Information processing method and information processing apparatus

Abstract

To allow a user to easily know a reason for a congestion degree and easily select a destination according to the reason for the congestion degree.SOLUTION: A management server 200 includes a congestion information processing unit 204 extracting a specific location where at least one user among a plurality of users exists from among registered locations based on position information included in user information, calculating a congestion degree at the specific location, and estimating a reason for the congestion degree at the specific location based on posted information regarding the specific location.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to an information processing method and an information processing apparatus for providing information on congestion.

Background Art

[0002] Conventionally, there is a technology for providing information on congestion to a predetermined device. For example, a technology has been proposed in which destination information with a larger number of visitors than a preset threshold is extracted, and the extracted destination information is transmitted to an in-vehicle device (see, for example, Patent Document 1).

Prior Art Document

Patent Document

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the above-described conventional technology, a facility with a larger number of visitors than a preset threshold can be guided to the passengers of a vehicle as a popular facility. However, it is difficult for the passengers of the vehicle to know the reason for the large number of visitors.

[0005] An object of the present invention is to enable the reason for the degree of congestion to be easily known and for the user to easily select a destination according to the reason for the degree of congestion.

Means for Solving the Problems

[0006] One aspect of the present invention is an information processing method for providing information on the congestion of registered locations to a plurality of users. This information processing method includes user information related to at least one user among the plurality of users, including the position information of the at least one user, and posted on a communication service constructed on a predetermined network by using an electronic device by at least one user among the plurality of users. This is information and includes the posted text.A data acquisition process to obtain posted information, a calculation process to extract specific locations from registered locations where at least one of multiple users is present based on location information included in user information, and calculate the congestion level at those specific locations, and data acquisition process to obtain posted information related to those specific locations. The system extracts frequently occurring words from the posts contained within, and based on the location and time information obtainable from the post information and the extracted frequently occurring words, it performs a search process using devices connected to a predetermined network, and based on the search results from the search process... Estimation process to estimate the reason for congestion at a specific location, and congestion at a specific location Along with, at least one of the search results and the reason for the congestion estimated based on those search results. This includes an output process that outputs congestion information including the following: [Effects of the Invention]

[0007] According to the present invention, the reason for congestion can be easily determined, and users can easily select a destination according to the reason for congestion. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 shows an example of the system configuration of an information processing system. [Figure 2] Figure 2 is a block diagram showing an example of the functional configuration of the management server and the SNS server. [Figure 3] Figure 3 is a schematic diagram illustrating the various pieces of information stored in the vehicle information database. [Figure 4] Figure 4 is a schematic diagram illustrating the various pieces of information stored in the user information database. [Figure 5] Figure 5 is a schematic diagram illustrating the various pieces of information stored in the congestion information database. [Figure 6] Figure 6 is a schematic diagram illustrating the various pieces of information stored in the submission information database. [Figure 7] Figure 7 shows an example of the relationship between the congestion levels of facilities and parking lots estimated by the management server. [Figure 8] Figure 8 is a simplified diagram showing an example of a user-submitted post and image using an electronic device. [Figure 9] Figure 9 shows an example of output when congestion information is output from an information output device. [Figure 10]Figure 10 shows an example of output when congestion information is output from an information output device. [Figure 11] Figure 11 is a flowchart showing an example of information provision processing in a control device. [Figure 12] Figure 12 is a flowchart showing an example of congestion information processing on the management server. [Figure 13] Figure 13 is a flowchart showing an example of congestion information output processing in an information output device. [Modes for carrying out the invention]

[0009] Embodiments of the present invention will be described below with reference to the attached drawings.

[0010] [Example of an information processing system configuration] Figure 1 shows an example of the system configuration of the information processing system 10. The information processing system 10 is a communication system that performs processing to provide information about congestion at registered locations registered in the management server 200 to users U1 to UM and vehicles C1 to CM. In Figure 1, a simplified example is shown where there are N users (N≧2) U1 to UM and M vehicles (M≧2) C1 to CM. User U1 is assumed to be the user who owns vehicle C1.

[0011] The information processing system 10 is composed of a plurality of devices connected via a network 20. For example, control devices 100 to 100N, information output devices 150 to 150N, electronic devices MC1 to MCM, a management server 200, and an SNS (Social Networking Service) server 300 are connected via the network 20. Note that, for communication between these devices, communication using wired communication or communication using wireless communication is performed. Also, for communication between these devices, direct communication between devices may be performed in addition to communication via the network 20. Further, the electronic devices MC1 to MCM are portable information processing devices, such as information processing devices like smartphones, tablet terminals, and portable personal computers.

[0012] The control device 100 and the information output device 150 are in-vehicle devices installed in the vehicle C1. The control device 100, for example, acquires vehicle information regarding the vehicle C1 and user information regarding the passengers of the vehicle C1, and transmits each acquired piece of information to the management server 200. For example, the vehicle information and the user information are acquired using various sensors installed in the vehicle C1.

[0013] The information output device 150 is a device capable of outputting the congestion information transmitted from the management server 200. The information output device 150 is realized by, for example, a tablet terminal, a car navigation device, or an IVI (In-Vehicle Infotainment). Examples of the output of the congestion information are shown in FIGS. 9 and 10.

[0014] The management server 200 is an information processing device and an information presentation device that realizes a congestion information providing service for providing information on the congestion of registered locations to a plurality of users. When using this congestion information providing service, a user needs to register for using the congestion information providing service. For a user who owns a vehicle, the vehicle can be used as the main registration, and the user can be registered by associating the user with the vehicle. Also, for a user who does not own a vehicle, user registration can be performed with the user as the main registration. Note that the configuration of the management server 200 will be described in detail with reference to FIG. 2 and the like.

[0015] The SNS server 300 is an information processing device that realizes a Web service, such as an SNS service, in which registered users can communicate and exchange various contents with each other. Here, SNS means a communication service constructed on a predetermined network, for example, the Internet. Note that the SNS shown in this embodiment includes a service that can post and record various information on a website using a predetermined network, for example, the Internet. As a user ID for using the SNS service, for example, a telephone number, an e-mail address, an SNS ID of a Web service, etc. can be used. Note that the configuration of the SNS server 300 will be described in detail with reference to FIG. 2 and the like.

[0016] [Configuration Example of Management Server and SNS Server] FIG. 2 is a block diagram showing a functional configuration example of the management server 200 and the SNS server 300.

[0017] The management server 200 includes a communication unit 201, a control unit 202, a storage unit 203, and a congestion information processing unit 204.

[0018] The communication unit 201 exchanges various information with other devices using wired communication or wireless communication based on the control of the control unit 202.

[0019] The control unit 202 controls each part based on various programs stored in the memory unit 203. The control unit 202 is implemented by a processing unit such as a CPU (Central Processing Unit).

[0020] The memory unit 203 is a storage medium for storing various types of information. For example, the memory unit 203 stores various types of information necessary for the control unit 202 to perform various processes (e.g., control programs, vehicle information DB 210, user information DB 230, congestion information DB 240). The memory unit 203 also stores various types of information acquired via the communication unit 201. As the memory unit 203, for example, ROM (Read Only Memory), RAM (Random Access Memory), HDD (Hard Disk Drive), SSD (Solid State Drive), or a combination thereof can be used. The vehicle information DB 210, user information DB 230, and congestion information DB 240 will be explained in detail with reference to Figures 3 to 5.

[0021] The congestion information processing unit 204 uses the vehicle information DB 210, user information DB 230, and congestion information DB 240 to perform processing to determine the degree of congestion, the reason for congestion, etc., at the registered location, and stores the determined information in the congestion information DB 240. The congestion information processing unit 204 also provides the information stored in the congestion information DB 240 in response to congestion information requests from information output devices 150 to 150N and electronic devices MC1 to MCM. The congestion information processing unit 204 is implemented by a processing unit such as a CPU. The processing to determine the degree of congestion, the reason for congestion, etc. will be explained in detail with reference to Figures 7 and 8. The processing to provide each piece of information will be explained in detail with reference to Figures 9 and 10.

[0022] The SNS server 300 comprises a communication unit 301, a control unit 302, and a storage unit 303.

[0023] The communication unit 301, based on the control of the control unit 302, exchanges various types of information with other devices using wired or wireless communication.

[0024] The control unit 302 controls each part based on various programs stored in the memory unit 303. The control unit 302 is implemented by a processing unit such as a CPU.

[0025] The memory unit 303 is a storage medium for storing various types of information. For example, the memory unit 303 stores various types of information necessary for the control unit 302 to perform various processes (e.g., control programs, submission information DB 310, user information DB 320). The memory unit 303 also stores various types of information acquired via the communication unit 301. The memory unit 303 can be, for example, ROM, RAM, HDD, SSD, or a combination thereof.

[0026] The Post Information DB310 is a database that stores post information transmitted from electronic devices MC1 to MCM using the SNS service. The Post Information DB310 will be explained in detail with reference to Figure 6.

[0027] User Information DB320 is a database that stores user information for users U1 to UM of electronic devices MC1 to MCM that use the SNS service. User information includes SNS ID, attribute information, etc.

[0028] [Example of Vehicle Information Database Contents] Figure 3 is a schematic diagram illustrating the various pieces of information stored in the Vehicle Information DB210. The Vehicle Information DB210 is a database for managing vehicle information related to vehicles C1 through CN that use the congestion information provision service.

[0029] The vehicle information DB210 stores user ID212, SNS ID213, vehicle type214, location information215, occupant information216, vehicle on / off information217, vehicle speed information218, traffic congestion information219, travel trajectory220, image information221, and SNS usage information222, all associated with vehicle ID211. Some of this information is sequentially transmitted from control devices 100 to 100N installed in vehicles C1 to CN to the management server 200 and stored therein.

[0030] Vehicle ID 211 is identification information used to identify vehicles C1 through CN that use the congestion information service. For example, "C001" is stored as the vehicle ID for vehicle C1 in vehicle ID 211.

[0031] User ID 212 contains information about the user who owns the vehicle corresponding to vehicle ID 211. For example, "U01" is stored as User ID 212 for user U1, who owns vehicle C1.

[0032] SNSID213 is identification information used to identify each user in the SNS service registered by the user who owns the vehicle corresponding to vehicle ID211. This identification information is sometimes referred to as account information. In this embodiment, for the sake of simplicity, we will explain assuming that users U1 to UM have registered to use the SNS service provided by the SNS server 300. For example, "SNS001" is stored in SNSID232 as the SNSID of user U1.

[0033] Vehicle type 214 is information indicating the type of vehicle corresponding to vehicle ID 211. For example, the model number, size, color, and whether or not it is an electric vehicle are stored in vehicle type 214. In this embodiment, for the sake of simplicity, an example is shown where the vehicle size is used as vehicle type 214. For example, one of the following sizes, large, medium, or small, is stored as the vehicle size. This makes it possible to obtain the size of vehicles parked in the parking lot of the registered location managed by the management server 200. In other words, it is possible to identify the size of vehicles that can be parked in that parking lot.

[0034] Location information 215 is information indicating the location of the vehicle corresponding to vehicle ID 211. For example, the current location of the vehicle is acquired using various sensors installed on each vehicle, such as a location information acquisition unit, and this information, which associates the latitude and longitude of the current location with the time of acquisition, is transmitted to the management server 200 and stored in location information 215. A GNSS (Global Navigation Satellite System) receiver can be used as the location information acquisition unit. Based on location information 215, the current location of each vehicle can be determined.

[0035] The occupant information 216 is information about the occupants riding in the vehicle corresponding to the vehicle ID 211. For example, the number of occupants, their ages, genders, etc., are obtained using various sensors installed in each vehicle, and this information is transmitted to the management server 200 and stored as occupant information 216. For example, the number of occupants riding in a vehicle can be obtained using gravity sensors, pressure sensors, or any sensors for object recognition installed in the vehicle. In addition, the number of occupants riding in a vehicle and the ages, genders, etc., of each occupant can be obtained using images obtained by an image acquisition device installed in the vehicle, such as an in-vehicle camera. In this way, the number of occupants, their ages, genders, etc., of occupants riding in or who were riding in each vehicle can be identified based on the occupant information 216.

[0036] Vehicle on / off information 217 is information regarding the on / off status of the vehicle corresponding to vehicle ID 211. For example, the time when the vehicle was turned on and the time when the vehicle was turned off are sent to the management server 200 and stored as vehicle on / off information 217. Based on this vehicle on / off information 217, it is possible to identify the time periods when the vehicle is on and the time periods when the vehicle is off, i.e., parked.

[0037] Vehicle speed information 218 is information regarding the vehicle speed of the vehicle corresponding to vehicle ID 211. For example, the vehicle speed is acquired using various sensors installed on each vehicle, such as a vehicle speed sensor, and this vehicle speed is transmitted to the management server 200 and stored as vehicle speed information 218.

[0038] Traffic congestion information 219 is information about road congestion around the vehicle corresponding to vehicle ID 211. For example, it is possible to determine whether or not a road is congested based on the relationship between the vehicle speed stored in vehicle speed information 218 and the road including the location stored in location information 215. For example, if a vehicle traveling on a highway is moving at a low speed, it is presumed that the highway is congested. On the other hand, even if a vehicle traveling on a narrow road is moving at a low speed, it is presumed that there is no congestion. In addition, traffic congestion information may be obtained from an external device that can be connected via the network 20, such as a traffic congestion information provision server, based on the location stored in location information 215, to obtain traffic congestion information around each vehicle.

[0039] The movement trajectory 220 is information that shows the movement trajectory of the vehicle corresponding to the vehicle ID 211. For example, information that associates the location stored in the location information 215 with the time when this location information was acquired is stored as the movement trajectory in the movement trajectory 220.

[0040] Image information 221 is information about images acquired by an image acquisition device, such as a camera, installed on the vehicle corresponding to vehicle ID 211. For example, images acquired by an external camera that photographs the outside of the vehicle, an internal camera that photographs the inside of the vehicle, or a camera that photographs a wide area including both the inside and outside of the vehicle are transmitted to the management server 200 and stored as image information 221. Based on the image information 221, it is possible to identify the conditions inside each vehicle, the conditions outside each vehicle, etc.

[0041] SNS usage information 222 is information indicating that the SNS corresponding to SNSID 213 was used by a user who owns the vehicle corresponding to vehicle ID 211. For example, suppose user U1 is riding in vehicle C1 and user U1 uses electronic device MC1 to send SNS posting information to SNS server 300. In this case, information indicating that the SNS posting information was sent, the time of transmission, location information indicating the location where the SNS posting information was sent, and the SNSID are associated and sent to the management server 200 and stored in SNS usage information 222.

[0042] [Example of user information database contents] Figure 4 is a schematic diagram illustrating the various pieces of information stored in the User Information DB230. The User Information DB230 is a database for managing user information related to users U1 through UM who use the congestion information provision service.

[0043] The user information DB230 stores SNSID232, location information233, movement trajectory234, attribute information235, and SNS usage information236, all associated with user ID231. Some of this information is sequentially transmitted from the electronic devices MC1 to MCM to the management server 200 and stored there. User ID231 corresponds to user ID212 shown in Figure 3. SNSID232 corresponds to SNSID213 shown in Figure 3. SNS usage information236 corresponds to SNS usage information222 shown in Figure 3.

[0044] Location information 233 is information indicating the location of the user corresponding to user ID 231. For example, the current location of each electronic device is acquired using various sensors built into each electronic device, such as a location information acquisition unit, and this information, which associates the latitude and longitude of the current location with the time of acquisition, is transmitted to the management server 200 and stored in location information 233. Based on location information 233, the current location of each user can be identified.

[0045] The movement trajectory 234 is information that shows the movement trajectory of the user corresponding to user ID 231. For example, information that associates the location stored in location information 233 with the time when this location information was acquired is stored as the movement trajectory in the movement trajectory 234.

[0046] Attribute information 235 contains information about the user attributes corresponding to user ID 231. For example, the user's age and gender are stored in attribute information 235. This attribute information is registered when the user begins using the congestion information service. Note that the age information and gender information are examples of each user's profile information.

[0047] [Example of contents in the congestion information database] Figure 5 is a schematic diagram illustrating the various pieces of information stored in the congestion information DB 240. The congestion information DB 240 is a database for managing each piece of congestion information generated by the congestion information processing unit 304.

[0048] The congestion information DB240 stores location information 242, capacity 243, number of available parking spaces 244, specific location extraction information 245, human congestion level 246, vehicle congestion level 247, congestion reason information 248, and supplementary information 249, all associated with the registered location ID 241. Some of this information is generated and stored by the congestion information processing unit 304.

[0049] The registered location ID 241 is identification information used to identify locations subject to congestion management. Locations subject to the congestion information service refer to places where people or vehicles can gather. Examples include well-known landmarks, parks, amusement parks and their facilities, conference halls, gymnasiums and other facilities.

[0050] Location information 242 is information indicating the location of the place corresponding to registered location ID 241. If a parking lot exists at the location corresponding to registered location ID 241, information indicating the location of that parking lot is also stored in location information 242. The same applies to the following. For example, the latitude and longitude of the registered location are stored in location information 242. Based on location information 242, the location of each registered location can be identified. If the location corresponding to registered location ID 241 is a relatively large area, such as an amusement park or a large facility, information indicating the area corresponding to those locations may be stored as location information.

[0051] The capacity value 243 indicates the number of people that can be accommodated at the location corresponding to registered location ID 241. The degree of crowding can be calculated based on the capacity value 243. Note that some facilities and venues do not have a fixed capacity. In such cases, a fixed value or a variable value based on the average daily capacity over the past years may be used as the capacity value.

[0052] The number of available parking spaces, 244, indicates the number of vehicles that can be parked at the location corresponding to registered location ID 241. The degree of vehicle congestion can be calculated based on this number of available parking spaces. Note that some parking lots do not have a fixed number of available spaces. In such cases, a fixed or variable value based on the average daily values ​​from the past may be used as the number of available spaces.

[0053] The specific location extraction information 245 indicates whether or not a location has been extracted as a specific location by the congestion information processing unit 304. The method for extracting specific locations will be explained in detail with reference to Figure 7, etc.

[0054] The crowd density 246 indicates the level of crowding at the location corresponding to registered location ID 241. The crowd density can be calculated based on the capacity 243. The method for calculating the crowd density will be explained in detail with reference to Figure 7, etc.

[0055] The vehicle congestion level 247 is information indicating the level of vehicle congestion at the location corresponding to registration location ID 241. The method for calculating the vehicle congestion level will be explained in detail with reference to Figure 7, etc. In this embodiment, when the term "vehicle congestion level" is used, it refers to the congestion level of the parking lot.

[0056] The congestion reason information 248 is information generated by the congestion information processing unit 304 that indicates the reason for the congestion at a specific location. The method for estimating the reason for congestion will be explained in detail with reference to Figure 8, etc.

[0057] The supplementary information 249 is supplementary information related to a specific location, generated by the congestion information processing unit 304. The supplementary information will be explained in detail with reference to Figure 10, etc.

[0058] [Example of contents in the user submission database] Figure 6 is a schematic diagram illustrating the information stored in the submission information DB310. The submission information DB310 is a database for managing each submission information transmitted using electronic devices such as MC1 to MCM.

[0059] The posting information DB310 stores the reception time 312, location information 313, and posting information 314, associated with SNSID 311. Note that SNSID 311 corresponds to SNSID 213 shown in Figure 3 and SNSID 232 shown in Figure 4.

[0060] The reception time 312 is information indicating the time associated with the post information stored in the post information 314, or the time when the SNS server 300 received the post information. The location information 313 is information indicating the location information associated with the post information stored in the post information 314.

[0061] Posted information 314 is posted by users U1 to UM using electronic devices MC1 to MCM. This posted information includes various types of information, such as the post text and image information. The posted information will be explained in detail with reference to Figure 8.

[0062] [Example of extracting specific locations] Here, we will explain the process of extracting specific locations by the congestion information processing unit 304.

[0063] The congestion information processing unit 304 extracts specific locations from among the locations corresponding to registered location ID 241 (see Figure 5) in the congestion information DB 240 where it is assumed that at least one user is present. Specifically, the congestion information processing unit 304 obtains location information from the location information 215 in the vehicle information DB 210 and the location information 233 in the user information DB 230. Next, the congestion information processing unit 304 extracts specific locations from among the locations corresponding to registered location ID 241 in the congestion information DB 240 based on the location information obtained from the vehicle information DB 210 and the user information DB 230, and the location information 242 in the congestion information DB 240. That is, the congestion information processing unit 304 extracts specific locations where it is estimated that at least one of the vehicles C1 to CM and users U1 to UM is present, for the locations corresponding to the location information 242 in the congestion information DB 240.

[0064] Specifically, the congestion information processing unit 304 determines whether there is any location information among the location information obtained from the vehicle information DB 210 and user information DB 230 that matches the location information 242 in the congestion information DB 240. Next, if there is any location information among the location information obtained from the vehicle information DB 210 and user information DB 230 that matches the location information 242 in the congestion information DB 240, the congestion information processing unit 304 extracts the registered location ID 241 corresponding to the matching location information 242 as the ID of the specific location. Next, the congestion information processing unit 304 stores "Yes" in the specific location extraction information 245 corresponding to the extracted specific location ID. On the other hand, "No" is stored in the specific location extraction information 245 corresponding to location information 242 that does not match the location information obtained from the vehicle information DB 210 and user information DB 230.

[0065] It is also assumed that the vehicle information DB210 and user information DB230 may contain past location information. Therefore, from the location information stored in the vehicle information DB210 and user information DB230, only location information associated with the current time or a time close to it may be extracted and used. This can improve the accuracy of the extraction process for specific locations.

[0066] In this example, we have shown how to extract a specific location from a registered location where at least one user exists. However, a specific location may also be extracted from a registered location where at least one or more posts exist. In this case, the congestion information processing unit 304 obtains SNS usage information from the SNS usage information 222 in the vehicle information DB 210 and the SNS usage information 236 in the user information DB 230. Next, the congestion information processing unit 304 extracts a specific location from the locations corresponding to the registered location ID 241 in the congestion information DB 240, based on the location information contained in the SNS usage information obtained from the vehicle information DB 210 and the user information DB 230, and the location information 242 in the congestion information DB 240.

[0067] [Example of calculating congestion levels for people and vehicles] Next, we will explain the process by which the congestion information processing unit 304 calculates the degree of congestion for people and vehicles.

[0068] Figure 7 shows an example of the relationship between the congestion levels of facility 450 and parking lot 460 estimated by the management server 200. In Figure 7, facility 450, which can accommodate a large number of people, and parking lot 460, which is located near facility 450 for people visiting facility 450, are shown as simplified rectangles.

[0069] The congestion information processing unit 304 calculates the number of users present at the specific location extracted by the specific location extraction process described above. Specifically, the congestion information processing unit 304 extracts the number of location information entries from the vehicle information DB 210 and user information DB 230 that are the same as the specific location extracted by the specific location extraction process described above, and uses that number as the number of users present at the specific location. Then, the congestion information processing unit 304 calculates the degree of human congestion based on the relationship between the number of users and the congestion threshold. An example of this calculation of the degree of human congestion will be explained in detail with reference to Figure 7.

[0070] Furthermore, it is assumed that the same location information may be stored in both the vehicle information DB210 and the user information DB230 for users who use a vehicle. Therefore, for users whose location information is stored in both the vehicle information DB210 and the user information DB230, it is preferable to calculate the number of users present at a specific location using information from either the vehicle information DB210 or the user information DB230.

[0071] Furthermore, it is assumed that a vehicle may have multiple occupants. In this case, it is possible to obtain the number of occupants in the vehicle based on the occupant information 216 (see Figure 3) in the vehicle information DB210, and add this number of occupants to the number of users present at a specific location.

[0072] Furthermore, the number of users in a group at a specific location may be estimated using a known statistical method, such as population estimation. Specifically, the congestion information processing unit 304 can estimate the number of users in a group at a specific location based on population estimation using the number of users estimated based on location information obtained from the vehicle information DB 210 and user information DB 230. In this way, at least one of the following can be executed: a first estimation process that estimates the number of users at a specific location based on location information obtained from the vehicle information DB 210 and user information DB 230, or a second estimation process that estimates the number of users in a group at a specific location based on population estimation using the number of users estimated based on that location information. In this case, at least one of the number of users estimated by the first estimation process and the number of people in the group estimated by the second estimation process can be included in the congestion information and output.

[0073] [Example of calculating vehicle congestion level] Next, we will explain the process by which the congestion information processing unit 304 calculates the degree of congestion in vehicles.

[0074] The congestion information processing unit 304 calculates the number of vehicles present in the parking lot at or near the specific location extracted by the specific location extraction process described above. Specifically, the congestion information processing unit 304 extracts the number of location information stored in the vehicle information DB 210 that matches the location information of the specific location extracted by the specific location extraction process described above, and takes this number as the number of vehicles present in the parking lot at or near the specific location. Then, the congestion information processing unit 304 calculates the vehicle congestion level based on the relationship between the number of vehicles and the congestion level threshold. An example of this vehicle congestion level calculation will be explained in detail with reference to Figure 7. In addition, similar to the congestion level of people, the number of groups composed of vehicles present in the parking lot at or near the specific location may be estimated using known statistical methods, such as population estimation.

[0075] Let's assume that facility 450, shown in Figure 7, has a capacity of 40 people. In this case, "40 people" will be stored in the capacity 243 of the congestion information DB 240 (see Figure 5). Congestion thresholds TH1 and TH2 are also set. Congestion thresholds TH1 and TH2 are thresholds set according to the registered location such as a facility or venue, and are used to determine the level of congestion. In the example shown in Figure 7, we show an example where congestion threshold TH1 is set to 8 and congestion threshold TH2 is set to 32. In this case, if the capacity of facility 450 is less than or equal to congestion threshold TH1, that is, if the capacity of facility 450 is 8 people or less, the congestion level is determined to be low. Also, if the capacity of facility 450 is more than congestion threshold TH1 but less than or equal to congestion threshold TH2, that is, if the capacity of facility 450 is more than 8 people but 32 people or less, the congestion level is determined to be medium. Furthermore, if the capacity of facility 450 is greater than the congestion threshold TH2, that is, if the capacity of facility 450 is greater than 32 people, the congestion level is determined to be high. However, if the capacity of facility 450 is at or exceeds the maximum capacity (40 people), the congestion level may be determined to be full.

[0076] The parking lot 460 is assumed to have a capacity of 24 vehicles. In the example shown in Figure 8, each of the three rows of parking spaces 461 to 463 is assumed to have a capacity of 8 vehicles. In this case, "24 vehicles" will be stored in the number of available parking spaces 244 in the congestion information DB 240 (see Figure 5). In addition, congestion thresholds TH11 and TH12 are set. Congestion thresholds TH11 and TH12 are thresholds set according to the parking lot of the registered location and are used when determining the level of congestion. In the example shown in Figure 7, a congestion threshold TH11 is set to 6 and congestion threshold TH2 is set to 21. In this case, if the number of vehicles parked in parking lot 460 is less than or equal to the congestion threshold TH11, that is, if the number of vehicles parked in parking lot 460 is 6 or less, the congestion level is determined to be low. Furthermore, if the number of parking spaces in parking lot 460 is greater than or equal to the congestion threshold TH11 but less than or equal to the congestion threshold TH12, that is, if the number of parking spaces in parking lot 460 is greater than 6 but 21 or less, the congestion level is determined to be medium. Also, if the number of parking spaces in parking lot 460 is greater than the congestion threshold TH12, that is, if the number of parking spaces in parking lot 460 is greater than 21, the congestion level is determined to be high. However, if the number of parking spaces in parking lot 460 is equal to or greater than the maximum number of available spaces (24), the congestion level may be determined to be full.

[0077] Figure 7(A) shows an example where there are 6 visitors to facility 450 and 2 vehicles parked in parking lot 460. In this case, since the capacity of facility 450 is less than or equal to the congestion threshold TH1, the congestion level of facility 450 is determined to be "low". Also, since the number of vehicles parked in parking lot 460 is less than or equal to the congestion threshold TH11, the congestion level of parking lot 460 is also determined to be "low".

[0078] Figure 7(B) shows an example where there are 40 visitors to facility 450 and 4 vehicles parked in parking lot 460. In this case, since the capacity of facility 450 is greater than the congestion threshold TH2, the congestion level of facility 450 is determined to be "high". In this case, since the capacity of facility 450 is the maximum capacity (40 people), the congestion level may also be determined to be full. On the other hand, since the number of vehicles parked in parking lot 460 is less than or equal to the congestion threshold TH11, the congestion level of parking lot 460 is determined to be "low".

[0079] Figure 7(C) shows an example where there are 12 visitors to facility 450 and 24 vehicles parked in parking lot 460. In this case, the capacity of facility 450 is greater than the congestion threshold TH1 but less than or equal to the congestion threshold TH2, so the congestion level of facility 450 is determined to be "medium". On the other hand, the number of vehicles parked in parking lot 460 is greater than the congestion threshold TH12, so the congestion level of parking lot 460 is determined to be "high". In this case, since the congestion level of parking lot 460 is equal to the number of available parking spaces (24), the congestion level may be determined to be full.

[0080] Figure 7(D) shows an example where there are 40 visitors to facility 450 and 24 vehicles parked in parking lot 460. In this case, since the capacity of facility 450 is greater than the congestion threshold TH2, the congestion level of facility 450 is determined to be "high". In this case, since the capacity of facility 450 is equal to the maximum capacity (40 people), the congestion level may also be determined to be "full". Also, since the number of parked vehicles in parking lot 460 is greater than the congestion threshold TH12, the congestion level of parking lot 460 is determined to be "high". In this case, since the congestion level of parking lot 460 is equal to the maximum capacity (24 vehicles), the congestion level may also be determined to be "full".

[0081] In Figure 7, an example is shown where fixed values ​​are used as congestion thresholds TH1, TH2, TH11, and TH12. However, variable values ​​may also be set as congestion thresholds. For example, variable values ​​based on the average value of the past day can be set as congestion thresholds. This makes it possible to calculate congestion based on the average value of the past day, for example.

[0082] Furthermore, while Figure 7 illustrates facilities 450 with a fixed capacity and parking lots 460 with a fixed number of parking spaces as examples, there are also facilities and venues where the capacity is not fixed, and parking lots where the number of parking spaces is not fixed. In cases where the capacity or number of parking spaces is not fixed, a fixed or variable value based on the average daily values ​​from the past may be set as the congestion threshold.

[0083] [Examples of estimated reasons for congestion] Next, we will explain the congestion information processing unit 304's process for estimating the reason for the congestion level.

[0084] Figure 8 is a simplified diagram showing an example of a user-submitted text and image using electronic devices MC1 to MCM, etc. Figure 8 shows an example of user-submitted information located in a DEF facility corresponding to the congestion information display area 520 shown in Figure 9. The congestion information processing unit 304 can determine user-submitted information based on the SNS usage information 222 in the vehicle information DB 210 and the SNS usage information 236 in the user information DB 230. The congestion information processing unit 304 can also obtain user-submitted information from the SNS server 300 based on the SNSID 213 in the vehicle information DB 210 and the SNSID 232 in the user information DB 230.

[0085] Figure 8(A) shows display screens 401 to 403 that are displayed on the display units of electronic devices such as MC1 to MCM. Display screens 401 to 403 are created by user operation using the electronic devices such as MC1 to MCM. The post text display areas 405 to 407 display the post text created by the user operation. The image display areas 411 to 413 display images acquired using cameras built into the electronic devices such as MC1 to MCM. All information displayed on display screens 401 to 403, including SNS ID, transmission time, and location information, is transmitted to the SNS server 300.

[0086] When the control unit 302 of the SNS server 300 receives posting information from each electronic device, it associates the received posting information with the same SNSID 311 as the SNSID included in the received posting information and stores the received posting information in the posting information DB 310 as posting information 314. In this case, if the received posting information includes at least one of location information and time information, it stores the received time 312 and location information 313, respectively.

[0087] Figure 8(B) shows the posts 421 to 423 and images 425 to 427 extracted from the posted information contained in the display screens 401 to 403 shown in Figure 8(A). As shown in Figure 8(A), when multiple posts are transmitted from the same specific location, frequently occurring words are extracted from the posts 421 to 423 contained in the multiple posts. These frequently occurring words refer to the same words or phrases that appear in many of the posts among the posts contained in the multiple posts. For the extraction of frequently occurring words, known frequently occurring word extraction techniques can be employed.

[0088] For example, posts 421 through 423 contain the terms "stage show," "ABC Man," and "DEF facility." In this case, "stage show," "ABC Man," and "DEF facility" are extracted as frequently occurring words from posts 421 through 423.

[0089] Furthermore, as shown in Figure 8(A), if multiple posts are transmitted from the same specific location, similar images are extracted from the posted images 425 to 427 included in the multiple posts. These similar images refer to multiple images with a high degree of similarity among the posted images included in the multiple posts. For the extraction of images with high similarity, known similar image extraction techniques can be employed.

[0090] For example, posted images 425 to 427 contain "ABC Man" in various poses, enclosed in dotted rectangles 431 to 433. In this case, "ABC Man" enclosed in dotted rectangles 431 to 433 is extracted from posted images 425 to 427 as an image with a high similarity.

[0091] Figure 8(C) shows the frequently occurring words 430 "stage show," "ABC man," and "DEF facility" extracted from the posts 421 to 423 shown in Figure 8(B), and a group of similar images 440 extracted from the posted images 425 to 427 shown in Figure 8(B).

[0092] In this way, the frequently occurring words 430 "stage show," "ABC Man," and "DEF facility," extracted from user-submitted information (submitted information included in display screens 401 to 403) present at the DEF facility, along with a group of similar images 440, are provided to the user as the reason for the congestion level of the DEF facility. For example, this is displayed in the congestion information display area 520 in Figure 9.

[0093] In this example, we have shown an example where frequently occurring words and similar images extracted from posted information are used as reasons for congestion, but other information may also be used as reasons for congestion. For example, if an internet search is performed using frequently occurring words extracted from posted information as search keywords, and information matching the current time and registered location is found in the internet search results, that information matching the current time and registered location may be used as a reason for congestion. Alternatively, for example, if an internet search is performed using frequently occurring words extracted from posted information, the current time, and the registered location as search keywords, and information that is presumed to have a high degree of similarity to the extracted frequently occurring words is found in the internet search results, that information may be used as a reason for congestion. For example, if an internet search is performed using frequently occurring words extracted from posted information, such as "stage show," "ABC man," and "DEF facility," and the current time and registered location "DEF facility," as search keywords, and information that is presumed to have a high degree of similarity to the extracted frequently occurring words is found in the internet search results, that information can be estimated as a reason for congestion. In this example, we demonstrated searching the internet for frequently occurring words, but you could also use similar images to perform an internet search and use the search results to estimate the reasons for the level of congestion.

[0094] In this example, we have shown an example of estimating the reasons for the degree of congestion of people at a specific location, but it is also possible to estimate the reasons for the degree of congestion of vehicles at a specific location. For example, if information about vehicles or parking lots is included in the posted information, it is possible to estimate the reasons for the degree of congestion of vehicles based on that information. Information about vehicles or parking lots is, for example, information such as posted text that includes terms such as parking lot and traffic congestion. For example, it is assumed that posted information obtained from the SNS server 300 based on SNSID213 in the vehicle information DB210 often includes information about vehicles. In this way, when the reasons for the degree of congestion of both people and vehicles at a specific location are estimated, the reasons for the congestion of both people and vehicles are included in the congestion information and provided to the user.

[0095] [Example of generating supplementary information] Next, the process of generating supplementary information by the congestion information processing unit 304 will be described.

[0096] The congestion information processing unit 304 generates supplementary information to be output in the congestion information based on the user information of each user present at the specific location extracted by the specific location extraction process described above. Specifically, the congestion information processing unit 304 obtains the age and gender of users present at the specific location based on the profile information, i.e., attribute information 235 (see Figure 4), included in the user information of users present at the specific location, which was used in the specific location extraction process described above. The congestion information processing unit 304 then aggregates the age and gender of each user present at the specific location and generates supplementary information regarding the group composed of users present at the specific location, i.e., supplementary information regarding the age composition and gender composition. An example of the display of this supplementary information is shown in Figure 10.

[0097] Furthermore, it is assumed that a vehicle may have multiple occupants. In this case, based on the occupant information 216 (see Figure 3) in the vehicle information DB210, profile information about the vehicle's occupants may be obtained, and this occupant profile information may be used to aggregate the age and gender of each user present at a specific location.

[0098] Furthermore, the congestion information processing unit 304 calculates the average stay time for each user present at a specific location as supplementary information, based on the user information of each user present at the specific location extracted by the specific location extraction process described above. Specifically, the congestion information processing unit 304 calculates the stay time for each user at the specific location based on the location information 233 (see Figure 4) included in the user information of users present at the specific location, which was used in the specific location extraction process described above. Then, the congestion information processing unit 304 calculates the average stay time for each user present at the specific location based on the calculated stay time for each user. Specifically, the average stay time for each user is calculated by adding up the stay times of each user and dividing this added value by the number of users. An example of this average stay time display is shown in Figure 10. Alternatively, the average stay time for each vehicle may be calculated using the location information 215 or vehicle on / off information 217 from the vehicle information DB 210, and this average stay time may be estimated as the average stay time for each user.

[0099] [Examples of generating other supplementary information, examples of estimating other reasons for congestion levels] Other supplementary information may be generated using user information or vehicle information. Furthermore, other reasons for congestion may be estimated using user information or vehicle information. Here, we show examples of generating supplementary information and estimating reasons for congestion using vehicle information DB210 (see Figure 3), including vehicle type 214, vehicle speed information 218, traffic congestion information 219, movement trajectory 220, and image information 221.

[0100] For example, based on vehicle type 214, it is possible to generate supplementary information regarding the size of vehicles that can be parked in a specific parking lot. That is, it is possible to extract the largest vehicle among those parked in the specific parking lot and use that size as the size of vehicles that can be parked. Alternatively, the size of the vehicles that can be parked may be used as a reason for the level of congestion.

[0101] For example, based on image information 221, images acquired by vehicles parked in a parking lot at a specific location, or by vehicles passing near it, can be generated as supplementary information. By outputting such images as supplementary information, the situation at a specific location and its surroundings can be easily understood. Images of the situation at a specific location and its surroundings may also be used as reasons for the level of congestion.

[0102] For example, based on vehicle speed information 218 and traffic congestion information 219, road conditions around a specific location can be generated as supplementary information. By outputting such road conditions as supplementary information, it becomes easy to understand that the roads around a specific location are congested. This allows users to consider alternative ways of getting to that specific location, such as looking for parking at another location, for example, a parking lot at the next station. Alternatively, the road conditions around a specific location may be used as the reason for the degree of congestion.

[0103] For example, based on the movement trajectory 220, road conditions around a specific location can be generated as supplementary information. For instance, it is conceivable that a parking lot at a specific location can only be accessed from one direction. In this case, the movement history of each vehicle that went to the parking lot at the specific location can be displayed on a map and used as the route to that parking lot. This movement history can also be used as the reason for the level of congestion.

[0104] [Example of congestion information output] Figures 9 and 10 show examples of output when congestion information is output from the information output device 150. Figures 9 and 10 show examples of congestion information being displayed on the display unit 151 of the information output device 150. Alternatively, congestion information may be displayed on the display unit MC1 of the electronic device owned by user U1. Furthermore, congestion information may be made available through a dedicated map application.

[0105] Figure 9 shows an example of displaying congestion information on the display unit 151 of the information output device 150 on a map 500 within a predetermined range based on the current location of vehicle C1. The predetermined range is, for example, a range set by the user based on their operation. Furthermore, the range of the map displayed on the display unit 151 can be enlarged or reduced based on user operation.

[0106] Furthermore, a vehicle marker 501 indicating the position of vehicle C1 is displayed at a predetermined location on the map 500, for example, at the center. In addition, the range of the map displayed on the display unit 151 of the information output device 150 moves in accordance with the movement of vehicle C1.

[0107] Furthermore, Figure 9 shows an example of displaying congestion information display areas 510, 520, and 530 in a callout format on map 500, near the location of the corresponding registered location.

[0108] The congestion information display areas 510, 520, and 530 display, for example, registered locations extracted as specific locations, reasons for congestion, and congestion levels. As registered locations, for example, the name or abbreviation of the registered location can be displayed. As reasons for congestion, for example, frequently occurring words and similar images 511, 521, and 531 obtained in the congestion level estimation process described above can be displayed. As congestion levels, the congestion levels of people and vehicles obtained in the congestion level calculation process described above can be displayed in multiple levels, such as low, medium, and high, and can also be displayed using images 512, 522, and 532 that visualize the congestion level. Images 512, 522, and 532 are designed to make it easy to visually grasp the congestion level, and can be, for example, images using human figures to represent congestion levels, or images using colors to represent congestion levels. For example, more human figures can be displayed as the congestion level increases. Also, when the congestion level is high, red can be displayed, and as the congestion level decreases, the display can transition from red to yellow.

[0109] Furthermore, if any of the congestion information display areas 510, 520, or 530 is pressed, detailed information regarding the specific location corresponding to the pressed congestion information display area may be displayed. In this case, for example, supplementary information 544 shown in Figure 10 may be newly displayed.

[0110] The congestion information display area 510 is an area that displays the level of congestion at the GHI headquarters facilities of the automobile company. Figure 9 shows an example of the display when a new car launch event is being held and the new car is being unveiled.

[0111] The congestion information display area 520 is an area that displays the level of congestion for DEF facilities where various events are held. Figure 9 shows an example of the display when an ABC Man stage show is being held and a show in which ABC Man fights is taking place.

[0112] The congestion information display area 530 is the area that displays the level of congestion at H Hospital. Figure 9 shows an example of the display when free health water is being distributed at H Hospital.

[0113] In this way, the degree of congestion of registered locations around the vehicle C1's current location and the reasons for that congestion (e.g., frequently used words, posted images) can be displayed on map 500, allowing user U1 to easily find out which registered locations are congested, which are not, and the reasons for these congestion. For example, consider a scenario where different events are often held at the DEF facility on different days. In this case, there will be days when the location is congested and days when it is not, but even in such cases, it will be easy to find out the reasons for the congestion and the lack thereof.

[0114] In Figure 9, an example is shown where a map 500 within a predetermined range based on the current location of vehicle C1 is displayed. However, the system may also display a map of a range specified by the user and show congestion information for that range. For example, if a user wants to know the level of congestion around their destination, they can display a map of the area around that destination and show congestion information for that area. In this way, in addition to congestion information around vehicle C1, it is possible for the user to specify any point and display congestion information, making it easy to obtain congestion information for the area the user wants to know about. Furthermore, since the user can specify the range for which congestion information is to be obtained, it is easy to obtain congestion information for the range the user wants to know about.

[0115] Figure 10 shows an example of a display showing congestion information for registered locations within a predetermined range based on the current location of vehicle C1. Figure 10 shows an example of displaying the information corresponding to the congestion information display areas 510, 520, and 530 shown in Figure 9 in tabular and list formats. The predetermined range is, for example, the same as in the example shown in Figure 9.

[0116] The specific location 541, reason for congestion 542, and congestion level 543 are the same as in the example shown in Figure 9. For the supplementary information 544, the information generated by the supplementary information generation process described above is displayed. In this way, by displaying supplementary information such as the attributes of users present at a specific location, it becomes easy to understand where there are gatherings of people of similar age and gender. It also provides an opportunity to find places that suit one's preferences. Furthermore, by displaying the average time users spend there, interest in specific locations with longer average stay times can be increased. Users can also estimate their own stay time at a specific location by referring to the average stay time.

[0117] Furthermore, the congestion information display screen shown in Figure 9 and the congestion information display screen shown in Figure 10 may be switchable based on user operation, such as pressing a toggle button. Alternatively, only the congestion information related to the selected registered location on the congestion information display screen shown in Figure 9 may be displayed for each item shown in Figure 10.

[0118] In this way, the congestion level of registered locations around the vehicle C1's current location and the reasons for that congestion (e.g., frequently used words, posted images) can be displayed in a table or list format, allowing user U1 to easily find out more detailed congestion information about congested and uncongested registered locations, and the reasons for them.

[0119] Furthermore, when displaying the congestion information screen shown in Figure 9 and the congestion information screen shown in Figure 10, it is anticipated that the number of specific locations to be displayed may increase. In this case, congestion information for a large number of specific locations will be displayed, which may make the display difficult for the user to read. Therefore, in such cases, the specific locations to be displayed may be limited based on pre-set priorities, and only congestion information relevant to the user may be displayed. For example, the priority of congestion information considered suitable for the user can be set using the user's profile information, user usage history information, etc. These priorities may also be changeable based on user operations, etc. Specifically, based on the attribute information 235 of the user information DB 230, profile information such as the user's age and gender can be generated, and based on this profile information, specific locations with many users with the same attributes as the user can be given a higher priority, and a predetermined number of specific locations with high priority can be displayed. For example, if user U1 is a male in his 40s, specific locations with many males in their 40s can be given a higher priority. Furthermore, if user U1 is a male in his 40s, the priority of specific locations with fewer men in their 40s may be increased, or the priority of specific locations may be set based on other conditions. This makes it possible to display only specific locations that are considered suitable for the user.

[0120] In addition, while Figures 9 and 10 show examples of displaying registered locations with at least one user as specific locations, registered locations with no users may also be displayed along with congestion information. Similarly, while Figures 9 and 10 show examples of displaying registered locations with at least one post as specific locations, registered locations with no posts may also be displayed along with congestion information.

[0121] In Figures 9 and 10, an example is shown where congestion information is displayed on the display unit 151 of the information output device 150. However, congestion information may be output by other methods. For example, congestion information may be output by voice from the audio output unit of the information output device 150, such as a speaker. Alternatively, congestion information may be transmitted from the information output device 150 to another device, which may then output the congestion information.

[0122] [Example of control device operation] Figure 11 is a flowchart illustrating an example of information provision processing in the control device 100. This information provision processing is executed by the control unit (not shown) of the control device 100 based on a program stored in the memory unit (not shown) of the control device 100. This information provision processing is performed continuously at each control cycle. This information provision processing will be explained with reference to Figures 1 to 10 as appropriate. Although Figure 11 shows an example of information provision processing in the control device 100, similar information provision processing can be performed in other control devices and electronic devices MC1 to MCM.

[0123] In step S601, the control unit of the control device 100 determines whether or not the setting for providing information has been made. Users of the congestion information provision service in this embodiment shall pre-set whether or not it is possible to provide information from their vehicles and electronic devices. If the setting for providing information has not been made, the information provision process is terminated. On the other hand, if the setting for providing information has been made, the process proceeds to step S602.

[0124] In step S602, the control unit of the control device 100 transmits location information acquired by the location information acquisition unit installed on vehicle C1, i.e., location information indicating the current location of vehicle C1, to the management server 200. In this case, the time of acquisition of the location information is also transmitted. In addition, various vehicle information related to vehicle C1, such as occupant information, vehicle on / off information, vehicle speed, images, etc., are also transmitted. If the information provision process is performed by the electronic device MC1, location information acquired by the location information acquisition unit built into the electronic device MC1, i.e., location information indicating the current location of the electronic device MC1, is transmitted to the management server 200. In this case, the time of acquisition of the location information and user information related to user U1 are also transmitted.

[0125] In step S603, the control unit of the control device 100 determines whether or not it is an in-vehicle device. If it is an in-vehicle device, the process proceeds to step S604. On the other hand, if it is not an in-vehicle device, the process proceeds to step S607. In this example, the control device 100 is an in-vehicle device, so the process proceeds to step S604. Also, if it is an information provision process in the electronic device MC1, the process proceeds to step S607. Note that there is no change in whether or not the device that performs the information provision process is an in-vehicle device, so the process in step S603 may be omitted.

[0126] In step S604, the control unit of the control device 100 determines whether or not a setting has been made to link vehicle C1 with SNS. Users of the congestion information service in this embodiment shall pre-set whether or not their owned vehicle C1 can be linked with SNS. If a setting has been made to link vehicle C1 with SNS, the process proceeds to step S605. On the other hand, if a setting has not been made to link vehicle C1 with SNS, the process proceeds to step S610.

[0127] In step S605, the control unit of the control device 100 acquires usage information regarding the use of the SNS or check-in function. This usage information indicates that the SNS or check-in function was used using the control device 100. This usage information may include both cases where the SNS or check-in function was used using the control device 100 and cases where the SNS or check-in function was used using equipment other than the control device 100 inside the vehicle C1. The check-in function refers to a function that allows users to share the places they have visited with other users using the location information of each device.

[0128] In step S606, the control unit of the control device 100 associates the usage information of the SNS or check-in function acquired in step S605 with the vehicle information of vehicle C1, such as the vehicle ID, and transmits it to the management server 200. That is, when posting information is sent via SNS, etc., or when the check-in function is used, the management server 200 receives a message indicating that the posting information or check-in function information has been used.

[0129] The processes in steps S607 to S609 are executed in the electronic devices MC1 to MCM and correspond to the processes in steps S604 to S606. However, in step S609, the control unit of the electronic devices MC1 to MCM associates the SNS or check-in function usage information acquired in step S608 with user information, such as a user ID, and sends it to the management server 200. The process in step S610 corresponds to the process in step S605.

[0130] In step S611, the control unit of the control device 100 transmits the SNS or check-in function usage information acquired in step S610 to the management server 200 without associating it with the vehicle information of vehicle C1.

[0131] In this manner, various types of information are uploaded to the management server 200 at predetermined timings by the control devices 100 to 100N and the electronic devices MC1 to MCM.

[0132] [Example of management server operation] Figure 12 is a flowchart showing an example of congestion information processing in the management server 200. This congestion information processing is executed by the congestion information processing unit 204 of the control device 100 based on a program stored in the storage unit 203 (see Figure 2) of the management server 200. This congestion information processing is executed continuously at each control cycle. This congestion information processing will be explained with reference to Figures 1 to 11 as appropriate.

[0133] In step S621, the congestion information processing unit 204 determines whether it has received information from each device, namely user information, vehicle information, and related information. If information is received from any device, the process proceeds to step S622. On the other hand, if no information is received from any device, the process proceeds to step S623.

[0134] In step S622, the congestion information processing unit 204 stores the information received from the devices in the respective databases. Specifically, when vehicle information and related information are received from an in-vehicle device, these are stored in the vehicle information database 210. Similarly, when user information and related information are received from an electronic device owned by the user, these are stored in the user information database 230. If information not associated with either vehicle information or user information is received, such as information on the use of SNS or check-in functions, this information is stored in the usage information database (not shown) of the storage unit 203.

[0135] In step S623, the congestion information processing unit 204 determines whether it is time to update the congestion information. The timing for updating can be, for example, when new information is received from any device, or when a certain interval of time has passed. If it is time to update the congestion information, the process proceeds to step S624. On the other hand, if it is not time to update the congestion information, the process proceeds to step S629.

[0136] In step S624, the congestion information processing unit 204 uses the vehicle information DB 210 and the user information DB 230 to extract a specific location from among the registered locations corresponding to the registered location ID 241 in the congestion information DB 240. This specific location extraction process is the same as the specific location extraction process described above.

[0137] In step S625, the congestion information processing unit 204 calculates the degree of congestion for people and vehicles for the specific location extracted in step S622, using the vehicle information DB 210 and the user information DB 230. This congestion calculation process is the same as the congestion calculation process described above. For example, as shown in Figure 7, the degree of congestion for people and vehicles is calculated.

[0138] In step S626, the congestion information processing unit 204 generates congestion reason information estimated to be the reason for the congestion level calculated in step S625. This congestion reason information generation process is the same as the congestion reason information generation process described above. For example, as shown in Figure 8, frequently occurring words 430 are extracted using the posted texts 421 to 423, and these frequently occurring words 430 are estimated to be the reason for the congestion level. Also, as shown in Figure 8, a group of similar images 440 is extracted using the posted images 425 to 427, and each image in the group of similar images 440 is estimated to be the reason for the congestion level.

[0139] In step S627, the congestion information processing unit 204 generates supplementary information to be output along with congestion information for the specific location extracted in step S624. This supplementary information generation process is the same as the supplementary information generation process described above. For example, the supplementary information 544 shown in Figure 10 is generated.

[0140] In step S628, the congestion information processing unit 204 stores the information obtained in steps S624 to S627 in the congestion information DB 240. Specifically, the congestion information processing unit 204 stores the information obtained in steps S625 to S627 in the congestion information DB 240 in association with the registered location ID 241 corresponding to the specific location extracted in step S624. By storing the information obtained in steps S624 to S627 in the congestion information DB 240 in this way, the congestion information DB 240 is updated at a predetermined timing. This makes it possible to provide the user with the latest congestion information.

[0141] In step S629, the congestion information processing unit 204 determines whether or not a congestion information request has been received. If a congestion information request has been received, the process proceeds to step S630. On the other hand, if no congestion information request has been received, the congestion information processing operation is terminated.

[0142] In step S630, the congestion information processing unit 204 extracts congestion information from the congestion information DB 240 based on the location information included in the congestion information request received in step S629. Specifically, the congestion information processing unit 204 extracts information from the congestion information DB 240 regarding the registered locations extracted as specific locations from among the registered locations within a predetermined range based on the location specified by the location information included in the congestion information request.

[0143] In step S631, the congestion information processing unit 204 transmits the congestion information extracted in step S630 to the device that sent the congestion information request received in step S629.

[0144] [Example of information output device operation] Figure 13 is a flowchart illustrating an example of congestion information output processing in the information output device 150. This congestion information output processing is executed by the control unit (not shown) of the information output device 150 based on a program stored in the storage unit (not shown) of the information output device 150. This congestion information output processing is executed continuously at each control cycle. This congestion information output processing will be explained with reference to Figures 1 to 12 as appropriate.

[0145] In step S641, the control unit of the information output device 150 determines whether or not there has been an instruction to output congestion information. This instruction to output congestion information is given, for example, by a voice command from the occupant of vehicle C1 or by an operation using the operating device of the occupant of vehicle C1. Alternatively, the instruction to output congestion information may be made automatically. For example, it may be determined that there has been an instruction to output congestion information when vehicle C1 moves to an area that the occupant of vehicle C1 frequently visits, or when vehicle C1 moves to an area that is presumed to be of interest to the occupant of vehicle C1. If there has been an instruction to output congestion information, the process proceeds to step S642. On the other hand, if there has been no instruction to output congestion information, the congestion information output process is terminated.

[0146] In step S642, the control unit of the information output device 150 sends a congestion information request to the management server 200, which includes the location information of the information output device 150 (location information of vehicle C1). If the user has specified a range for displaying congestion information, a congestion information request including location information that allows the user to identify that range is sent to the management server 200.

[0147] In step S643, the control unit of the information output device 150 acquires congestion information received from the management server 200 in response to the congestion information request transmitted in step S642.

[0148] In step S644, the control unit of the information output device 150 outputs the congestion information acquired in step S643. For example, the congestion information can be displayed as shown in Figures 9 and 10.

[0149] In step S645, the control unit of the information output device 150 determines whether or not an instruction to stop outputting congestion information has been received. This instruction to stop outputting congestion information is given, for example, by a voice command from an occupant of vehicle C1 or by an operation using an operating device by an occupant of vehicle C1. If an instruction to stop outputting congestion information has been received, the output of congestion information is stopped and the congestion information output processing operation is terminated. On the other hand, if there is no instruction to stop outputting congestion information, the process returns to step S642.

[0150] Thus, in this embodiment, it is possible to provide users not only with map guidance but also with information on the degree of congestion and the reasons for it. This allows users to easily find out the reasons for congestion and easily select a destination based on those reasons. In other words, users can automatically obtain and learn about the reasons for congestion around a given point without having to search for them themselves. For example, users who want to go to a crowded place and users who want to go to a less crowded place can easily find out the reasons for congestion and easily select a destination. Furthermore, since it is possible to display the current situation, users can quickly find out the current congestion information for a specific location. In addition, since the congestion information is updated using user information about users receiving the congestion information service, the provider of the congestion information service also becomes a recipient of information, allowing them to improve their service.

[0151] In this embodiment, an example is shown in which the management server 200 performs the congestion information provision processing. However, all or part of the congestion information provision processing may be performed by other devices. For example, all or part of the congestion information provision processing may be performed by any of the SNS server 300, control device 100, information output device 150, or electronic device MC1. In this case, the information processing system is composed of each device that performs part of the congestion information provision processing. Furthermore, in this embodiment, an example is shown in which each DB (vehicle information DB 210 (see Figure 3), user information DB 230 (see Figure 4), congestion information DB 240 (see Figure 5)) is managed by the management server 200. However, all or part of each DB may be managed by one or more other devices other than the management server 200, and the management server 200 may acquire the information of each DB managed by the other devices and use it for congestion information provision processing.

[0152] Furthermore, some (or all) of the information processing system capable of executing the functions of the management server 200 may be provided by an application that can be provided via a predetermined network such as the Internet. This application may be, for example, SaaS (Software as a Service).

[0153] Furthermore, each process shown in this embodiment is executed based on a program that causes a computer to execute each processing procedure. For this reason, this embodiment can also be understood as an embodiment of a program that realizes the function of executing each of these processes, and a recording medium that stores that program. For example, an update process to add a new function to an information processing device can cause that program to be stored in the storage device of the information processing device. This makes it possible to have the updated information processing device perform each of the processes shown in this embodiment.

[0154] [Configuration and Effects of This Embodiment] The information processing method according to this embodiment is an information processing method that provides information on congestion at registered locations to multiple users U1 to UM. This information processing method includes an acquisition process (step S621) that acquires user information about at least one of the multiple users U1 to UM, including the location information of that user, and posted information posted by at least one of the multiple users U1 to UM to a communication service (e.g., SNS) built on a predetermined network (e.g., the Internet) using electronic devices MC1 to MCM; a calculation process (steps S624, S625) that extracts a specific location where at least one of the multiple users U1 to UM is located from the registered locations of the congestion information DB240 based on the location information included in the user information, and calculates the degree of congestion at that specific location; an estimation process (step S626) that estimates the reason for the degree of congestion at that specific location based on the posted information about that specific location; and an output process (step S631) that outputs congestion information including the degree of congestion at that specific location and the reason. In this output process, an example of transmitting congestion information is shown, but the congestion information may also be output by displaying it or outputting it as sound.

[0155] This configuration allows users to receive not only map directions but also information about congestion levels and the reasons for them. This makes it easy for users to understand the reasons for congestion and to easily select a destination based on those reasons.

[0156] Furthermore, in the information processing method according to this embodiment, the acquisition process (step S621) acquires vehicle information, including location information of a vehicle owned by at least one of the multiple users U1 to UM, and the calculation process (steps S624, S625) extracts a specific location based on the location information contained in the user information or vehicle information, and calculates the degree of congestion at that specific location.

[0157] This configuration allows for the extraction of specific locations based on location information contained in user information or vehicle information, thereby improving the accuracy of location extraction.

[0158] Furthermore, in the information processing method according to this embodiment, the acquisition process (step S621) acquires post information (or information that can identify it) which includes at least one of the post text and image information, the estimation process (step S626) uses at least one of the post text and image information contained in the post information relating to the specific location as the reason for the congestion at that specific location, and the output process (step S631) outputs the congestion information which includes at least one of the post text and image information that was used as the reason for the congestion.

[0159] This configuration allows for the provision of at least one of the posted text and image information as the reason for the level of congestion at a specific location, making it possible for users to see a visually easy-to-understand explanation for the level of congestion.

[0160] Furthermore, in the information processing method according to this embodiment, in the estimation process (step S626), frequently occurring words are extracted from the posted text contained in the posted information, and the reason for the degree of congestion at a specific location is estimated based on the extracted frequently occurring words. In the output process (step S631), at least one of the extracted frequently occurring words and the reason for the degree of congestion at the specific location estimated based on the frequently occurring words is included in the congestion information and output.

[0161] This configuration allows for the provision of at least one of the following as the reason for congestion at a specific location: frequently occurring words extracted from the posts and reasons for congestion at that location estimated based on those frequently occurring words. This makes it possible for users to see reasons for congestion that are easy to understand visually. In other words, it becomes possible to provide users with more appropriate reasons for congestion.

[0162] Furthermore, in the information processing method according to this embodiment, the estimation process (step S626) performs a search process using a device connected to a predetermined network based on location information and time information obtainable from the posted information and the extracted frequently occurring words, estimates the reason for the degree of congestion at a specific location based on the search results from the search process, and in the output process (step S631), outputs congestion information including at least one of the search results and the reason for the degree of congestion estimated based on those search results.

[0163] This configuration allows for the provision of at least one of the following as the reason for congestion at a specific location: the search results from a search process using network-connected devices, and the reason for congestion estimated based on those search results. This enables users to see a highly accurate reason for congestion. For example, if there is an event searched as a search keyword, the search results can be provided to the user as the reason for congestion, allowing the user to easily obtain more detailed information about that event.

[0164] Furthermore, in the information processing method according to this embodiment, in the estimation process (step S626), if there are multiple image information entries related to the posted information for that specific location, the multiple image information entries are compared, and based on the image information determined to have a high similarity, an image corresponding to the reason for the congestion at that specific location is extracted, and in the output process (step S631), the image corresponding to the reason for the congestion is included in the congestion information and output.

[0165] This configuration allows images extracted based on image information determined to have a high degree of similarity to be provided as reasons for the level of congestion at a specific location, enabling users to see the reasons for congestion in a visually easy-to-understand way. For example, users can quickly understand the reasons for congestion from images that are easy to grasp.

[0166] Furthermore, in the information processing method according to this embodiment, the acquisition process (step S621) acquires profile information as user information, which includes age information and gender information for each user; the estimation process (steps S626, S627) generates supplementary information about a group of users present in a specific location based on the profile information included in the user information; and the output process (step S631) outputs this supplementary information along with the congestion information.

[0167] This configuration allows for the provision of supplementary information generated based on profile information, along with the reasons for the level of congestion at a specific location. This enables users to view supplementary information other than the reasons for congestion. For example, users can easily understand the composition of the group at a particular location.

[0168] Furthermore, in the information processing method according to this embodiment, the estimation process (steps S626, S627) calculates the time a user spends at a specific location based on the location information contained in the user information relating to the user at that location, calculates the average time each user spends at that location based on the time each user spends there, and in the output process (step S631), outputs the average time spent as supplementary information to the congestion information.

[0169] This configuration makes it possible to provide the average time each user spends at a specific location, along with the reasons for the level of congestion at that location. This allows users to view and refer to the average time each user spends at a specific location.

[0170] Furthermore, in the information processing method according to this embodiment, the calculation process (step S625) performs a first estimation process to estimate the number of users present at a specific location based on location information included in user information relating to users present at that location, or a second estimation process to estimate the number of people in a group composed of users present at that specific location based on population estimation using the number of users estimated based on the location information. The congestion level at that specific location is calculated using the number of users estimated by the first estimation process and the number of people in the group estimated by the second estimation process. In the output process (step S631), at least one of the number of users estimated by the first estimation process and the number of people in the group estimated by the second estimation process is included in the congestion information and output.

[0171] This configuration makes it possible to provide at least one of the number of users estimated by the first estimation process and the number of people in the group estimated by the second estimation process, along with the reason for the level of congestion at that particular location, allowing users to see numerical data that is easy to understand visually.

[0172] Furthermore, in the information processing method according to this embodiment, the calculation process (step S625) calculates the degree of human congestion at a specific location based on location information included in user information or vehicle information, and calculates the degree of vehicle congestion at a specific location based on location information included in vehicle information, the estimation process (step S626) estimates the reason for the degree of vehicle congestion at a specific location based on posted information and vehicle information related to the specific location, and the output process (step S631) outputs congestion information including the degree of human and vehicle congestion at the specific location and the reasons for them.

[0173] This configuration makes it possible to provide information on the degree of congestion of people and vehicles at a specific location, as well as the reasons for these congestion levels. In this case, for example, as shown in Figure 9, by displaying the degree of congestion of people and vehicles at a specific location in a manner that allows for comparison between the two, users can easily understand the differences in the degree of congestion between people and vehicles. Furthermore, by displaying the reasons for the degree of congestion of people and vehicles at a specific location in a manner that allows for comparison between the two, users can easily understand the reasons for the congestion of people and vehicles. As a result, users can easily select a destination based on the degree of congestion of both people and vehicles and the reasons for it.

[0174] Furthermore, in the information processing method according to this embodiment, the acquisition process (step S621) acquires vehicle information including vehicle image information including images of the interior and exterior of the vehicle, vehicle type, traffic congestion information, vehicle speed, and movement trajectory, and the estimation process (steps S626, S627) executes at least one of the following: an estimation process that estimates the reason for the degree of congestion at a specific location based on at least one of the vehicle image information, vehicle type, traffic congestion information, vehicle speed, and movement trajectory, and a generation process that generates supplementary information to be included in the congestion information.

[0175] This configuration allows for estimation processing to determine the reasons for congestion using various vehicle information, and generation processing to generate supplementary information, thereby improving the accuracy of generating the reasons for congestion at a specific location.

[0176] Furthermore, the management server 200 (an example of an information processing device) is an information processing device that provides information about congestion at registered locations to multiple users U1 to UM. The management server 200 includes a congestion information processing unit 204 (an example of an acquisition unit) that acquires user information about at least one of the multiple users U1 to UM, including the location information of that user, and posting information posted by at least one of the multiple users U1 to UM to a communication service (e.g., SNS) built on a predetermined network (e.g., the Internet) using electronic devices MC1 to MCM; a congestion information processing unit 204 (an example of a calculation unit) that extracts a specific location where at least one of the multiple users is located from among the registered locations based on the location information included in the user information, and calculates the degree of congestion at that specific location; a congestion information processing unit 204 (an example of an estimation unit) that estimates the reason for the degree of congestion at that specific location based on the posting information about that specific location; and a congestion information processing unit 204 (an example of an output unit) that outputs congestion information including the degree of congestion at that specific location and the reason for it. Here, an example of transmitting congestion information is shown, but the congestion information may also be output by displaying it or outputting it as audio.

[0177] Although embodiments of the present invention have been described above, these embodiments only represent a part of the application examples of the present invention, and are not intended to limit the technical scope of the present invention to the specific configurations of the above embodiments. [Explanation of Symbols]

[0178] 10 Information processing system, 20 Network, 100 to 100N Control device, 150 to 150N Information output device, 200 Management server, 300 SNS server, 201 Communication unit, 202 Control unit, 203 Storage unit, 204 Congestion information processing unit, 301 Communication unit, 302 Control unit, 303 Storage unit, 210 Vehicle information DB, 230 User information DB, 240 Congestion information DB, MC1 to MCM Electronic equipment

Claims

1. An information processing method that provides information about congestion at a registered location to multiple users, An acquisition process that acquires user information relating to at least one of the aforementioned users, including the location information of that user, and posted information, including the posted text, that has been posted to a communication service established on a predetermined network by at least one of the aforementioned users using an electronic device. Based on the location information included in the user information, a calculation process is performed to extract a specific location where at least one of the multiple users is located from among the registered locations, and to calculate the degree of congestion at the specific location. An estimation process is performed to extract frequently occurring words from the posts contained in the posted information relating to the specified location, to perform a search process using a device connected to a predetermined network based on the location information and time information obtainable from the posted information and the extracted frequently occurring words, and to estimate the reason for the degree of congestion at the specified location based on the search results obtained from the search process. The output process includes outputting congestion information that includes the degree of congestion at the specified location, along with at least one of the search results and the reason for the degree of congestion estimated based on the search results. Information processing methods.

2. The information processing method according to claim 1, In the acquisition process described above, vehicle information relating to a vehicle, including location information of a vehicle owned by at least one of the multiple users, is acquired. In the calculation process described above, the specific location is extracted based on the location information contained in the user information or the vehicle information, and the degree of congestion at the specific location is calculated. Information processing methods.

3. The information processing method according to claim 1 or 2, In the acquisition process described above, the post information including image information is acquired, In the estimation process described above, at least one of the posted text and image information contained in the posted information relating to the specific location is used as the reason for the degree of congestion at the specific location. In the output process described above, at least one of the posted text and image information that was cited as the reason for the congestion at the specific location is included in the congestion information and output. Information processing methods.

4. The information processing method according to claim 3, In the estimation process described above, the reason for the degree of congestion at the specific location is estimated based on the extracted frequently occurring words. In the output process, at least one of the extracted frequent words and the reason for the degree of congestion at the specific location estimated based on those frequent words is included in the congestion information and output. Information processing methods.

5. The information processing method according to claim 3, In the estimation process described above, if there are multiple image pieces of information included in the posted information relating to the specific location, the multiple image pieces of information are compared, and based on the image piece determined to have a high degree of similarity, an image corresponding to the reason for the congestion at the specific location is extracted. In the output process, an image corresponding to the reason for the degree of congestion at the specific location is included in the congestion information and output. Information processing methods.

6. The information processing method according to claim 1 or 2, In the aforementioned acquisition process, profile information is acquired as user information, which includes age information regarding each user's age and gender information regarding each user's gender. In the estimation process described above, supplementary information concerning a group of users located in the specified location is generated based on the profile information contained in the user information. In the output process described above, the supplementary information is included in the congestion information and output. Information processing methods.

7. The information processing method according to claim 1 or 2, In the estimation process described above, the time spent by a user at a specific location is calculated based on the location information included in the user information relating to the user at that specific location, and the average time spent by each user at that specific location is calculated based on the time spent by that user. In the output process described above, the average stay time is output as supplementary information to the congestion information. Information processing methods.

8. The information processing method according to claim 1 or 2, In the calculation process described above, a first estimation process is performed to estimate the number of users present at the specified location based on location information included in the user information relating to the users present at the specified location, and a second estimation process is performed to estimate the number of people in the group composed of the users present at the specified location based on population estimation using the number of users estimated based on the location information. Using the number of users estimated by the first estimation process and the number of people in the group estimated by the second estimation process, the degree of congestion at the specific location is calculated. In the output process, at least one of the number of users estimated by the first estimation process and the number of people in the group estimated by the second estimation process is included in the congestion information and output. Information processing methods.

9. The information processing method according to claim 2, In the calculation process described above, the degree of human congestion at the specific location is calculated based on the location information included in the user information or the vehicle information, and the degree of vehicle congestion at the specific location is calculated based on the location information included in the vehicle information. In the estimation process described above, the reason for the degree of vehicle congestion at the specific location is estimated based on the posted information and vehicle information relating to the specific location. The output process outputs congestion information including the degree of congestion of people and vehicles at the specific location and the reasons for such congestion. Information processing methods.

10. An information processing method according to any one of claims 2 or 9, In the acquisition process described above, at least one of the following is acquired as vehicle information: vehicle image information including images of the interior and exterior of the vehicle, vehicle type, traffic congestion information, vehicle speed, and movement trajectory. The estimation process performs at least one of the following: an estimation process that estimates the reason for the degree of congestion at the specific location based on at least one of the vehicle image information, the vehicle type, the traffic congestion information, the vehicle speed, and the movement trajectory; and a generation process that generates supplementary information to be included in the congestion information. Information processing methods.

11. An information processing device that provides information about congestion at a registered location to multiple users, An acquisition unit that acquires user information relating to at least one of the aforementioned users, including the location information of that user, and post information, including the post text, that has been posted to a communication service established on a predetermined network by at least one of the aforementioned users using an electronic device. A calculation unit extracts a specific location from the registered locations where at least one of the multiple users is located, based on the location information included in the user information, and calculates the degree of congestion at the specific location. An estimation unit extracts frequently occurring words from posts containing the posted information relating to the specified location, performs a search process using a device connected to a predetermined network based on the location information and time information obtainable from the posted information and the extracted frequently occurring words, and estimates the reason for the degree of congestion at the specified location based on the search results obtained from the search process. The system includes an output unit that outputs congestion information including the degree of congestion at the specified location, and at least one of the search results and the reason for the degree of congestion estimated based on the search results. Information processing device.

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