Information processing method, information processing device, and information processing program

Abstract

The aim is to improve the utilization rate of each of the one or more areas and to revitalize communication among users. [Solution] The information processing method is a computer information processing method that includes: acquiring area information which is information relating to one or more areas; estimating at least one of the number of people conversing in each area and the amount of conversation in each area based on sensing information obtained by sensing in each area; calculating a first indicator that shows the scale of communication in each area based on at least one of the number of people and the amount of conversation; and outputting information showing the first indicator in association with the area information.

Description

Technical Field

[0001] The present disclosure relates to a technique for activating communication.

Background Art

[0002] Patent Document 1 discloses a technique for detecting human movement with a camera and visualizing the degree of congestion and activity amount of people for each area.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, with the technique of Patent Document 1, although the degree of congestion and activity amount of each area can be grasped, it is not possible to grasp what kind of communication is being carried out in each area. Therefore, it has been difficult to consider measures for improving the utilization rate of each area and activating communication between users.

[0005] The present disclosure has been made to solve such problems, and an object thereof is to provide a technique capable of improving the utilization rate of each of one or more areas and activating communication between users.

Means for Solving the Problems

[0006] An information processing method in one aspect of the present disclosure is an information processing method in a computer, comprising: acquiring area information which is information relating to one or more areas; estimating at least one of the number of people conversing in each area and the amount of conversation in each area based on sensing information obtained by sensing in each area; calculating a first indicator indicating the scale of communication in each area based on at least one of the number of people and the amount of conversation; and outputting information indicating the first indicator in association with the area information. [Effects of the Invention]

[0007] According to this disclosure, it is possible to improve the utilization rate of one or more areas and to revitalize communication among users. [Brief explanation of the drawing]

[0008] [Figure 1] This is an overall diagram of the information processing system. [Figure 2] This figure shows an example of a floor map image containing one or more spaces. [Figure 3] This figure shows the first example of an image displayed on the terminal device's screen. [Figure 4] This is a flowchart showing the processing steps of an information processing system. [Figure 5] This figure shows a second example of an image displayed on the terminal device's screen. [Figure 6] This figure shows the first calculation example of the second indicator. [Figure 7] This figure shows a second example of calculating the second indicator. [Figure 8] This figure shows a third example of an image displayed on the terminal device's screen. [Figure 9] This figure shows a fourth example of an image displayed on the terminal device's screen. [Figure 10] This figure shows an example of a ripple image output as information indicating the first indicator. [Figure 11]This figure shows another example of a ripple image output as information indicating the first indicator. [Figure 12] This figure shows the fifth example of an image displayed on the terminal device's screen. [Modes for carrying out the invention]

[0009] (Background leading to this disclosure) The inventors are investigating technologies to improve the utilization rate of communication spaces such as conference rooms and communication areas, as well as general-purpose spaces such as entrances, rest areas, and cafes, and to revitalize communication among users, in facilities such as office buildings and commercial buildings.

[0010] While the technology described in Patent Document 1 above can grasp the degree of congestion and activity levels in each area, it cannot grasp what kind of communication is taking place in each area. Therefore, it has been difficult to consider measures to improve the utilization rate of each area and revitalize communication among users, such as determining the scale of communication possible in each area.

[0011] Therefore, the inventors diligently studied technologies that could improve the utilization rate of one or more areas and revitalize communication among users, and came up with the present disclosure described below.

[0012] (1) An information processing method in one aspect of the present disclosure is an information processing method in a computer, which includes: acquiring area information which is information relating to one or more areas; estimating at least one of the number of people having a conversation in each area and the amount of conversation in each area based on sensing information obtained by sensing in each area; calculating a first indicator indicating the scale of communication in each area based on at least one of the number of people and the amount of conversation; and outputting information indicating the first indicator in association with the area information.

[0013] In this configuration, a first index indicating the scale of communication in each area is calculated based on at least one of the number of people having a conversation in each area and the amount of conversation in each area. Since the information indicating this first index is output in association with the area information, the scale of communication in each area can be easily grasped. As a result, it becomes possible to effectively consider measures for improving the utilization rate of each area and activating communication among users. As a result, it is possible to improve the utilization rate of each of one or more areas and activate communication among users.

[0014] (2) In the information processing method described in (1) above, the one or more areas may be set according to the use of the space including the one or more areas.

[0015] According to this configuration, the scale of communication in each area set according to the use of the space can be easily grasped. As a result, it becomes possible to effectively consider measures for improving the utilization rate of each area and activating communication among users according to the use of the space.

[0016] (3) In the information processing method described in (2) above, when the use of the space is for communication, one or more spaces partitioned within the space may be set as the one or more areas.

[0017] According to this configuration, the scale of communication in each space partitioned within the space for communication use can be easily grasped. As a result, it becomes possible to effectively consider measures for improving the utilization rate of each space partitioned within the space for communication use and activating communication among users.

[0018] (4) In the information processing method described in (2) above, if the use of the space is for general purposes, one or more spaces within the space where a conversation is taking place may be set as the one or more areas.

[0019] This configuration makes it easy to grasp the scale of communication in each space where conversations are taking place within a general-purpose space. This allows for the effective consideration of measures to improve the utilization rate of each space where conversations are taking place within the general-purpose space and to revitalize communication among users.

[0020] (5) In the information processing method described in any one of (1) to (4) above, at least one of the number of people and the amount of conversation is the amount of conversation, and the calculation of the first indicator may include calculating the first indicator such that the larger the amount of conversation, the larger the scale of the communication.

[0021] In this configuration, a first indicator is calculated that shows the scale of communication as the amount of conversation in each area increases. Information indicating this first indicator is output in association with area information. This allows for accurate understanding of the scale of communication, which changes according to the amount of conversation in each area.

[0022] (6) In the information processing method described in (5) above, estimating at least one of the number of people and the amount of conversation may include estimating the time spent by multiple people in each area based on the sensing information, and estimating the amount of conversation based on the time spent.

[0023] In this configuration, the dwell time of multiple people is estimated based on sensing information in each area. Therefore, the amount of conversation in each area can be appropriately estimated based on that dwell time.

[0024] (7) In the information processing method described in (5) above, estimating at least one of the number of people and the volume of conversation may include estimating the time spent by multiple people in each area based on the sensing information, estimating the volume of conversations taking place in each area based on the sensing information, and estimating the volume of conversation based on the time spent and the volume of conversations.

[0025] In this configuration, based on sensing information in each area, the dwell time of multiple people, as well as the volume of their conversations, are estimated. Therefore, the amount of conversation in each area can be estimated more accurately based on both dwell time and conversation volume.

[0026] (8) In the information processing method described in (5) above, estimating at least one of the number of people and the amount of conversation may include estimating the time spent by multiple people in each area based on the sensing information, estimating the elapsed time from the start to the end of a conversation taking place in each area based on the sensing information, and estimating the amount of conversation based on the time spent and the elapsed time.

[0027] In this configuration, based on sensing information in each area, not only the time spent by multiple people but also the elapsed time from the start to the end of a conversation is estimated. Therefore, the amount of conversation in each area can be estimated more accurately based on both the time spent and the elapsed time of the conversation.

[0028] (9) In the information processing method described in any one of (1) to (4) above, at least one of the number of people and the amount of conversation is the number of people, and the calculation of the first indicator may include calculating the first indicator in such a way that the larger the number of people, the larger the scale of the communication.

[0029] In this configuration, a first indicator is calculated that shows the scale of communication as the number of people conversing in each area increases. This first indicator is then output in association with area information. Therefore, it is possible to accurately grasp the scale of communication, which changes according to the number of people conversing in each area.

[0030] (10) In the information processing method described in (9) above, the sensing information includes images taken of each area, and estimating at least one of the number of people and the amount of conversation may include detecting from the images first information indicating the orientation of the faces of multiple people or second information indicating the movement of their mouths, and estimating the number of people based on the first information or the second information.

[0031] In this configuration, images taken in each area are used to detect either first information indicating the orientation of multiple people's faces or second information indicating their mouth movements. Therefore, based on the first or second information in each area, the number of people having a conversation in each area can be appropriately estimated.

[0032] (11) In the information processing method described in (9) above, the sensing information includes sounds picked up in each area, and estimating at least one of the number of people and the amount of conversation may include detecting human voices from the sounds and estimating the number of people based on the voices.

[0033] In this configuration, human voices are detected from the sounds collected in each area. Therefore, based on the voices detected in each area, the number of people conversing in each area can be appropriately estimated.

[0034] (12) In the information processing method described in (10) above, the detection of the first information may include detecting third information indicating the skeletons of the multiple people from the image, and detecting the first information based on the third information.

[0035] According to this configuration, based on third information indicating the skeletons of multiple people detected from images taken in each area, first information indicating the orientation of multiple people's faces can be appropriately detected. As a result, based on this first information, the number of people having a conversation in each area can be appropriately estimated.

[0036] (13) In the information processing method described in any one of (1) to (12) above, the area information includes a map image of a space containing the one or more areas, and outputting information indicating the first indicator may include displaying information indicating the first indicator at a position corresponding to each area in the map image.

[0037] In this configuration, information indicating the first indicator is displayed at the location corresponding to each area in the map image. This makes it possible to visualize the scale of communication in each area. As a result, the scale of communication in each area can be easily grasped.

[0038] (14) The information processing method described in (13) above further includes calculating a second indicator of the quality of conversations taking place in each area based on the sensing information, and outputting information indicating the first indicator further includes outputting the information indicating the second indicator in association with the information indicating the first indicator.

[0039] With this configuration, information representing the second indicator is output in correspondence with information representing the first indicator, making it easy to grasp not only the scale of communication in each area but also the quality of conversations. This makes it possible to effectively consider measures to improve the utilization rate in each area and to revitalize high-quality communication among users.

[0040] (15) In the information processing method described in any one of (1) to (12) above, outputting information indicating the first indicator may include obtaining status information indicating the usage status of each area, and outputting a table that associates the status information with the information indicating the first indicator, in association with the area information.

[0041] This configuration generates a table that links status information showing the usage status of each area with information showing the first indicator, which is then output in association with the area information. This allows for simultaneous understanding of usage status and the scale of communication in each area. As a result, it becomes possible to efficiently consider measures to improve the utilization rate of each area and to revitalize communication among users.

[0042] Furthermore, this disclosure can be implemented not only as an information processing method that performs the characteristic processing described above, but also as an information processing device, etc., having a characteristic configuration corresponding to the characteristic processing performed by the information processing method. It can also be implemented as a computer program that causes a computer to execute the characteristic processing included in such an information processing method. Therefore, the same effects as the above-described information processing method can be achieved in the following other embodiments.

[0043] (16) An information processing device in another aspect of the present disclosure is an information processing device including a processor, the processor performing the following: acquiring area information which is information relating to one or more areas; estimating at least one of the number of people having a conversation in each area and the amount of conversation in each area based on sensing information obtained by sensing in each area; calculating a first index indicating the scale of communication in each area based on at least one of the number of people and the amount of conversation; and outputting information indicating the first index in association with the area information.

[0044] (17) An information processing program in yet another aspect of the present disclosure causes a computer to perform the following actions: acquire area information which is information relating to one or more areas; estimate at least one of the number of people having a conversation in each area and the amount of conversation in each area based on sensing information obtained by sensing in each area; calculate a first index indicating the scale of communication in each area based on at least one of the number of people and the amount of conversation; and output information indicating the first index in association with the area information.

[0045] This disclosure can also be implemented as an information processing system operated by such an information processing program. Furthermore, it goes without saying that such a computer program can be distributed via a computer-readable, non-transient recording medium such as a CD-ROM, or via a communication network such as the Internet.

[0046] Embodiments of this disclosure will be described below with reference to the attached drawings. Note that the embodiments described below are all specific examples of this disclosure. The numerical values, shapes, components, steps, and order of steps shown in the following embodiments are examples and are not intended to limit this disclosure. Furthermore, components in the following embodiments that are not described in the independent claim representing the highest-level concept will be described as optional components. Also, in all embodiments, the contents of each can be combined.

[0047] (First Embodiment) Figure 1 is an overall diagram of Information Processing System 1. Information Processing System 1 is a system that calculates an index indicating the scale of communication in each of one or more areas, and outputs that index in association with information about one or more areas.

[0048] As shown in Figure 1, the information processing system 1 includes one or more position sensors 31, one or more cameras 32, one or more microphones 33, a terminal device 40, and a server 10 (information processing device and computer). The one or more position sensors 31, cameras 32 and microphones 33, the terminal device 40, and the server 10 are connected to each other so as to be able to communicate with one another via a network 80. An example of the network 80 is the internet.

[0049] One or more position sensors 31, cameras 32, and microphones 33 are arranged in one or more spaces provided within a facility. The facility includes, for example, office buildings and commercial facilities. The spaces include, for example, spaces for communication purposes such as conference rooms and meeting rooms, as well as spaces for general use such as cafeterias, rest areas, and cafes. The spaces are divided into one or more areas. One or more position sensors 31 are placed in each space. One or more cameras 32 and one or more microphones 33 are placed in each area.

[0050] Figure 2 shows an example of a map image of floor 90 that includes one or more spaces 9A to 9F. In the example in Figure 2, the general-purpose space 9A is divided into four spaces 91A, 92A, 93A, and 94A. The communication space 9B is divided into two spaces 91B and 92B. The general-purpose space 9C and the three communication spaces 9D, 9E, and 9F are not divided into multiple spaces, but consist of one space each, 91C, 91D, 91E, and 91F.

[0051] The position sensor 31 is composed of, for example, a beacon sensor, an infrared sensor, or an optical sensor. The position sensor 31 periodically detects (senses) the location of one or more people present in the space where the position sensor 31 is located, and transmits information to the server 10 that includes the current date and time, identification information of the position sensor 31, and information indicating the location of the one or more people detected.

[0052] Furthermore, if the position sensor 31 is composed of a beacon sensor, various information, such as identification information of facility users, included in the beacon signal received by the beacon sensor may be transmitted to the server 10. The position of a person detected by the position sensor 31 may be a relative position based on the placement position of the position sensor 31, or it may be an absolute position expressed as latitude, longitude, and altitude.

[0053] Camera 32 periodically takes images (senses) of the space in which it is located and transmits information to the server 10, including the current date and time, data indicating the captured image, and identification information of camera 32.

[0054] The microphone 33 periodically senses sounds generated within the space where it is located, and transmits information to the server 10, including the current date and time, data indicating the sound that was sensed, and identification information for the microphone 33.

[0055] Hereafter, the position sensor 31, camera 32, and microphone 33 will be referred to collectively as "sensors." The information transmitted by the position sensor 31, camera 32, and microphone 33 to the server 10 will be referred to collectively as "sensing information."

[0056] The terminal device 40 consists of a computer equipped with a display. The terminal device 40 may consist of, for example, a stationary computer such as a personal computer, or a portable computer such as a smartphone or tablet device.

[0057] The terminal device 40 receives space information, spatial information, and sensor information, as described later, through operations by facility managers, etc. The terminal device 40 transmits the input space information, spatial information, and sensor information to the server 10. When various instructions are input to the server 10 through operations by facility users, etc., the terminal device 40 transmits the input instructions to the server 10.

[0058] Furthermore, the terminal device 40 displays various images on the display according to display instructions from the server 10. In the example in Figure 1, one terminal device 40 is shown, but multiple terminal devices may be connected to the server 10 via the network 80.

[0059] Server 10 is, for example, a cloud server composed of one or more computers. However, this is just an example, and Server 10 may also be composed of edge servers.

[0060] Server 10 includes a communication device 12, memory 13, and a processor 11.

[0061] The communication device 12 is a communication interface that connects the server 10 to the network 80. The communication device 12 receives various information and instructions transmitted by the terminal device 40 via the network 80 and outputs the received information and instructions to the processor 11. The communication device 12 transmits display instructions for various images output by the output unit 115 (described later) to the terminal device 40 via the network 80.

[0062] The memory 13 consists of a non-volatile, rewritable storage device, such as a solid-state drive or a hard disk drive. The memory 13 stores various information used for processing performed by the processor 11.

[0063] Specifically, the memory 13 stores information about one or more spaces provided in the facility (hereinafter referred to as space information) under the control of the acquisition unit 112, which will be described later. The space information includes a map image (floor plan, floor map) of the floor 90 containing one or more spaces, identification information for each space, information indicating the shape, size, and location of each space, and information indicating the purpose of each space (hereinafter referred to as purpose information). The space information may further include information indicating the location of one or more spaces within each space where multiple people can gather. A space where people can gather (hereinafter referred to as a gathering space) includes, for example, a space where desks are placed and a space where chairs are placed. The purpose information includes, for example, communication purposes and general-purpose purposes.

[0064] Memory 13 stores information about one or more spaces partitioned within each space (hereinafter referred to as spatial information) under the control of the acquisition unit 112, which will be described later. The spatial information includes identification information for each space and information indicating the shape, size, and location of each space.

[0065] The memory 13 stores information (hereinafter referred to as sensor information) regarding one or more position sensors 31, cameras 32, and microphones 33 provided by the facility, under the control of the acquisition unit 112, which will be described later. The sensor information includes identification information for each sensor and information indicating the placement location of each sensor.

[0066] The memory 13 stores sensing information received from each sensor installed in the facility, under the control of the collection unit 111, which will be described later.

[0067] The processor 11 consists of a central processing unit (CPU). The processor 11 includes a reception unit 110, a collection unit 111, an acquisition unit 112, an estimation unit 113, a calculation unit 114, and an output unit 115. The reception unit 110, the collection unit 111, the acquisition unit 112, the estimation unit 113, the calculation unit 114, and the output unit 115 are realized by the CPU executing an information processing program stored in memory 13. However, this is just an example, and the reception unit 110, the collection unit 111, the acquisition unit 112, the estimation unit 113, the calculation unit 114, and the output unit 115 may be composed of a dedicated integrated circuit or may be distributed across multiple computers.

[0068] The reception unit 110 receives various instructions from the terminal device 40 to the communication device 12.

[0069] The collection unit 111 collects sensing information received by the communication device 12 from the sensor and stores the sensing information in the memory 13.

[0070] The acquisition unit 112 acquires space information, spatial information, and sensor information received by the communication device 12 from the terminal device 40, and stores the space information, spatial information, and sensor information in the memory 13.

[0071] The estimation unit 113 refers to the space information, sensor information, and sensing information stored in the memory 13 to identify one or more spaces where multiple people are present.

[0072] Specifically, the estimation unit 113 performs the following processing for each of the one or more spaces provided in the facility. The estimation unit 113 acquires the sensing information (hereinafter referred to as space sensing information) that each sensor placed in the space transmitted at the most recent date and time within a predetermined error time from the present moment, out of the sensing information related to the space to be processed. The sensing information related to the space is the sensing information transmitted to the server 10 from one or more sensors placed in the space.

[0073] If multiple people are present in the space to be processed, the position sensor 31 located in that space transmits sensing information to the server 10 that includes information indicating the position of each of those multiple people. Therefore, the estimation unit 113 determines that multiple people are present in the space to be processed if the space sensing information includes sensing information that includes information indicating multiple positions within the space to be processed. In this case, the estimation unit 113 identifies the space to be processed as a space where multiple people are present.

[0074] On the other hand, if the estimation unit 113 is unable to acquire space sensing information, it determines that there are no multiple people in the space being processed. Also, if the sensing information containing information indicating multiple locations within the space being processed is not included in the space sensing information, the estimation unit 113 determines that there are no multiple people in the space being processed. If the estimation unit 113 determines that there are no multiple people in the space being processed, it does not identify the space being processed as a space where multiple people exist.

[0075] Furthermore, if the estimation unit 113 contains sensing information that includes information indicating multiple locations within the space to be processed, it may acquire additional sensing information that includes image data contained in the space sensing information. The estimation unit 113 may then use the image data to perform known image analysis processing to determine if multiple people are present in the space to be processed, and if it fails to recognize multiple people, it may determine that no multiple people are present in the space to be processed.

[0076] Furthermore, if the space sensing information includes sensing information indicating multiple locations within the space to be processed, the estimation unit 113 may acquire additional sensing information including sound data included in the space sensing information. The estimation unit 113 may then perform known speech analysis processing using the sound data to determine that multiple people are present in the space to be processed if the sound data includes data indicating human voices and the volume of the sound is above a predetermined threshold. On the other hand, the estimation unit 113 may perform known speech analysis processing using the sound data to determine that multiple people are not present in the space to be processed if the sound data does not include data indicating human voices, or if the volume of the sound is below a predetermined threshold.

[0077] The estimation unit 113 sets up one or more areas for each of the one or more spaces where multiple people are present, according to the purpose of each space.

[0078] Specifically, the estimation unit 113 refers to the space information and spatial information stored in the memory 13 and, if the purpose of the space to be processed (hereinafter referred to as the target space) is for communication purposes, sets one or more spaces partitioned within the target space as one or more areas of the target space.

[0079] In the example shown in Figure 2, suppose the estimation unit 113 determines that there are multiple people in the communication spaces 9B, 9D, 9E, and 9F. In this case, the estimation unit 113 sets the two spaces 91B and 92B partitioned within space 9B as two areas of space 9B. The estimation unit 113 sets the one space 91D partitioned within space 9D as one area of ​​space 9D. Similarly, the estimation unit 113 sets the one space 91E partitioned within space 9E as one area of ​​space 9E, and sets the one space 91F partitioned within space 9F as one area of ​​space 9F.

[0080] On the other hand, if the target space is for general-purpose use, the estimation unit 113 identifies one or more spaces within the target space where a conversation is taking place, based on sensing information about the target space. The estimation unit 113 sets the identified one or more spaces as one or more areas within the target space.

[0081] Specifically, the estimation unit 113 refers to the space information, sensor information, and sensing information stored in the memory 13 and performs the following processing.

[0082] The estimation unit 113 acquires sensing information related to the target space, specifically the sensing information transmitted by each sensor placed in the target space at the most recent date and time within a predetermined error time from the present (hereinafter referred to as the first target sensing information). The estimation unit 113 refers to the location information of the target space and divides the target space into one or more spaces using a predetermined division method, and performs the following processing on each of the divided spaces (hereinafter referred to as the target space). The method of dividing the target space by the estimation unit 113 includes, for example, a method of dividing so that the base area is equal. If the space information includes information indicating the location of one or more collective spaces, the estimation unit 113 may treat each of those one or more collective spaces as a target space.

[0083] (Process 1) The estimation unit 113 determines whether or not multiple people are present in the target space. Specifically, if the estimation unit 113 is unable to obtain the first target sensing information, it determines that there are no multiple people in the target space. If multiple people are present in the target space, the position sensor 31, which detects the positions of the people present in the target space, transmits sensing information including information indicating the position of each of those people to the server 10. For this reason, if the first target sensing information does not contain sensing information including information indicating multiple positions in the target space, the estimation unit 113 determines that there are no multiple people in the target space. In these cases, the estimation unit 113 does not identify the target space as a space where a conversation is taking place. On the other hand, if the first target sensing information contains sensing information including information indicating multiple positions in the target space, the estimation unit 113 determines that there are multiple people in the target space.

[0084] Furthermore, if the estimation unit 113 contains sensing information that includes information indicating multiple locations within the target space, it may acquire sensing information that includes image data included in the first target sensing information. The estimation unit 113 may then use the image data to perform known image analysis processing to determine if multiple people are present in the target space, and if it fails to recognize multiple people, it may determine that no multiple people are present in the target space.

[0085] Furthermore, if the first target sensing information includes sensing information indicating multiple locations within the target space, the estimation unit 113 may acquire sensing information including sound data included in the first target sensing information. The estimation unit 113 may then perform known speech analysis processing using the sound data to determine that multiple people are present in the target space if the sound data includes data indicating human voices and the volume of the sound is above a predetermined threshold. On the other hand, the estimation unit 113 may perform known speech analysis processing using the sound data to determine that multiple people are not present in the target space if the sound data does not include data indicating human voices, or if the volume of the sound is below a predetermined threshold.

[0086] (Process 2) If the estimation unit 113 determines in Process 1 that there are multiple people in the target space, it determines whether the sensing information transmitted by the microphone 33 that picks up sounds generated in the target space is included in the first target sensing information.

[0087] (Process 3) Assume that in Process 2, the estimation unit 113 determined that the sensing information transmitted by the microphone 33, which captures sounds generated in the target space, is included in the first target sensing information. In this case, the estimation unit 113 determines, by performing known sound analysis processing, whether or not the data indicating sounds included in the sensing information contains data indicating human speech.

[0088] (Process 4) If the estimation unit 113 determines in process 3 that no data indicating human voice is included, it detects that no conversation is taking place in the target space. In this case, the estimation unit 113 does not identify the target space as a space where a conversation is taking place.

[0089] On the other hand, if the estimation unit 113 determines in processing 3 that data indicating human voice is included, it detects that a conversation is taking place in the target space. In this case, the estimation unit 113 identifies the target space as a space where a conversation is taking place and sets the target space as the area of ​​the target space.

[0090] (Process 5) If the estimation unit 113 determines in Process 1 that there are multiple people in the target space, it determines whether the sensing information transmitted by the camera 32 that photographs the target space is included in the first target sensing information.

[0091] (Process 6) If the estimation unit 113 determines in process 5 that sensing information is included in the first target sensing information, it extracts the skeletal points (third information) of multiple people from the data representing the image included in the sensing information (hereinafter referred to as target image data) by performing known image analysis processing. The skeletal points include, for example, the left eye, right eye, left ear, right ear, nose, and mouth.

[0092] The estimation unit 113 acquires information indicating the orientation of each person's face (first information) and information indicating the movement of their mouth (second information) based on the extracted skeletal points. Alternatively, the estimation unit 113 may directly acquire information indicating the orientation of the faces and the movement of the mouths of multiple people from the target image data by using a machine learning model, etc., instead of extracting skeletal points from multiple people.

[0093] (Process 7) If the information obtained in Process 6 does not indicate that two or more people's faces are facing each other, the estimation unit 113 detects that no conversation is taking place in the target space. If the information obtained in Process 6 indicates that the mouths of all the people are closed, the estimation unit 113 detects that no conversation is taking place in the target space. In these cases, the estimation unit 113 does not identify the target space as a space where a conversation is taking place.

[0094] On the other hand, the estimation unit 113 detects that a conversation is taking place in the target space if the information obtained in process 6 indicates that two or more people's faces are facing each other and that one or more people's mouths are open. In this case, the estimation unit 113 identifies the target space as a space where a conversation is taking place and sets the target space as the area of ​​the target space.

[0095] Furthermore, the estimation unit 113 may choose not to perform processes 2, 3, and 4. Alternatively, the estimation unit 113 may choose not to perform processes 5, 6, and 7.

[0096] The estimation unit 113 designates one or more areas of the target space as target areas and estimates the amount of conversation in each target area based on sensing information obtained by sensing in each target area (hereinafter referred to as second target sensing information).

[0097] (First method for estimating the volume of conversation in the target area) Specifically, the estimation unit 113 refers to the space information, sensor information, and sensing information stored in the memory 13. The estimation unit 113 then acquires sensing information including information indicating one or more locations within the target area, sensing information transmitted by the camera 32 that photographs the target area, and sensing information transmitted by the microphone 33 that captures sounds generated within the target area, as second target sensing information.

[0098] The estimation unit 113 estimates the time spent by multiple people in the target area based on the second target sensing information, and estimates the amount of conversation in the target area based on that time spent.

[0099] Specifically, the estimation unit 113 acquires the sensing information (hereinafter referred to as the first sensing information) that includes the most recent date and time within a predetermined error time from the present moment, from among the sensing information that includes information indicating multiple locations within the target area, which is included in the second target sensing information. If the estimation unit 113 is unable to acquire the first sensing information, it detects that there are no multiple people in the target area. In this case, the estimation unit 113 estimates the time spent by multiple people in the target area to be 0.

[0100] If the estimation unit 113 is able to acquire the first sensing information, it acquires the sensing information containing the most recent date and time (hereinafter referred to as the second sensing information) from among the sensing information containing information indicating only one location within the target area that is included in the second target sensing information. The estimation unit 113 acquires the sensing information containing the most recent date and time (hereinafter referred to as the third sensing information) from among the sensing information containing information indicating multiple locations within the target area that is included in the second target sensing information that is after the date and time included in the second sensing information.

[0101] The estimation unit 113 estimates the elapsed time from the date and time included in the third sensing information (hereinafter referred to as the first date and time) to the date and time included in the first sensing information (hereinafter referred to as the second date and time) as the stay time of multiple people in the target area. In this case, the estimation unit 113 estimates the estimated stay time as the amount of conversation in the target area. That is, the first date and time is the date and time when the stay of multiple people in the target area begins. The second date and time is the date and time when the stay of multiple people in the target area ends.

[0102] For example, if the estimated stay time is 60 minutes, the estimation unit 113 estimates 60 as the amount of conversation in the target area. If the estimated stay time is 30 minutes, the estimation unit 113 estimates 30 as the amount of conversation in the target area. If the estimated stay time is 10 minutes, the estimation unit 113 estimates 10 as the amount of conversation in the target area.

[0103] Furthermore, the method used by the estimation unit 113 to estimate the amount of conversation in the target area may be the estimation method shown below.

[0104] (Second method for estimating the volume of conversation in the target area) The estimation unit 113 estimates the time spent by multiple people in the target area, similar to the first method for estimating the amount of conversation in the target area described above. Based on the second target sensing information, the estimation unit 113 estimates the volume of conversation taking place in the target area. Based on the estimated time spent and the volume of conversation, the estimation unit 113 estimates the amount of conversation in the target area.

[0105] Specifically, the estimation unit 113 acquires sensing information (hereinafter referred to as the fourth sensing information) transmitted by the microphone 33 that picks up sounds generated within the target area, which is included in the second target sensing information. The estimation unit 113 then acquires sensing information (hereinafter referred to as the fifth sensing information) from the fourth sensing information that includes the date and time within the period from the first date and time to the second date and time.

[0106] The estimation unit 113 extracts data indicating human speech from the sound data included in the fifth sensing information by performing known sound analysis processing. The estimation unit 113 estimates the average value of the volume of speech indicated by the extracted data as the volume of conversation taking place in the target area. The estimation unit 113 estimates the amount of conversation in the target area as the sum of the product of the estimated stay time and the first coefficient, and the product of the estimated conversation volume and the second coefficient.

[0107] For example, suppose the first coefficient is 10 and the second coefficient is 3. In this case, if the estimated stay time is 30 minutes and the estimated conversation volume is 60 dB, the estimation unit 113 estimates 480 (= 30 × 10 + 60 × 3) as the amount of conversation in the target area. If the estimated stay time is 10 minutes and the estimated conversation volume is 80 dB, the estimation unit 113 estimates 340 (= 10 × 10 + 80 × 3) as the amount of conversation in the target area. If the estimated stay time is 10 minutes and the estimated conversation volume is 50 dB, the estimation unit 113 estimates 250 (= 10 × 10 + 50 × 3) as the amount of conversation in the target area.

[0108] (A third method for estimating the volume of conversation in the target area) The estimation unit 113 estimates the time spent by multiple people in the target area, similar to the first method for estimating the amount of conversation in the target area described above. Based on the second target sensing information, the estimation unit 113 estimates the elapsed time from the start to the end of a conversation taking place in the target area. Based on the estimated time spent and elapsed time, the estimation unit 113 estimates the amount of conversation in the target area.

[0109] Specifically, the estimation unit 113 acquires sensing information (hereinafter referred to as the sixth sensing information) transmitted by the camera 32 that photographs the target area, which is included in the second target sensing information. By performing known image analysis processing, the estimation unit 113 acquires sensing information (hereinafter referred to as the seventh sensing information) from the sixth sensing information that includes data showing an image in which two or more people's faces are facing each other and one or more people's mouths are open, and also includes a date and time within the period from the first date and time to the second date and time.

[0110] The estimation unit 113 estimates the elapsed time from the most recent date and time included in the seventh sensing information as the elapsed time from the start to the end of a conversation taking place in the target area. The estimation unit 113 estimates the amount of conversation in the target area as the sum of the product of the estimated stay time and the third coefficient, and the product of the estimated elapsed time and the fourth coefficient.

[0111] For example, suppose the third coefficient is 1 and the fourth coefficient is 3. In this case, if the estimated stay time is 30 minutes and the estimated elapsed time is 25 minutes, the estimation unit 113 estimates 105 (=30 × 1 + 25 × 3) as the amount of conversation in the target area. If the estimated stay time is 30 minutes and the estimated elapsed time is 10 minutes, the estimation unit 113 estimates 60 (=30 × 1 + 10 × 3) as the amount of conversation in the target area. If the estimated stay time is 30 minutes and the estimated conversation volume is 3 minutes, the estimation unit 113 estimates 39 (=30 × 1 + 3 × 3) as the amount of conversation in the target area.

[0112] Furthermore, in the third method for estimating the amount of conversation in the target area, the estimation unit 113 may estimate the elapsed time from the start to the end of a conversation taking place in the target area as follows.

[0113] Specifically, the estimation unit 113 acquires fifth sensing information in the same manner as the second method for estimating the amount of conversation in the target area described above. The estimation unit 113 extracts sensing information (hereinafter referred to as eighth sensing information) that includes data indicating human speech, which is contained in the fifth sensing information, by performing known sound analysis processing.

[0114] The estimation unit 113 estimates the elapsed time from the most recent date and time to the most recent date and time, which is included in the eighth sensing information, as the elapsed time from the start to the end of a conversation taking place in the target area.

[0115] (Fourth method for estimating conversation volume in the target area) The estimation unit 113 estimates the time spent by multiple people in the target area, similar to the first method for estimating the amount of conversation in the target area described above. The estimation unit 113 estimates the volume of conversation taking place in the target area, similar to the second method for estimating the amount of conversation in the target area described above. The estimation unit 113 estimates the elapsed time from the start to the end of conversation taking place in the target area, similar to the third method for estimating the amount of conversation in the target area described above. Based on the estimated time spent, conversation volume, and elapsed time, the estimation unit 113 estimates the amount of conversation in the target area.

[0116] Specifically, the estimation unit 113 estimates the amount of conversation in the target area as the sum of the product of the estimated stay time and the fifth coefficient, the product of the estimated conversation volume and the sixth coefficient, and the product of the estimated elapsed time and the seventh coefficient.

[0117] For example, suppose the fifth coefficient is 10, the sixth coefficient is 3, and the seventh coefficient is 3. In this case, if the estimated stay time is 30 minutes, the estimated conversation volume is 80 dB, and the estimated elapsed time is 25 minutes, the estimation unit 113 estimates 615 (=30×10+80×3+25×3) as the amount of conversation in the target area. If the estimated stay time is 30 minutes, the estimated conversation volume is 60 dB, and the estimated elapsed time is 25 minutes, the estimation unit 113 estimates 555 (=30×10+60×3+25×3) as the amount of conversation in the target area. If the estimated stay time is 30 minutes, the estimated conversation volume is 50 dB, and the estimated elapsed time is 10 minutes, the estimation unit 113 estimates 480 (=30×10+50×3+10×3) as the amount of conversation in the target area.

[0118] The calculation unit 114 calculates an index (hereinafter referred to as the first index) that indicates the scale of communication in the target area, based on the amount of conversation in the target area estimated by the estimation unit 113.

[0119] Specifically, the calculation unit 114 calculates the first indicator such that the larger the amount of conversation in the target area estimated by the estimation unit 113, the larger the scale of communication indicated by the first indicator.

[0120] For example, suppose the estimation unit 113 estimates the amount of conversation in the target area using the first estimation method for the amount of conversation in the target area described above. In this case, if the estimated amount of conversation is greater than or equal to a predetermined first threshold (for example, 60), the calculation unit 114 calculates "large" as the first indicator, indicating that the scale of communication is large.

[0121] The calculation unit 114 calculates "medium" as the first index if the estimated conversation volume is greater than or equal to a second threshold (e.g., 30) which is smaller than the first threshold, and less than the first threshold, indicating that the scale of communication is moderate.

[0122] The calculation unit 114 calculates "small" as the first indicator if the estimated conversation volume is greater than 0 and less than the second threshold, indicating that the scale of communication is small. The calculation unit 114 calculates "0" as the first indicator if the conversation volume estimated by the estimation unit 113 is 0, indicating that no communication took place.

[0123] Assume that the estimation unit 113 has estimated the amount of conversation in the target area using the second method for estimating the amount of conversation in the target area described above. In this case, if the estimated amount of conversation (e.g., 480) is greater than or equal to a predetermined third threshold (e.g., 400), the calculation unit 114 calculates "large" as the first indicator, indicating that the scale of communication is large.

[0124] The calculation unit 114 calculates "medium" as the first index if the estimated conversation volume (e.g., 340) is greater than or equal to the fourth threshold (e.g., 300), which is smaller than the third threshold, and less than the third threshold, and the size of the communication is moderate.

[0125] The calculation unit 114 calculates "small" as the first indicator if the estimated conversation volume (e.g., 250) is greater than 0 and less than the fourth threshold, indicating that the scale of communication is small. The calculation unit 114 also calculates "0" as the first indicator if the conversation volume estimated by the estimation unit 113 is 0, indicating that no communication took place.

[0126] Assume that the estimation unit 113 has estimated the amount of conversation in the target area using the third estimation method for the amount of conversation in the target area described above. In this case, if the estimated amount of conversation (e.g., 105) is greater than or equal to a predetermined fifth threshold (e.g., 100), the calculation unit 114 calculates "large" as the first index, indicating that the scale of communication is large.

[0127] The calculation unit 114 calculates "medium" as the first index if the estimated conversation volume (e.g., 60) is greater than or equal to the sixth threshold (e.g., 50), which is smaller than the fifth threshold, and less than the fifth threshold, and the size of the communication is moderate.

[0128] The calculation unit 114 calculates "small" as the first indicator if the estimated conversation volume (e.g., 39) is greater than 0 and less than the sixth threshold, indicating that the scale of communication is small. The calculation unit 114 also calculates "0" as the first indicator if the conversation volume estimated by the estimation unit 113 is 0, indicating that no communication took place.

[0129] Assume that the estimation unit 113 has estimated the amount of conversation in the target area using the fourth estimation method for the amount of conversation in the target area described above. In this case, if the estimated amount of conversation (e.g., 615) is greater than or equal to a predetermined seventh threshold (e.g., 600), the calculation unit 114 calculates "large" as the first indicator, indicating that the scale of communication is large.

[0130] The calculation unit 114 calculates "medium" as the first index, indicating that the scale of communication is moderate, if the estimated conversation volume (e.g., 555) is greater than or equal to the eighth threshold (e.g., 500), which is smaller than the seventh threshold, and less than the seventh threshold.

[0131] The calculation unit 114 calculates "small" as the first indicator if the estimated conversation volume (e.g., 480) is greater than 0 and less than the eighth threshold, indicating that the scale of communication is small. The calculation unit 114 also calculates "0" as the first indicator if the conversation volume estimated by the estimation unit 113 is 0, indicating that no communication took place.

[0132] The output unit 115 outputs information indicating a first indicator representing the scale of communication in one or more areas calculated by the calculation unit 114, in association with information relating to one or more areas (hereinafter referred to as area information). Hereafter, the first indicator representing the scale of communication in a given area will be abbreviated as the first indicator of that area.

[0133] Specifically, the output unit 115 acquires space information, which includes one or more areas, stored in the memory 13, as area information. The output unit 115 generates an image in which information indicating the first indicator of each area is superimposed on the positions corresponding to each area in the map image included in the area information. In this way, the output unit 115 associates the information indicating the first indicator of each area with the area information.

[0134] The output unit 115 controls the communication device 12 to transmit data indicating the generated image, along with a display instruction for the image indicated by the data, to the terminal device 40. In response, the terminal device 40 displays the image indicated by the data received along with the display instruction on the display unit of the terminal device 40, in accordance with the image display instruction received from the server 10.

[0135] Figure 3 shows a first example of an image displayed on the terminal device 40's display. Figure 3 shows an example of an image displayed on the terminal device 40's display when the estimation unit 113 identifies the general-purpose space 9A shown in Figure 2 as a space where multiple people are present, and detects that a conversation is taking place in three spaces 991A, 992A, and 993A, which are set as areas of space 9A.

[0136] In this example, the estimation unit 113 estimates the amount of conversation in each area (spaces 991A, 992A, 993A) of space 9A, and based on the estimation results, the calculation unit 114 calculates a first index for each area (spaces 991A, 992A, 993A). The output unit 115 controls the communication device 12 to transmit a display instruction to the terminal device 40, which is an image in which information indicating the first index for each area (spaces 991A, 992A, 993A) is superimposed on the map image of floor 90 including space 9A at the positions corresponding to each area (spaces 991A, 992A, 993A).

[0137] Figure 3 shows an example where the first index for area 991A is calculated as "large," and information indicating the first index "large" for that area is superimposed at the corresponding location in the map image. Figure 3 shows an example where the first index for area 992A is calculated as "small," and information indicating the first index "small" for that area is superimposed at the corresponding location in the map image. Figure 3 shows an example where the first index for area 993A is calculated as "medium," and information indicating the first index "medium" for that area is superimposed at the corresponding location in the map image.

[0138] In Figure 3, information indicating a "large" first indicator is shown with an image of a predetermined first density. Information indicating a "small" first indicator is shown with an image of a second density, which is lighter than the first density. Information indicating a "medium" first indicator is shown with an image of a third density, which is darker than the second density but lighter than the first density. This allows for a quick understanding that areas with denser superimposed images indicate a larger scale of communication.

[0139] Furthermore, Figure 3 shows an example where, similar to the above, the three spaces 991C, 992C, and 993C are set as areas for the general-purpose space 9C, and information indicating the first indicator ("large", "small", "medium") of each area is superimposed at the corresponding location in the map image of floor 90, which includes space 9C.

[0140] Furthermore, Figure 3 shows an example of an image displayed on the terminal device 40's screen when the estimation unit 113 identifies the communication space 9B shown in Figure 2 as a space where multiple people are present, and sets the two spaces 91B and 92B partitioned within space 9B as areas of space 9B.

[0141] In this example, the estimation unit 113 estimates the amount of conversation in each area (spaces 91B and 92B) of space 9B, and based on the estimation results, the calculation unit 114 calculates a first index for each area (spaces 91B and 92B). The output unit 115 controls the communication device 12 to transmit a display instruction to the terminal device 40, which is an image in which information indicating the first index for each area (spaces 91B and 92B) is superimposed on the map image of floor 90, including space 9B, at the positions corresponding to each area (spaces 91B and 92B).

[0142] Figure 3 shows an example where the first index for area 91B is calculated as "large," and information indicating the first index "large" for that area is superimposed at the corresponding location in the map image. Figure 3 also shows an example where the first index for area 92B is calculated as "0," and information indicating the first index "0" for that area is superimposed at the corresponding location in the map image. In Figure 3, the information indicating the first index "0" is shown as a transparent image surrounding the target area with a dashed line. However, the output unit 115 may choose not to superimpose anything at the location corresponding to an area where the first index is calculated as "0."

[0143] Furthermore, Figure 3 shows an example where, similar to the above, when space 91D is set as the area of ​​space 9D for communication purposes, information indicating the first indicator "Middle" of the area (space 91D) is superimposed on the map image of floor 90, which includes space 9D, at the position corresponding to that area (space 91D).

[0144] Similarly, Figure 3 shows an example where, when space 91E is set as the area for communication space 9E, information indicating the first indicator "small" for that area (space 91E) is superimposed at the position corresponding to that area (space 91E) in the map image of floor 90 including space 9E. Figure 3 also shows an example where, when space 91F is set as the area for communication space 9F, information indicating the first indicator "large" for that area (space 91F) is superimposed at the position corresponding to that area (space 91F) in the map image of floor 90 including space 9F.

[0145] (Processing by Information Processing System 1) Next, we will explain the processing of the information processing system 1. Figure 4 is a flowchart of the processing of the information processing system 1. The processing shown in Figure 4 is triggered by the startup of the server 10.

[0146] In step S1, the acquisition unit 112 acquires space information, spatial information, and sensor information received by the communication device 12 from the terminal device 40, and stores the space information, spatial information, and sensor information in the memory 13.

[0147] Next, in step S2, the reception unit 110 does not accept an instruction to execute the conversation scale display process from the terminal device 40 while the communication device 12 has not received an instruction to execute the conversation scale display process (NO in step S2), and repeats the process in step S2. The conversation scale display process is a process that calculates an index indicating the scale of communication in each of one or more areas and displays an image containing information indicating the index on the terminal device 40. When the communication device 12 receives an instruction to execute the conversation scale display process from the terminal device 40, the reception unit 110 accepts the instruction to execute the conversation scale display process (YES in step S2).

[0148] When the reception unit 110 receives an instruction to execute the conversation scale display process (YES in step S2), the estimation unit 113 refers to the space information, sensor information, and sensing information stored in the memory 13 and performs the processing in steps S3 to S8.

[0149] In step S3, the estimation unit 113 identifies one or more spaces where multiple people are present. After step S3, each of the one or more spaces identified in step S3 is treated as a target space, and the processing in steps S4 to S12 is performed.

[0150] In step S4, the estimation unit 113 determines whether the target space is for communication purposes or general-purpose purposes. If the target space is for communication purposes (communication in step S4), the estimation unit 113 proceeds to step S5. On the other hand, if the target space is for general-purpose purposes (general-purpose in step S3), the estimation unit 113 proceeds to step S6.

[0151] In step S5, the estimation unit 113 sets one or more spaces partitioned within the target space as one or more areas of the target space. After step S5, each of the one or more areas set for the target space is treated as a target area, and the processing in steps S9 to S11 is performed.

[0152] In step S6, the estimation unit 113 acquires sensing information about the target space from the memory 13. Next, in step S7, the estimation unit 113 identifies one or more spaces within the target space where a conversation is taking place, based on the sensing information about the target space acquired in step S6. Next, in step S8, the estimation unit 113 sets the one or more spaces identified in step S7 as one or more areas of the target space. After step S8, each of the one or more areas of the target space is treated as a target area, and the processing in steps S9 to S11 is performed.

[0153] In step S9, the estimation unit 113 acquires second target sensing information, which is sensing information obtained by sensing in the target area.

[0154] Next, in step S10, the estimation unit 113 estimates the amount of conversation in the target area based on the second target sensing information.

[0155] Next, in step S11, the calculation unit 114 calculates a first index, which is an index indicating the scale of communication in the target area, based on the amount of conversation in the target area estimated in step S10.

[0156] Once the processing in steps S9 to S11, which treats each of the one or more areas of the target space as a target area, is completed, step S12 is performed.

[0157] In step S12, the output unit 115 outputs information indicating a first indicator for one or more areas of the target space, in association with area information which is information relating to that one or more areas.

[0158] After step S12, if there is one or more spaces identified in step S3 that have not been processed in steps S4 to S12 (hereinafter referred to as unprocessed spaces) (NO in step S13), then the unprocessed spaces are treated as target spaces and the processing in steps S4 to S12 is performed.

[0159] If there are no unprocessed spaces and the processing in steps S4 to S12, which targets each of the spaces identified in step S3, is completed (YES in step S13), the process shown in Figure 4 is terminated.

[0160] In the first embodiment, a first index indicating the scale of communication in each area is calculated based on the amount of conversation taking place in each area. An image is displayed that associates this first index information with area information, making it easy to grasp the scale of communication in each area.

[0161] This makes it easier for facility users, for example, to find places suitable for communication. Furthermore, facility managers can consider measures to address communication issues, such as increasing communication among employees in areas where casual conversation is encouraged, if frequent large meetings are contributing to decreased employee satisfaction.

[0162] Thus, according to the first embodiment, it becomes possible to effectively consider measures to improve the utilization rate of each area and to revitalize communication among users. As a result, it becomes possible to improve the utilization rate of one or more areas and to revitalize communication among users.

[0163] (Second Embodiment) In the first embodiment, an example was described in which the estimation unit 113 estimates the amount of conversation in each of the one or more areas of the target space as a target area in step S10 (Figure 4) based on second target sensing information, which is sensing information obtained by sensing in the target area. Furthermore, an example was described in which the calculation unit 114 calculates a first index in step S11 (Figure 4) based on the amount of conversation estimated by the estimation unit 113.

[0164] In contrast, in the second embodiment, the estimation unit 113 treats each of the one or more areas of the target space as a target area and, in step S10 (Figure 4), estimates the number of people conversing in the target area based on the second target sensing information. In step S11 (Figure 4), the calculation unit 114 calculates the first index based on the estimated number of people. The details of the second embodiment will be described below, but detailed explanations of content that overlaps with the first embodiment will be omitted.

[0165] (First method for estimating the number of people speaking in the target area) The estimation unit 113 acquires from the memory 13, similar to the first estimation method for the amount of conversation in the target area in the first embodiment, sensing information including information indicating one or more locations within the target area, sensing information transmitted by the camera 32 that photographs the target area, and sensing information transmitted by the microphone 33 that picks up sounds generated within the target area, as second target sensing information.

[0166] The estimation unit 113 acquires first sensing information, which includes the most recent date and time within a predetermined error time from the present moment, from among the sensing information that includes information indicating multiple locations within the target area, as included in the second target sensing information, similar to the first method for estimating the amount of conversation in the target area in the first embodiment. If the estimation unit 113 is unable to acquire the first sensing information, it detects that there are no multiple people in the target area. In this case, the estimation unit 113 estimates the number of people having a conversation in the target area to be 0.

[0167] If the estimation unit 113 is able to acquire the first sensing information, it refers to the information indicating multiple locations within the target area contained in the first sensing information and estimates the number of locations within the target area indicated by the information as the number of people having a conversation in the target area.

[0168] Furthermore, the estimation method used by the estimation unit 113 to estimate the number of people conversing in the target area may be the estimation method shown below.

[0169] (Second method for estimating the number of people speaking in the target area) The estimation unit 113 acquires fourth sensing information transmitted by the microphone 33, which picks up sounds generated within the target area, from the second target sensing information, similar to the second method for estimating the amount of conversation in the target area in the first embodiment. The estimation unit 113 acquires fifth sensing information from the fourth sensing information, which includes the date and time within the period from the first date and time to the second date and time. The first date and time is the date and time when the stay of multiple people in the target area begins. The second date and time is the date and time when the stay of multiple people in the target area ends.

[0170] The estimation unit 113 extracts data indicating human voices from the data indicating sounds included in the fifth sensing information by performing known sound analysis processing. The estimation unit 113 obtains the number of people who spoke during the time multiple people were in the target area by performing known voiceprint recognition processing using the extracted voice data. The estimation unit 113 estimates the obtained number of people as the number of people having a conversation in the target area.

[0171] (A third method for estimating the number of people speaking in the target area) The estimation unit 113 acquires sixth sensing information transmitted by the camera 32 that photographs the target area from the second target sensing information, similar to the third method for estimating the amount of conversation in the target area in the first embodiment. The estimation unit 113 acquires seventh sensing information from the sixth sensing information that includes data showing an image indicating that two or more people's faces are facing each other and that one or more people's mouths are open, and that the date and time are within the period from the first date and time to the second date and time.

[0172] The estimation unit 113 obtains the number of people who spoke during the time multiple people were in the target area by performing known image analysis processing using data showing images included in the seventh sensing information. The estimation unit 113 estimates the obtained number of people as the number of people having a conversation in the target area.

[0173] The calculation unit 114 calculates the first indicator such that the larger the number of people having a conversation in the target area estimated by the estimation unit 113, the larger the scale of communication indicated by the first indicator.

[0174] For example, if the number of people having a conversation in the target area estimated by the estimation unit 113 is equal to or greater than a predetermined ninth threshold, the calculation unit 114 calculates "large" as the first indicator, indicating that the scale of communication is large.

[0175] The calculation unit 114 calculates "medium" as the first indicator, indicating that the scale of communication is moderate, if the estimated number of people is greater than or equal to the 10th threshold (which is smaller than the 9th threshold) and less than the 9th threshold.

[0176] The calculation unit 114 calculates "small" as the first indicator if the estimated number of people is greater than 0 and less than the second threshold, indicating that the scale of communication is small. The calculation unit 114 calculates "0" as the first indicator if the estimated number of people is 0, indicating that no communication is taking place.

[0177] In the second embodiment, a first index indicating the scale of communication in each area is calculated based on the number of people conversing in each area. An image is displayed that associates this first index information with area information, making it easy to grasp the scale of communication in each area.

[0178] (modified version) The following variations of this disclosure may be adopted.

[0179] (1) The output unit 115 may display information indicating the first indicator with a larger image the larger the scale of communication indicated by the first indicator. Figure 5 shows a second example of an image displayed on the display of the terminal device 40.

[0180] For example, as shown in Figure 5, the output unit 115 may indicate information indicating the first indicator "large" with an image of a predetermined color surrounding the target area (e.g., space 91B) with a dashed line. The output unit 115 may indicate information indicating the first indicator "small" with an image of the predetermined color surrounding a space obtained by reducing the target area (e.g., space 91E) by a predetermined first reduction ratio (e.g., 50%) with a dashed line. The output unit 115 may indicate information indicating the first indicator "medium" with an image of the predetermined color surrounding a space obtained by reducing the target area (e.g., space 91D) by a second reduction ratio greater than the first reduction ratio but less than 100% (e.g., 80%) with a dashed line.

[0181] (2) The calculation unit 114 may further calculate a second index indicating the quality of conversations taking place in the target area based on second target sensing information, which is sensing information obtained by sensing in the target area.

[0182] Accordingly, the output unit 115 may output information indicating the second indicator calculated for the target area, in association with information indicating the first indicator calculated for the same target area. This configuration can be realized, for example, as follows.

[0183] In step S12 (Figure 4), the calculation unit 114 acquires second target sensing information in the same manner as the first method for estimating the amount of conversation in the target area in the first embodiment. The calculation unit 114 acquires fourth sensing information transmitted by the microphone 33, which picks up sounds generated in the target area, from the second target sensing information in the same manner as the second method for estimating the number of people talking in the target area in the second embodiment. The calculation unit 114 acquires fifth sensing information from the fourth sensing information, which includes the date and time within the period from the first date and time to the second date and time. The first date and time is the date and time when the stay of multiple people in the target area begins. The second date and time is the date and time when the stay of multiple people in the target area ends.

[0184] The calculation unit 114 extracts data representing human voices from the data representing sounds included in the fifth sensing information by performing known sound analysis processing. The calculation unit 114 calculates the sum of the number of speakers at predetermined time intervals during the period from the first date and time to the second date and time by performing known voiceprint recognition processing using the extracted data representing human voices. The calculation unit 114 calculates the second index such that the larger the calculated sum, the higher the quality of communication in the second index.

[0185] Figure 6 shows an example of the first calculation of the second indicator. For example, suppose the predetermined time used to calculate the second indicator is set to 10 minutes. Also, as shown in Figure 6, suppose that during the 30 minutes from the first date and time to the second date and time, two users, "A" and "B," spoke in the first 10 minutes, users "A," "B," and "C" spoke in the next 10 minutes, and five users, "A," "B," "C," "D," and "E," spoke in the last 10 minutes. In this case, the calculation unit 114 calculates the sum of the number of speakers every 10 minutes during the 30 minutes from the first date and time to the second date and time as "10 (=2+3+5)."

[0186] The calculation unit 114 calculates "High" as the second indicator if the sum of the number of speakers at predetermined intervals during the period from the first date and time to the second date and time is equal to or greater than a predetermined 11th threshold, indicating that the conversation is bidirectional and the quality of communication is high. On the other hand, if the sum is less than the 11th threshold, the calculation unit 114 calculates "Low" as the second indicator, indicating that the conversation is one-way and the quality of communication is low.

[0187] Furthermore, the method for calculating the second indicator by the calculation unit 114 is not limited to the above. For example, the calculation unit 114 may calculate the number of times a speaker was replaced by another speaker during the period from the first date and time to the second date and time by performing known voiceprint recognition processing using data representing a person's voice extracted from the fifth sensing information. The calculation unit 114 may then calculate the second indicator such that a larger number indicates a higher quality of communication.

[0188] Figure 7 shows a second example of the calculation of the second indicator. For example, as shown in Figure 7, suppose that during the 60 minutes from the first date and time to the second date and time, user "A" speaks first, then user "B" speaks, then user "C" speaks, and finally user "A" speaks again. In this case, the calculation unit 114 calculates "3" as the number of times the speaker changed to another speaker during the 60 minutes from the first date and time to the second date and time.

[0189] In this case, if the sum of the number of speakers at predetermined intervals during the period from the first date and time to the second date and time is equal to or greater than a predetermined 12th threshold, the calculation unit 114 calculates "high" as the second indicator, indicating that the conversation is bidirectional and the quality of communication is high. On the other hand, if the sum is less than the 12th threshold, the calculation unit 114 calculates "low" as the second indicator, indicating that the conversation is one-way and the quality of communication is low.

[0190] The output unit 115 associates the information indicating the second indicator "high" with the information indicating the first indicator by brightening the brightness of the superimposed image, which is superimposed as information indicating the first indicator, by a predetermined amount. The output unit 115 associates the information indicating the second indicator "low" with the information indicating the first indicator by darkening the brightness of the superimposed image, which is superimposed as information indicating the first indicator, by a predetermined amount. Figure 8 shows a third example of an image displayed on the display of the terminal device 40.

[0191] For example, Figure 8 shows an example in which, similar to modification (1), the output unit 115 superimposes information indicating the first indicator "large" for the area in the map image (Figure 5) at a position corresponding to the area of ​​space 91B in the map image, and then associates information indicating the second indicator "high" for the area, resulting in the brightness of the image becoming brighter by a predetermined amount.

[0192] Figure 8 shows an example in which, similar to modification (1), the output unit 115 superimposed information indicating the first indicator "Large" for the area corresponding to the area of ​​space 91F in the map image (Figure 5), and then associated the information indicating the second indicator "Low" for the area, resulting in the brightness of the image becoming darker by a predetermined amount.

[0193] Figure 8 shows an example in which, similar to modification (1), the output unit 115 superimposed information indicating the first indicator "medium" for the area of ​​space 993A in the map image (Figure 5) as information indicating the first indicator "high" for the area, and then associated this with information indicating the second indicator "high" for the area, resulting in the brightness of the image becoming brighter by a predetermined amount.

[0194] Figure 8 shows an example in which, similar to modification (1), the output unit 115 superimposed information indicating the first indicator "medium" for the area in the map image (Figure 5) at a position corresponding to the area of ​​space 91D in the map image, and then associated information indicating the second indicator "low" for the area, resulting in the brightness of the image becoming darker by a predetermined amount.

[0195] Figure 8 shows an example in which, similar to modification (1), the output unit 115 superimposed information indicating the first indicator "small" for the area in the map image (Figure 5) at the location corresponding to the area of ​​space 91E in the map image, and then associated information indicating the second indicator "high" for the area, resulting in the brightness of the image becoming brighter by a predetermined amount.

[0196] Figure 8 shows an example in which, similar to modification (1), the output unit 115 superimposed information indicating the first indicator "small" for the area of ​​space 992A in the map image (Figure 5) as information indicating the first indicator "low" for the area, and then associated this with information indicating the second indicator "low" for the area, resulting in the brightness of the image becoming darker by a predetermined amount.

[0197] Furthermore, the method by which the output unit 115 associates information indicating the second index with information indicating the first index is not limited to the above. For example, the output unit 115 may associate information indicating the second index "high" with information indicating the first index by superimposing a predetermined first icon that gives a bright impression onto the image superimposed as information indicating the first index. Alternatively, the output unit 115 may associate information indicating the second index "low" with information indicating the first index by superimposing a predetermined second icon that gives a dark impression onto the image superimposed as information indicating the first index. Figure 9 shows a fourth example of an image displayed on the display of the terminal device 40.

[0198] For example, Figure 9 shows an example in which the output unit 115, similar to modification (1), superimposes information indicating the second indicator "high" for the area on an image (Figure 5) that is superimposed as information indicating the first indicator "large" for the area corresponding to the area of ​​space 91B in the map image, and as a result, the first icon E1 is superimposed on the image.

[0199] Figure 9 shows an example in which the output unit 115, similar to modification (1), superimposes information indicating the second indicator "low" for the area on an image (Figure 5) that has been superimposed as information indicating the first indicator "large" for the area corresponding to the area of ​​space 91F in the map image, and as a result, the second icon E2 is superimposed on the image.

[0200] Figure 9 shows an example in which, similar to modification (1), the output unit 115 superimposes information indicating the first indicator "medium" for the area of ​​space 993A in the map image (Figure 5) as information indicating the first indicator "high" for the area, and then associates this information with information indicating the second indicator "high" for the area, resulting in the first icon E1 being superimposed on the image.

[0201] Figure 9 shows an example in which the output unit 115, similar to modification (1), superimposes information indicating the second indicator "low" for the area of ​​space 91D in the map image onto an image (Figure 5) that superimposes information indicating the first indicator "medium" for the area, and as a result, the second icon E2 is superimposed on the image.

[0202] Figure 9 shows an example in which the output unit 115, similar to modification (1), superimposes information indicating the second indicator "high" for the area on an image (Figure 5) that is superimposed as information indicating the first indicator "small" for the area corresponding to the area of ​​space 91E in the map image, and as a result, the first icon E1 is superimposed on the image.

[0203] Figure 9 shows an example in which the output unit 115, similar to modification (1), superimposes information indicating the second indicator "low" for the area of ​​space 992A in the map image (Figure 5) as information indicating the first indicator "small" for that area, and associates this information with information indicating the second indicator "low" for that area, resulting in the second icon E2 being superimposed on the image.

[0204] According to this modified version, information representing the second indicator is displayed in correspondence with information representing the first indicator, making it easy to grasp not only the scale of communication in each area but also the quality of conversations. This makes it possible to effectively consider measures to improve the utilization rate in each area and to revitalize high-quality communication among users.

[0205] (3) The output unit 115 may display information indicating the first index using images showing ripples with a higher wavenumber as the first index increases (hereinafter referred to as ripple images). Specifically, the output unit 115 may display information indicating a "large" first index using a ripple image with a predetermined first wavenumber (for example, 3). The output unit 115 may display information indicating a "small" first index using a ripple image with a second wavenumber (for example, 1) which is less than the first wavenumber. The output unit 115 may display information indicating a "medium" first index using a ripple image with a third wavenumber (for example, 2) which is less than the first wavenumber and more than the second wavenumber.

[0206] Figure 10 shows an example of a ripple image output as information indicating the first indicator. Figure 10 shows an example in which the output unit 115 superimposes a ripple image E3 with a wavenumber of 3 at a position corresponding to area A1 in the map image, as information indicating the first indicator "large" for area A1.

[0207] Furthermore, the output unit 115 may change the number and color of the ripple images superimposed as information indicating the first indicator of an area at a position corresponding to an area in the map image, in order to associate information indicating the second indicator with information indicating the first indicator of that area, according to the second indicator of that area.

[0208] Figure 11 shows another example of a ripple image output as information indicating the first indicator. Figure 11 shows an example in which the output unit 115 superimposes three ripple images E3a, E3b, and E3c, each with a different color, at the position corresponding to area A1 in the map image, as information indicating the first indicator "large" for area A1, when the second indicator for area A1 is "high".

[0209] (4) The acquisition unit 112 may acquire information (hereinafter referred to as status information) indicating the usage status of one or more spaces provided in the facility, which has been received by the communication device 12 from an external device such as a terminal device 40. The status information includes identification information for each space, information indicating whether each space is in use or not, and information indicating the number of people who can use each space.

[0210] Accordingly, the output unit 115 may output a table that associates the situation information with information indicating the first indicator for each of the one or more areas, and associates this table with the area information, which is information relating to the one or more areas.

[0211] Figure 12 shows a fifth example of an image displayed on the terminal device 40's display. For example, Figure 12 shows an example of an image displayed on the terminal device 40's display that associates the map image (floor map) G1 output by the output unit 115 with table G2.

[0212] Map image G1 is a map image of floor 90, which includes two general-purpose spaces 9A and 9C and four communication spaces 9B, 9D, 9E, and 9F, as shown in Figure 2.

[0213] Table G2 is a table that associates situational information for the 10 spaces partitioned into the 6 spaces mentioned above with information indicating the first indicator for each of the one or more areas contained within each space. Table G2 includes columns for location, purpose, situation, number of people, and communication.

[0214] The output unit 115 displays the identification information of each space included in the status information acquired by the acquisition unit 112 in the location column. The output unit 115 refers to the space information stored in the memory 13 and displays the use of the space including each space in the use column. The output unit 115 displays in the status column information included in the status information acquired by the acquisition unit 112 indicating whether each space is in use or not. The output unit 115 displays in the number of people column information included in the status information acquired by the acquisition unit 112 indicating the number of people who can use each space. The output unit 115 displays in the communication column information indicating the first indicator of each of the one or more areas included in each space.

[0215] For example, Figure 12 shows an example where the output unit 115 displays various information in Table G2 when the first indicator for the area set in space 91B, which is partitioned within space 9B (Figure 2) for communication purposes, is calculated to be "large".

[0216] The Location column displays the identification information for Space 91B, "91B". The Usage column displays the usage of the space including Space 91B, "Meeting Room". The Status column displays "●In Use", indicating that Space 91B is in use. The Number of People column displays "Medium", indicating that the number of people who can use Space 91B is medium. The Communication column displays "Active", indicating that the primary indicator for the area of ​​Space 91B is "Large".

[0217] Furthermore, Figure 12 shows an example in which, when three spaces 991C, 992C, and 993C (Figure 3) within space 91C (Figure 2), which is partitioned within the general-purpose space 9C (Figure 2), are set as areas of space 9C, the first indicator for each area is calculated as "small," "medium," and "large," and the output unit 115 displays various information in Table G2.

[0218] The Location column displays the identification information for Space 91C, "91C". The Usage column displays the usage of the space containing Space 91C, "General Purpose (Large)". The Status column displays "●In Use", indicating that Space 91C is currently in use. The Number of People column displays "Large", indicating that Space 91C is available to a large number of people. The Communication column displays "Silence", "Dialogue", and "Active", indicating that the primary indicator for each of the three areas set up within Space 91C is "Small", "Medium", and "Large".

[0219] This modified version allows for simultaneous understanding of the usage status of each space and the scale of communication within one or more designated areas within each space. This enables efficient consideration of measures to improve the utilization rate of each partitioned space and to revitalize communication among users.

[0220] (5) The estimation unit 113 may estimate the amount of conversation in the target area, similar to the first embodiment, and also estimate the number of people in the target area, similar to the second embodiment. In conjunction with this, the calculation unit 114 may calculate a first index for the target area based on the product of the amount of conversation in the target area and the number of people in the target area.

[0221] For example, suppose the estimation unit 113 estimates the amount of conversation in the target area using the first estimation method for the amount of conversation in the target area of ​​the first embodiment, and estimates the number of people in the target area using any of the first to third estimation methods for the number of people in conversation in the target area of ​​the second embodiment.

[0222] In this case, the calculation unit 114 calculates "Large" as the first indicator, indicating a large scale of communication, if the product of the estimated conversation volume (e.g., 65) and the estimated number of people (e.g., 2) (e.g., 130 (=65 × 2)) is greater than or equal to a predetermined 13th threshold (e.g., 100). The calculation unit 114 calculates "Medium" as the first indicator, indicating a moderate scale of communication, if the product of the estimated conversation volume (e.g., 30) and the estimated number of people (e.g., 3) (e.g., 90 (=30 × 3)) is greater than or equal to a 14th threshold (e.g., 50) which is smaller than the 13th threshold, and less than the 13th threshold.

[0223] The calculation unit 114 calculates "small" as the first indicator if the product of the estimated conversation volume (e.g., 10) and the estimated number of people (e.g., 4) (e.g., 40 (=10×4)) is greater than 0 and less than the 14th threshold. The calculation unit 114 also calculates "0" as the first indicator if the estimated conversation volume or the estimated number of people is 0, indicating that no communication took place.

[0224] The estimation unit 113 estimates the amount of conversation in the target area using the second estimation method for the amount of conversation in the target area of ​​the first embodiment, and estimates the number of people in the target area using any of the first to third estimation methods for the number of people in conversation in the target area of ​​the second embodiment.

[0225] In this case, the calculation unit 114 calculates "Large" as the first indicator, indicating a large scale of communication, if the product of the estimated conversation volume (e.g., 480) and the estimated number of people (e.g., 3) (e.g., 1440 (=480 × 3)) is greater than or equal to a predetermined 15th threshold (e.g., 1200). The calculation unit 114 calculates "Medium" as the first indicator, indicating a moderate scale of communication, if the product of the estimated conversation volume (e.g., 340) and the estimated number of people (e.g., 3) (e.g., 1020 (=340 × 3)) is greater than or equal to a 16th threshold (e.g., 600) which is smaller than the 15th threshold, and less than the 15th threshold.

[0226] The calculation unit 114 calculates "small" as the first indicator if the product of the estimated conversation volume (e.g., 250) and the estimated number of people (e.g., 2) (e.g., 500 (=250 × 2)) is greater than 0 and less than the 16th threshold. Furthermore, if the estimated conversation volume or the estimated number of people is 0, the calculation unit 114 calculates "0" as the first indicator, indicating that no communication took place.

[0227] The estimation unit 113 estimates the amount of conversation in the target area using the third estimation method for the amount of conversation in the target area of ​​the first embodiment, and estimates the number of people in the target area using any of the first to third estimation methods for the number of people in conversation in the target area of ​​the second embodiment.

[0228] In this case, the calculation unit 114 calculates "Large" as the first indicator, indicating a large scale of communication, if the product of the estimated conversation volume (e.g., 105) and the estimated number of people (e.g., 3) (e.g., 315 (=105 × 3)) is greater than or equal to a predetermined 17th threshold (e.g., 300). The calculation unit 114 calculates "Medium" as the first indicator, indicating a moderate scale of communication, if the product of the estimated conversation volume (e.g., 60) and the estimated number of people (e.g., 3) (e.g., 180 (=60 × 3)) is greater than or equal to the 18th threshold (e.g., 100) which is smaller than the 17th threshold, and less than the 17th threshold.

[0229] The calculation unit 114 calculates "small" as the first indicator if the product of the estimated conversation volume (e.g., 39) and the estimated number of people (e.g., 2) (e.g., 78 (=39 × 2)) is greater than 0 and less than the 18th threshold. The calculation unit 114 also calculates "0" as the first indicator if the estimated conversation volume or the estimated number of people is 0, indicating that no communication took place.

[0230] The estimation unit 113 estimates the amount of conversation in the target area using the fourth estimation method for the amount of conversation in the target area of ​​the first embodiment, and estimates the number of people in the target area using any of the first to third estimation methods for the number of people in conversation in the target area of ​​the second embodiment.

[0231] In this case, the calculation unit 114 calculates "Large" as the first indicator, indicating a large scale of communication, if the product of the estimated conversation volume (e.g., 615) and the estimated number of people (e.g., 4) (e.g., 2460 (=615 × 4)) is greater than or equal to a predetermined 19th threshold (e.g., 2000). The calculation unit 114 calculates "Medium" as the first indicator, indicating a moderate scale of communication, if the product of the estimated conversation volume (e.g., 555) and the estimated number of people (e.g., 3) (e.g., 1665 (=555 × 3)) is greater than or equal to a 20th threshold (e.g., 1000) which is smaller than the 19th threshold, and less than the 19th threshold.

[0232] The calculation unit 114 calculates "small" as the first indicator if the product of the estimated conversation volume (e.g., 480) and the estimated number of people (e.g., 2) (e.g., 960 (=480 × 2)) is greater than 0 and less than the 20th threshold. Furthermore, if the estimated conversation volume or the estimated number of people is 0, the calculation unit 114 calculates "0" as the first indicator, indicating that no communication took place.

[0233] (6) External devices such as terminal devices 40 that are connected to the server 10 via the network 80 in a communicable manner may transmit method specification information to the server 10 along with an instruction to execute the conversation scale display process. The method specification information is information that specifies which method to adopt from the methods described in the above embodiments and modifications for estimating the amount of conversation in the target area and the number of people having a conversation in the target area, the methods for calculating the first and second indicators of the target area, and the methods for outputting information indicating the first and second indicators.

[0234] Accordingly, in step S2 (Figure 4), when the reception unit 110 receives an instruction to execute the conversation scale display process (YES in step S2), the communication device 12 may perform the processing from step S3 onward in the manner specified by the method specification information received along with the instruction to execute the conversation scale display process.

[0235] (7) The estimation unit 113 may set one or more areas of the target space regardless of the use of the target space. Specifically, in the process shown in Figure 4, steps S4 and S5 may be omitted, and the process from step S6 onwards may be performed after step S3. Alternatively, in the process shown in Figure 4, steps S4, S6, S7, and S8 may be omitted, and the process from step S5 onwards may be performed after step S3.

[0236] (8) For each of the one or more spaces provided in the facility, one or more areas may be pre-defined within each space in which multiple people may be present. The space information may also include information indicating the shape, size, and location of the one or more pre-defined areas in each space.

[0237] In this case, steps S4 to S8 may be omitted from the process shown in Figure 4, and after step S3, the estimation unit 113 may refer to the space information and perform the processing in steps S9 to S11, treating each of the one or more pre-set areas of the target space as a target area. [Industrial applicability]

[0238] This disclosure is useful in improving the utilization rate of spaces in office buildings and public facilities, and in revitalizing communication among facility users. [Explanation of symbols]

[0239] 1: Information Processing System 10: Server (information processing equipment and computer) 11: Processor 110: Reception Department 111: Collection Department 112: Acquisition Department 113: Estimation section 114: Calculation Unit 115: Output section 40: Terminal device 9A~9F: Space G1: Map image G2: A table that links situational information with information showing the first indicator.

Claims

1. A method of information processing in a computer, Obtaining area information, which is information about one or more areas, Based on sensing information obtained through sensing in each area, the system estimates at least one of the number of people conversing in each area and the amount of conversation in each area. Based on at least one of the aforementioned number of people and conversation volume, a first indicator showing the scale of communication in each area is calculated, Outputting information indicating the first indicator in association with the area information, Information processing methods including

2. The one or more areas are set according to the intended use of the space including the one or more areas. The information processing method according to claim 1.

3. If the use of the space is for communication purposes, then one or more partitioned spaces within the space are set as the one or more areas. The information processing method according to claim 2.

4. If the use of the aforementioned space is for general purposes, then one or more spaces within the aforementioned space where a conversation is taking place are set as the one or more areas. The information processing method according to claim 2.

5. At least one of the aforementioned number of people and conversation volume is the conversation volume, Calculating the first indicator is The first indicator is calculated such that the larger the amount of conversation, the larger the scale of the communication. The information processing method according to any one of claims 1 to 4.

6. Estimating at least one of the aforementioned number of people and conversation volume is: Based on the aforementioned sensing information, estimate the time spent by multiple people in each area, Based on the aforementioned stay time, the amount of conversation is estimated, The information processing method according to claim 5, including the following:

7. Estimating at least one of the aforementioned number of people and conversation volume is: Based on the aforementioned sensing information, estimate the time spent by multiple people in each area, Based on the aforementioned sensing information, the volume of conversations taking place in each area is estimated, Based on the aforementioned duration of stay and the volume of the conversation, the amount of conversation is estimated, The information processing method according to claim 5, including the following:

8. Estimating at least one of the aforementioned number of people and conversation volume is: Based on the aforementioned sensing information, estimate the time spent by multiple people in each area, Based on the aforementioned sensing information, the elapsed time from the start to the end of a conversation taking place in each area is estimated. Based on the aforementioned stay time and elapsed time, the amount of conversation is estimated, The information processing method according to claim 5, including the following:

9. At least one of the aforementioned number of people and conversation volume is the aforementioned number of people, Calculating the first indicator is The first indicator is calculated such that the larger the number of people, the larger the scale of the communication. The information processing method according to any one of claims 1 to 4.

10. The aforementioned sensing information includes images taken of each area. Estimating at least one of the aforementioned number of people and conversation volume is: From the aforementioned image, first information indicating the orientation of multiple people's faces or second information indicating mouth movements is detected. This includes estimating the number of people based on the first or second information, The information processing method according to claim 9.

11. The aforementioned sensing information includes sounds collected in each area. Estimating at least one of the aforementioned number of people and conversation volume is: To detect human voices from the aforementioned sound, This includes estimating the number of people based on the aforementioned audio, The information processing method according to claim 9.

12. The detection of the first information is From the aforementioned image, third information indicating the skeletons of the multiple people is detected, Includes detecting the first information based on the third information, The information processing method according to claim 10.

13. The area information includes a map image of a space containing one or more of the aforementioned areas. Outputting information that shows the first indicator means This includes displaying information indicating the first indicator at a location corresponding to each area in the aforementioned map image, The information processing method according to claim 1.

14. This further includes calculating a second indicator showing the quality of conversations taking place in each area based on the aforementioned sensing information, Outputting information that shows the first indicator means This further includes outputting information indicating the second indicator in correspondence with information indicating the first indicator. The information processing method according to claim 13.

15. Outputting information that shows the first indicator means To obtain status information showing the usage status of each area, This includes outputting a table that associates the aforementioned situation information with the information indicating the first indicator, in association with the area information. The information processing method according to claim 1.

16. An information processing device including a processor, The aforementioned processor, Obtaining area information, which is information about one or more areas, Based on sensing information obtained through sensing in each area, the system estimates at least one of the number of people conversing in each area and the amount of conversation in each area. Based on at least one of the aforementioned number of people and conversation volume, a first indicator showing the scale of communication in each area is calculated, Outputting information indicating the first indicator in association with the area information, An information processing device that performs the following actions.

17. On the computer, Obtaining area information, which is information about one or more areas, Based on sensing information obtained through sensing in each area, the system estimates at least one of the number of people conversing in each area and the amount of conversation in each area. Based on at least one of the aforementioned number of people and conversation volume, a first indicator showing the scale of communication in each area is calculated, Outputting information indicating the first indicator in association with the area information, An information processing program that executes [something].

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