to be evaluated) is 1.4 m (evaluation distance) × K3 (weighting coefficient) or less. Note that in the latter calculation, the evaluation distance recorded between user C and user D, "2.0 m," may be used.

[0146] If the seat recommendation server 50 determines YES in S905, it proceeds to S906, and if the seat recommendation server 50 determines NO in S905, it proceeds to S907. In S906, the seat recommendation server 50 determines that there are adjacent seats and ends the proximity determination process. In the above example, if it is determined that there are adjacent seats between user A and user B, or if it is determined that there are adjacent seats between user A and user D, it is determined that there are "adjacent seats."

[0147] In S907, the seat recommendation server 50 determines whether all the users to be determined have been determined. If the determination in S907 is YES, the seat recommendation server 50 ends the proximity determination process, and if the determination in S907 is NO, the process returns to S901.

[0148] In S901, if user C is selected, it is determined that there are adjacent seats between user C and user B, or if it is determined that there are adjacent seats between user C and user D, it is determined that there are "adjacent seats."

[0149] Thus, in the second embodiment, when user A's evaluation of user B based on the factor "keystroke sound" is recorded in the evaluation DB 81, and user C's evaluation of user D based on the same factor "keystroke sound" is also recorded in the evaluation DB 81, the seat recommendation server 50 determines recommended seats for users A to D so that the distance between user A and user B is not less than 1.4m x K3, the distance between user A and user D is not less than 1.4m x K3 (or 2.0m x K3), the distance between user C and user D is not less than 2.0m x K3, and the distance between user C and user B is not less than 2.0m x K3 (or 1.4m x K3).

[0150] By doing this, even in a facility that has introduced a free address system and there is little evaluation information recorded in the evaluation DB 81, it is possible to present as recommended seats those that can minimize stress experienced by other users. Reducing stress contributes to improving user productivity.

[0151] [Note] The above-described embodiment is a specific example of the following additional notes.

[0152] (Appendix 1) A seat recommendation server that recommends seats for employees working in a facility to work on the day, a processor for determining a seat recommendation for the employee; a communication device that communicates with a terminal device that can display the recommended seats and that can input the employee's evaluation information regarding the seats; a storage device that stores an evaluation database that accumulates the evaluation information acquired from the terminal device, The processing device includes: Acquire location information of the employee sitting in the seat; determining the recommended seat based on the accumulated evaluation information and location information; The communication device transmits the determined recommended seats to the terminal device.

[0153] (Appendix 2) The seat recommendation server according to claim 1, wherein the evaluation information includes the stress factors that the evaluating employee felt toward the employee being evaluated.

[0154] (Appendix 3) The seat recommendation server described in Appendix 2, wherein when an evaluation made by a first evaluated employee on a first evaluated employee is recorded in the evaluation database as the evaluation information, the processing device determines the recommended seats for the first evaluated employee and the first evaluated employee so that the distance between the first evaluated employee and the first evaluated employee is not less than a predetermined distance.

[0155] (Appendix 4) The seat recommendation server described in Appendix 3, wherein the processing device determines the predetermined distance based on an evaluation distance, which is the distance between the first evaluated employee and the first evaluated employee when the first evaluated employee evaluated the first evaluated employee.

[0156] (Appendix 5) The predetermined distance is calculated by multiplying the evaluation distance by a coefficient, The seat recommendation server according to claim 3 or 4, wherein the coefficient differs depending on whether the factor used by the first evaluation employee to evaluate the first evaluation target employee is a first factor or a second factor.

[0157] (Appendix 6) The seat recommendation server of Appendix 2, wherein when a first evaluated employee's evaluation of a first evaluated employee based on a first factor is recorded in the evaluation database and a second evaluated employee's evaluation of a second evaluated employee based on the first factor is recorded in the evaluation database, the processing device determines the recommended seats for the first evaluated employee, the first evaluated employee, the second evaluated employee, and the second evaluated employee so that the distance between the first evaluated employee and the first evaluated employee is not less than a first distance, the distance between the second evaluated employee and the second evaluated employee is not less than a second distance, the distance between the first evaluated employee and the second evaluated employee is not less than a third distance, and the distance between the second evaluated employee and the first evaluated employee is not less than a fourth distance.

[0158] (Appendix 7) 7. The seat recommendation server according to any one of appendices 2 to 6, wherein the factors include at least one of cigarette odor, perfume odor, keystroke sounds, chewing sounds, and line of sight.

[0159] (Appendix 8) When the processing device detects that the employee is sitting in a seat other than the recommended seat, the processing device re-determines the recommended seat for the employee who is not sitting in a seat; The seat recommendation server according to any one of Supplementary Note 2 to Supplementary Note 7, wherein the communication device notifies the terminal device used by the employee whose recommended seat has been changed by redetermining of the change in the recommended seat.

[0160] (Appendix 9) The seat recommendation server according to any one of appendices 2 to 8, wherein the processing device deletes the oldest piece of evaluation information from the evaluation database when the number of pieces of evaluation information stored in the evaluation database exceeds a predetermined upper limit.

[0161] (Appendix 10) a seat recommendation server as described in Appendix 1; and the terminal device.

[0162] (Appendix 11) the terminal device displays an input screen for inputting the evaluation information, 11. The seat recommendation system of claim 10, wherein the input screen does not display information that can identify the employee.

[0163] (Appendix 12) A seat recommendation method for recommending seats for employees working in a facility to work on the day, comprising: determining a seat recommendation to recommend to the employee; a step of communicating with a terminal device capable of displaying the recommended seats and capable of inputting the employee's evaluation information regarding the seats; storing the evaluation information acquired from the terminal device in an evaluation database; The determining step includes: acquiring location information of the employee sitting in the seat; determining the recommended seat based on the accumulated rating information and location information; The seat recommendation method, wherein the communicating step includes a step of transmitting the determined recommended seats to the terminal device.

[0164] The embodiments disclosed herein are merely examples and are not limited to the above. The scope of the present invention is defined by the claims, and it is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0165] 10 Employee, 11, 30 Wireless communication device, 31 Air conditioner, 35 Ceiling, 36 Floor, 40 Equipment management device, 41 CPU, 42 RAM, 43 ROM, 44 I / F device, 45 Storage device, 46 Communication bus, 50 Seat recommendation server, 60 Terminal, 61 Display unit, 71 User table, 72 Equipment table, 73 Coefficient table, 81 Evaluation DB, 91-97 Images, 100 Seat recommendation system, 101 Message, S11-S16, S21-S26, S31-S36, S41-S46 Desk, NW Communication network.

Claims

1. A seat recommendation server that recommends seats for employees working in a facility to work on the day, a processor for determining a seat recommendation for the employee; a communication device that communicates with a terminal device that can display the recommended seating and that can input, as evaluation information, the causes of stress felt by the employee being evaluated regarding the employee being evaluated; a storage device for storing an evaluation database that stores the evaluation information acquired from the terminal device and an evaluation distance, which is the distance between the location of the evaluation employee and the location of the employee to be evaluated, in association with the evaluation employee and the employee to be evaluated; The processing device includes: acquiring the position of the employee sitting in the seat as position information; determining the recommended seat using a distance between the position of the candidate seat for the employee to whom the recommended seat is to be recommended and the position of the employee from which the position information was acquired, and a correspondence relationship between the evaluation information and the evaluation distance stored in the evaluation database; The communication device transmits the determined recommended seats to the terminal device.

2. 2. The seat recommendation server of claim 1, wherein, when an evaluation made by a first evaluated employee to a first evaluated employee is recorded in the evaluation database as the evaluation information, the processing device determines the recommended seats for the first evaluated employee and the first evaluated employee so that the distance between the first evaluated employee and the first evaluated employee is not less than a predetermined distance.

3. 3. The seat recommendation server according to claim 2, wherein the processing device determines the predetermined distance based on the evaluation distance, which is the distance between the first evaluated employee and the first employee to be evaluated when the first evaluated employee evaluated the first employee to be evaluated.

4. The predetermined distance is calculated by multiplying the evaluation distance by a coefficient, The seat recommendation server according to claim 3 , wherein the coefficient differs depending on whether the factor used by the first evaluating employee to evaluate the first evaluation target employee is the first factor or the second factor.

5. 2. The seat recommendation server of claim 1, wherein, when a first evaluated employee's evaluation of a first evaluated employee based on a first factor is recorded in the evaluation database and a second evaluated employee's evaluation of a second evaluated employee based on the first factor is recorded in the evaluation database, the processing device determines the recommended seats for the first evaluated employee, the first evaluated employee, the second evaluated employee, and the second evaluated employee so that the distance between the first evaluated employee and the first evaluated employee is not equal to or less than a first distance, the distance between the second evaluated employee and the second evaluated employee is not equal to or less than a second distance, the distance between the first evaluated employee and the second evaluated employee is not equal to or less than a third distance, and the distance between the second evaluated employee and the first evaluated employee is not equal to or less than a fourth distance.

6. 6. The seat recommendation server according to claim 1, wherein the factors include at least one of the following: cigarette odor, perfume odor, keystroke sounds, chewing sounds, and line of sight.

7. When the processing device detects that the employee is sitting in a seat other than the recommended seat, the processing device re-determines the recommended seat for the employee who is not sitting in a seat; A seat recommendation server as described in any one of claims 1 to 5, wherein the communication device notifies the terminal device used by the employee whose recommended seat has been changed by redetermining of the change in the recommended seat.

8. The seat recommendation server according to any one of claims 1 to 5, wherein the processing device deletes the oldest piece of evaluation information from the evaluation database when the number of pieces of evaluation information stored in the evaluation database exceeds a predetermined upper limit.

9. a seat recommendation server according to claim 1; and the terminal device.

10. the terminal device displays an input screen for inputting the evaluation information, The seat recommendation system according to claim 9 , wherein the input screen does not display information that can identify the employee.

11. A computer-implemented seat recommendation method for recommending seats for employees working in a facility to work on a given day, comprising: determining a seat recommendation to recommend to the employee; a step of communicating with a terminal device capable of displaying the recommended seating and capable of inputting, as evaluation information, the causes of stress felt by the employee to be evaluated regarding the employee to be evaluated; a step of storing the evaluation information acquired from the terminal device and an evaluation distance, which is the distance between the location of the evaluation employee and the location of the employee to be evaluated, in an evaluation database in association with the evaluation employee and the employee to be evaluated; The determining step includes: acquiring the position of the employee sitting in the seat as position information; determining the recommended seat using a distance between the position of the candidate recommended seat for the employee who is the target of the recommended seat and the position of the employee from which the position information was acquired, and a correspondence relationship between the evaluation information and the evaluation distance stored in the evaluation database; The seat recommendation method, wherein the communicating step includes a step of transmitting the determined recommended seats to the terminal device.

Images