Processing device, processing method, and program

Abstract

To reduce oversight when checking an environment of a candidate area for residence.SOLUTION: A processing device disclosed herein comprises an acquisition unit for acquiring lifestyle pattern information regarding facilities to be used by a target user, an identification unit for identifying facility usage characteristics of the target user based on the lifestyle pattern information, and an estimation unit for estimating a facility that may be used by the target user in a candidate area for residence on the basis of the facility usage characteristics and map information.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a processing device, a processing method, and a program. [Background technology]

[0002] A technology related to the present disclosure is disclosed in Patent Document 1. Patent Document 1 discloses a technology that enables efficient selection of a destination area and rental property. When the technology receives an input from a user specifying an area, it displays a list of facilities in the area, categorized by type. The user can identify the facilities in the specified area based on the output information, and select a destination area based on the identified contents. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2014-16699 A Summary of the Invention [Problem to be solved by the invention]

[0004] When searching for a new residential location, it is preferable to check the environment of the candidate residential area. For example, it is preferable to check whether the candidate residential area has the desired type of facilities. By selecting a candidate residential area whose environment meets the desired conditions as the new residential area, it is possible to reduce inconveniences caused by the surrounding environment in the new residential area.

[0005] However, when a user lists the types of facilities to be checked for their existence in a candidate residential area, some may be omitted. For example, such an omission may occur when there are many types of facilities to be checked. If an omission occurs, the user may decide on a new residential area without checking whether a certain type of facility exists. This may result in an inconvenience that the new residential area does not have a certain type of facility. Patent Document 1 does not disclose this problem or a means for solving it.

[0006] In view of the above-mentioned problems, an example of an objective of the present disclosure is to provide a processing device, a processing method, and a program that reduce oversights when checking the environment of a candidate residence area. [Means for solving the problem]

[0007] According to the present disclosure, An acquisition means for acquiring life pattern information related to facilities used by a target user; An identification means for identifying facility usage characteristics of the target user based on the life pattern information; an estimation means for estimating facilities that the target user may use in the potential residence area based on the facility usage characteristics and map information; A processing apparatus is provided having:

[0008] Further, according to the present disclosure, One or more computers Acquire information on the lifestyle patterns of the target users related to the facilities they use, Identifying facility usage characteristics of the target user based on the life pattern information; A processing method is provided for estimating facilities that the target user may potentially use in a potential residence area based on the facility usage characteristics and map information.

[0009] Further, according to the present disclosure, Computer, An acquisition means for acquiring life pattern information related to facilities used by a target user; An identification means for identifying facility usage characteristics of the target user based on the life pattern information; an estimation means for estimating facilities that the target user may use in the potential residence area based on the facility usage characteristics and map information; A program is provided to function as a Effect of the Invention

[0010] According to one aspect of the present disclosure, a processing device, a processing method, and a program are realized that reduce oversights when checking the environment of a candidate residence area. [Brief description of the drawings]

[0011] [Figure 1] FIG. 2 illustrates an example of a functional block diagram of a processing device according to the present disclosure. [Diagram 2] 1 is a flowchart illustrating an example of a processing flow of a processing device according to the present disclosure. [Diagram 3] FIG. 2 is a diagram illustrating an example of a hardware configuration of a processing device according to the present disclosure. [Figure 4] FIG. 13 is a diagram illustrating another example of a functional block diagram of a processing device according to the present disclosure. [Diagram 5] FIG. 2 is a diagram illustrating an example of information processed by a processing device according to the present disclosure. [Figure 6] FIG. 10 is a diagram illustrating another example of information processed by the processing device according to the present disclosure. [Figure 7] 13 is a flowchart showing another example of the processing flow of the processing device according to the present disclosure. [Figure 8] FIG. 13 is a diagram illustrating another example of a functional block diagram of a processing device according to the present disclosure. [Figure 9] 13 is a flowchart showing another example of the processing flow of the processing device according to the present disclosure. [Figure 10] 13 is a flowchart showing another example of the processing flow of the processing device according to the present disclosure. [Figure 11] FIG. 13 is a diagram illustrating another example of a functional block diagram of a processing device according to the present disclosure. [Figure 12] FIG. 13 is a diagram illustrating an example of a screen output by a processing device according to the present disclosure. [Figure 13] 13 is a flowchart showing another example of the processing flow of the processing device according to the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In the present disclosure, the drawings relate to one or more embodiments. In all the drawings, similar components are denoted by similar reference numerals, and descriptions thereof will be omitted as appropriate.

[0013] <<First embodiment>> Fig. 1 is a functional block diagram showing an overview of the processing device 10. Fig. 2 is a flowchart showing an example of the flow of processing executed by the processing device 10.

[0014] 1, the processing device 10 includes an acquisition unit 11, a specification unit 12, and an estimation unit 13. These functional units execute the process of the flowchart in FIG.

[0015] The acquisition unit 11 acquires lifestyle pattern information related to facilities used by the target user (S10). The identification unit 12 identifies facility usage characteristics of a target user based on the life pattern information (S11). The estimation unit 13 estimates facilities that the target user may possibly use in the potential residence area, based on the facility usage characteristics and the map information (S12).

[0016] In this way, the processing device 10 estimates "facilities that the target user may use in the candidate residence area." Based on the estimation result, the user can understand the environment of the candidate residence area, i.e., the facilities that the target user may use in the candidate residence area. With such a processing device 10, the user can avoid the troublesome task of browsing a map or the like and identifying the facilities that the target user may use in the candidate residence area. This can also reduce the number of times the user misses something when checking the environment of the candidate residence area.

[0017] Furthermore, the processing device 10 estimates facilities that the target user may use in the candidate residence area based on the "lifestyle pattern information." The target user is likely to use the same types of facilities in the candidate residence area as those that he or she uses in daily life. The processing device 10, which performs the estimation based on the life pattern information, can estimate the facilities that the target user may use in the candidate residence area without missing any facilities that the target user may use.

[0018] The processing device 10 also identifies the "facility usage characteristics" of the target user based on the lifestyle pattern information, and estimates facilities that the target user may use in the candidate residence area based on the facility usage characteristics. With this processing device 10, facilities whose facility usage characteristics satisfy a predetermined condition can be estimated as facilities that the target user may use in the candidate residence area. By applying a filter using the facility usage characteristics in this way, it is possible to estimate highly important facilities that the target user is likely to use as facilities that the target user may use in the candidate residence area.

[0019] Furthermore, the processing device 10 does not simply estimate the type of facility that the target user may use in the candidate residence area, but identifies that type of facility that exists in the candidate residence area using map information. If the type of facility that the target user may use in the candidate residence area is simply estimated, the user who has confirmed the result must then check for himself or herself whether that type of facility exists in the candidate residence area. On the other hand, with the processing device 10 using map information to identify that type of facility that exists in the candidate residence area, the user can ascertain whether the type of facility that the user may use exists in the candidate residence area based on the identification result.

[0020] <<Second embodiment>> <Summary> The processing apparatus 10 of the second embodiment is a specific embodiment of the configuration of the processing apparatus 10 of the first embodiment, which will be described in detail below.

[0021] <Hardware configuration> Next, an example of the hardware configuration of the processing device 10 will be described. Each functional unit of the processing device 10 is realized by any combination of hardware and software. Those skilled in the art will understand that there are various variations in the realization method and device. The software includes programs that are stored in the device before it is shipped, and programs downloaded from recording media such as CDs (Compact Discs) and servers on the Internet.

[0022] 3 is a block diagram illustrating a hardware configuration of a processing device 10. As shown in FIG. 3, the processing device 10 has a processor 1A, a memory 2A, an input / output interface 3A, a peripheral circuit 4A, and a bus 5A. The peripheral circuit 4A includes various modules. The processing device 10 does not have to have the peripheral circuit 4A. The processing device 10 may be composed of multiple devices that are physically and / or logically separated. In this case, each of the multiple devices can have the above hardware configuration.

[0023] The bus 5A is a data transmission path for the processor 1A, the memory 2A, the peripheral circuit 4A, and the input / output interface 3A to transmit and receive data to each other. The processor 1A is, for example, a central processing unit (CPU) or a graphics processing unit (GPU). The memory 2A is, for example, a random access memory (RAM) or a read only memory (ROM). The input / output interface 3A includes an interface for acquiring information from an input device, an external device, an external server, an external sensor, a camera, etc., and an interface for outputting information to an output device, an external device, an external server, etc. The input / output interface 3A also includes an interface for connecting to a communication network such as the Internet. The input device is, for example, a keyboard, a mouse, a microphone, a physical button, a touch panel, etc. The output device is, for example, a display, a speaker, a printer, a mailer, etc. The processor 1A can issue commands to each module and perform calculations based on the results of those calculations.

[0024] <Functional configuration> Next, a detailed description will be given of the functional configuration of the processing device 10. Fig. 4 shows an example of a functional block diagram of the processing device 10. As shown in the figure, the processing device 10 has an acquisition unit 11, a specification unit 12, an estimation unit 13, and an output unit 14.

[0025] The acquisition unit 11 acquires lifestyle pattern information relating to facilities used by a target user.

[0026] A "used facility" is at least one of a facility that has been used and a facility that may be used.

[0027] "Facilities" refer to stores, equipment, buildings, etc. that are used in social life, and examples include, but are not limited to, supermarkets, convenience stores, gyms, izakayas, bars, florists, stations, and golf driving ranges.

[0028] The "life pattern information" indicates at least one of facilities that have been used in daily life and facilities that may be used in daily life. The life pattern information indicates at least one of the following: - Facility usage history by target users -Types of facilities that people with the attributes of the target user tend to use

[0029] The acquisition unit 11 can generate lifestyle pattern information of the target user based on at least one of the following information: - Location information of the target user - Surveillance camera images Data provided by the facility to users who use the facility Payment information at the facility Visitor / user lists recording who either visits or uses the facility - Target user attribute information

[0030] "Generating facility usage history (life pattern information)" The facility usage history is a history of facilities that have been used in the past. The facility usage history may further indicate information such as the date and time of use, the means of transportation (car, bus, bicycle, walking, etc.) when using each facility, and the travel route.

[0031] An example of lifestyle pattern information showing facility usage history is shown in Figure 5. The lifestyle pattern information shown in the figure shows the facility usage history of a target user identified by user identification information "M007284". The lifestyle pattern information shown in the figure links together usage date and time, facility name, means of transportation, and travel route.

[0032] The acquisition unit 11 can generate lifestyle pattern information indicating facility usage history by using at least one of the following first to fifth generation methods. Note that the acquisition unit 11 may use a combination of a plurality of generation methods.

[0033] First generation method The acquisition unit 11 can generate lifestyle pattern information indicating the facility usage history based on the location information of the target user.

[0034] The "location information" indicates the current location of the target user. The location information is, for example, latitude and longitude information indicating the current location of the target user, but is not limited to this.

[0035] The acquisition unit 11 identifies facilities visited by the target user based on the history of the target user's current location indicated by the location information (information linking the current location with the current date and time) and map data indicating the location of each facility. The acquisition unit 11 can then identify the facilities visited by the target user as facilities used by the target user.

[0036] For example, the acquisition unit 11 compares the current location of the target user with the area occupied by each facility. Then, the acquisition unit 11 identifies a facility whose area includes the current location of the target user as a facility visited by the target user.

[0037] The acquisition unit 11 may specify, as a used facility, a facility for which the usage time after visiting (the time spent at the facility) exceeds a threshold. The acquisition unit 11 may calculate the time during which the target user's current location continues to be included within the area as the usage time of each facility. The threshold is set in advance. The usage time when using each facility differs depending on the type of facility. For example, the usage time at a gym may be 30 minutes to 1 hour or more, while the usage time at a convenience store may be about a few minutes. Therefore, the threshold may be set for each type of facility.

[0038] Furthermore, the acquisition unit 11 can identify the date and time when each facility was used, based on the current date and time indicated in the history of the current location.

[0039] Furthermore, the acquisition unit 11 can identify the travel route taken to visit each facility based on the history of the current location.

[0040] The acquisition unit 11 can also identify the means of travel when using each facility based on the target user's travel speed before visiting each facility. The target user's travel speed can be calculated based on, for example, the distance between two points indicated in the current location history and the time required to travel between the two points. For example, the acquisition unit 11 may sample a plurality of pairs between two points from the current location history, and calculate the statistical value of the travel speed calculated for each pair as the target user's travel speed. The statistical value may be, but is not limited to, an average value, a mode value, a median value, a maximum value, a minimum value, or the like.

[0041] The estimated travel speed of each transportation means is registered in advance in a storage device. The storage device may be provided in the processing device 10, or may be provided in an external device accessible from the processing device 10. The acquisition unit 11 can then identify the transportation means corresponding to the travel speed of the target user before visiting each facility as the transportation means to be used when using each facility.

[0042] The acquisition of the target user's location information can be realized by using any widely known technology. For example, a location detection function (e.g., GPS (global positioning system) function) installed in a navigation system installed in a mobile terminal carried by the target user or a means of transportation (e.g., a car) used by the target user may be used. Examples of mobile terminals include, but are not limited to, smartphones, mobile phones, smart watches, tablet terminals, and wearable terminals.

[0043] The acquisition unit 11 can acquire location information acquired by the mobile terminal or navigation system of the target user by any means (e.g., via the Internet) and any timing (e.g., real-time processing, batch processing) by linking the location information to the target user. The acquisition can be realized by using any widely known technology.

[0044] "Acquisition" includes at least one of the following: a device goes to retrieve data or information stored in another device or storage medium (active acquisition), and a device inputs data or information output from another device (passive acquisition). Examples of active acquisition include making a request to another device and receiving a reply, and accessing and reading information from another device or storage medium. An example of passive acquisition is receiving information that is distributed (or transmitted, push notification, etc.). Furthermore, "acquisition" may also mean selecting and acquiring data or information from received data or information, or selecting and receiving distributed data or information.

[0045] Second generation method The acquisition unit 11 can generate life pattern information indicating the usage history of the facility based on images from the surveillance camera.

[0046] Surveillance cameras are installed in various locations. For example, surveillance cameras may be installed in various facilities. Surveillance cameras may also be installed on the road. The locations where surveillance cameras are installed are not limited to the examples given here. Images generated by the surveillance cameras are provided with information indicating the date and time of capture.

[0047] The acquisition unit 11 can acquire images generated by a surveillance camera by any means (e.g., via the Internet) and at any timing (e.g., real-time processing, batch processing). The acquisition can be realized by using any widely known technology.

[0048] In this example, the appearance information of the target user is stored in advance in a storage device. The appearance information includes at least one of an appearance image (e.g., a face image) and an appearance feature amount (e.g., face, clothing, physique, gait feature amount, etc.). The storage device may be provided in the processing device 10, or may be provided in an external device accessible from the processing device 10.

[0049] The acquisition unit 11 detects the target user from the image generated by the surveillance camera using the appearance information of the target user, and identifies the facilities visited by the target user based on the detection result. The acquisition unit 11 can then identify the facilities visited by the target user as facilities used by the target user.

[0050] For example, when a target user is detected in an image generated by a surveillance camera installed in each facility, the acquisition unit 11 can generate lifestyle pattern information indicating that the target user has used the facility. When a target user is detected in an image generated by a surveillance camera installed on a street, the acquisition unit 11 tracks the target user in the image. Then, when a facility is captured in the image, the acquisition unit 11 detects that the target user has entered the facility in the image. Then, based on the detection result, the acquisition unit 11 can generate lifestyle pattern information indicating that the target user has used the facility.

[0051] The acquisition unit 11 may detect each facility in the image based on, for example, the feature amount of the facility's appearance. In this example, appearance information of each facility is stored in advance in a storage device. The appearance information includes at least one of an appearance image and the feature amount of the appearance. The storage device may be provided in the processing device 10, or may be provided in an external device accessible from the processing device 10.

[0052] Furthermore, the acquisition unit 11 can identify the date and time when each facility was used based on the date and time when each image was taken.

[0053] The acquisition unit 11 can also identify the means of transportation used when using each facility based on the installation location of the surveillance camera that generated the image in which the target user was detected. For example, when the target user is detected in an image generated by a surveillance camera installed in a parking lot of the facility, the acquisition unit 11 can identify a car as the means of transportation used when using the facility. When the target user is detected in an image generated by a surveillance camera installed in a bicycle parking lot of the facility, the acquisition unit 11 can identify a bicycle as the means of transportation used when using the facility. The installation location of each surveillance camera is registered in advance in a storage device. The storage device may be provided in the processing device 10, or may be provided in an external device accessible from the processing device 10.

[0054] In addition, if a target user who is on a means of transportation, a target user who is getting off a means of transportation, or a target user who is about to get on a means of transportation can be detected in the image, the acquisition unit 11 can identify the means of transportation used when using the facility based on the detection result. The acquisition unit 11 can identify the type of means of transportation (car, bicycle, etc.) by image analysis.

[0055] For example, if the acquisition unit 11 can detect a target user riding on a means of transportation in an image generated by a surveillance camera installed on the street before using each facility (e.g., during the period from the date and time of use to a specified time before), the acquisition unit 11 can identify that means of transportation as the means of transportation used when using the facility.

[0056] Furthermore, the acquisition unit 11 can identify the travel route taken to visit each facility based on the detection result of the target user in the images generated by each of the multiple surveillance cameras and the installation positions of each surveillance camera.

[0057] ○Third generation method The acquisition unit 11 can generate lifestyle pattern information indicating the facility usage history based on data provided by the facility to a target user who has used the facility.

[0058] A facility may provide predetermined data to a target user who has used the facility. The data provided to the target user who has used the facility may be, for example, but is not limited to, an electronic receipt. The data provided to the target user who has used the facility indicates information that identifies the facility used and the date and time of use. The data provided to the target user who has used the facility is stored in a server of the facility, a server that provides various services related to electronic receipts, a mobile terminal of the target user, etc.

[0059] The acquisition unit 11 can acquire the data stored in the server or the mobile terminal of the target user by any means (e.g., via the Internet) and at any timing (e.g., real-time processing, batch processing) by linking the data to the target user. The acquisition can be realized by using any widely known technology.

[0060] The acquisition unit 11 then identifies the facilities used by the target user based on the data. The acquisition unit 11 can also identify the date and time of use of each facility based on the content indicated by the data.

[0061] Fourth generation method The acquisition unit 11 can generate lifestyle pattern information indicating the history of facility usage, based on information regarding payments at facilities.

[0062] "Information regarding payments at facilities" is, for example, the usage history of various types of payments, such as credit card payments, code payments, electronic money payments, and point payments. The usage history of payments indicates the facility at which the payment was made, the date and time of the payment, the payment amount, etc. The usage history of payments is stored in a server that provides payment services, a server that provides household accounting management services, a mobile terminal of the target user, etc.

[0063] The acquisition unit 11 can acquire the payment usage history stored in the server or the mobile terminal of the target user by any means (e.g., via the Internet) and at any timing (e.g., real-time processing, batch processing) by linking it to the target user. The acquisition can be realized by using any widely known technology.

[0064] The acquisition unit 11 then identifies the facility used by the target user based on the payment usage history. The acquisition unit 11 can also identify the date and time of use of each facility based on the content indicated in the payment usage history.

[0065] ○ Fifth generation method The acquisition unit 11 can generate lifestyle pattern information indicating the facility usage history based on a visitor / user list that records people who either visit or use the facility.

[0066] Some facilities identify and record those who either visit or use the facility. For example, the facility installs equipment (gates, doors, etc.) at the entrances and exits to manage entrance and exit, and allows entry only to users who are successfully authenticated by the equipment. The facility's system then records the identification information of the successfully authenticated user and the date and time of the successful authentication in a visitor / user list. There are no particular limitations on the means of authentication. For example, the authentication may be biometric authentication using biometric information (e.g., face information, fingerprint information, voiceprint information, iris information, etc.), authentication using information stored in a mobile terminal or an IC (Integrated Circuit) card, or other methods. Examples of such facilities include, but are not limited to, stations and unmanned stores.

[0067] The acquisition unit 11 can acquire the visitor / user list stored in the facility's system by any means (e.g., via the Internet) and at any timing (e.g., real-time processing, batch processing) by linking the visitor / user list to each facility. The acquisition can be realized by using any well-known technology.

[0068] The acquisition unit 11 then identifies the facility used by the target user based on the visitor / user list of the facility. The acquisition unit 11 can also identify the date and time of use of each facility based on the contents of the visitor / user list of the facility.

[0069] “Generation of types of facilities (lifestyle pattern information) that people with the attributes of the target user tend to use” There is a tendency for target users with certain attributes to use the types of facilities. The attributes include, but are not limited to, gender, age, height, weight, occupation, annual income, hobbies, whether they have a partner, whether they are married, whether they have children, and the ages of their children. For example, a target user with attributes such as "gender: male", "age: 30-60 years old", and "hobby: golf" tends to use a golf driving range.

[0070] Therefore, the acquisition unit 11 can generate lifestyle pattern information indicating the types of facilities that are likely to be used by people who have the attributes of the target user, based on the attribute information of the target user.

[0071] In this example, the attributes of the target user are stored in advance in a storage device. The storage device may be provided in the processing device 10, or may be provided in an external device accessible from the processing device 10. The target user registers his / her attributes at any time. The registration can be realized by using any widely known technology.

[0072] In this example, tendency information indicating attributes of target users who tend to use various facilities is generated in advance and stored in a storage device. The storage device may be provided in the processing device 10, or in an external device accessible from the processing device 10. The tendency information may be generated by a person and registered in the storage device. Additionally, the processing device 10 may calculate attributes of target users who tend to use various facilities by statistical processing based on the facility usage history of the target users and the attributes of the target users described above. Then, the processing device 10 may generate tendency information indicating the calculation results.

[0073] When the acquisition unit 11 acquires the attributes of the target user stored in the storage device, the acquisition unit 11 identifies the types of facilities that people having the attributes of the target user tend to use based on the acquired attributes of the target user and the above-mentioned tendency information. Then, the acquisition unit 11 generates lifestyle pattern information indicating the identified contents.

[0074] Returning to FIG. 4, the identification unit 12 identifies the facility usage characteristics of the target user based on the lifestyle pattern information acquired by the acquisition unit 11.

[0075] When using lifestyle pattern information that is a facility usage history, the acquisition unit 11 can identify the facility usage characteristics of the target user based on the usage history within a predetermined period. The predetermined period is, for example, the most recent one year, the most recent six months, etc., but is not limited to these. Note that the target user may be able to change the predetermined period by himself / herself.

[0076] The "facility use characteristics" are various characteristics related to the use of the facility. The identification unit 12 can identify at least one of the following as the facility use characteristics. -Type of facility used Number of times each facility was used Frequency of use of various facilities ·Times when various facilities can be used Combinations of various facilities to be used in combination - Tendencies in the order of use of various facilities in combination ·Transportation when using various facilities -Information about routes taken when using various facilities - Tendencies in facility usage characteristics when people with the same attributes as the target user use various facilities

[0077] An example of the facility usage characteristics is shown in Fig. 6. The facility usage characteristics shown in the figure indicate the facility usage characteristics of a target user identified by user identification information "M007284". The facility usage characteristics shown in the figure include the items described above.

[0078] "Type of facility used" Facilities are classified into a plurality of types. There are various ways of classifying facilities, but they can be classified based on the type of business, business format, service content, etc., such as "convenience store," "supermarket," "golf driving range," etc.

[0079] Facility type information indicating the type of facility to which each facility belongs is stored in advance in a storage device. The storage device may be provided in the processing device 10 or in an external device accessible from the processing device 10.

[0080] The acquisition unit 11 can identify the facility used by the target user based on the facility usage history (lifestyle pattern information) of the target user within a predetermined period of time. Then, the acquisition unit 11 can identify the type of facility used by the target user based on the result of the identification and the facility type information.

[0081] "Number of times various facilities have been used" The acquisition unit 11 can generate the information by counting the number of times the target user has used each type of facility based on the target user's facility usage history (lifestyle pattern information) within a predetermined period of time.

[0082] "Frequency of use of various facilities" The acquisition unit 11 can generate the information by counting the number of times the target user uses each type of facility within a specified unit period based on the target user's facility usage history (lifestyle pattern information) within a specified period.

[0083] The unit period may be, but is not limited to, one week, one month, etc. For example, the acquisition unit 11 counts the number of times each type of facility is used each week (or each month). The acquisition unit 11 can then calculate, for each type of facility, a statistical value of the count results for each week (or each month) as the frequency of use of each type of facility. The statistical value may be, but is not limited to, an average value, a mode value, a median value, a maximum value, a minimum value, etc.

[0084] "Times when various facilities can be used" The acquisition unit 11 can generate the information by identifying the time period during which the target user uses each type of facility based on the target user's facility usage history (lifestyle pattern information) within a predetermined period of time.

[0085] For example, the acquisition unit 11 calculates the tendency of the time period during which various facilities are used based on the target user's facility usage history (lifestyle pattern information) within a predetermined period. Then, the acquisition unit 11 can identify the time period during which various facilities are used based on the calculated tendency. An example will be described below, but is not limited to this.

[0086] First, the acquisition unit 11 classifies the 24 hours into predetermined unit times. For example, the unit time is 1 hour, and the time is classified into 24 time slots such as 0:00 to 1:00, 1:00 to 2:00, etc. Note that this classification method is merely an example and is not limited to this.

[0087] The acquisition unit 11 then counts the number of times the target user uses various facilities for each time period. The acquisition unit 11 then identifies at least one time period in which the count value satisfies a predetermined condition as the time period in which the target user uses various facilities. Examples of the predetermined condition include, but are not limited to, "the time period with the highest count value," "a predetermined rank or higher in the order of the highest count value," and "a predetermined number or higher count value."

[0088] "Combination of various facilities for use" A "combination of facilities to be used in combination" is a combination in which, if one type of facility is used, the user tends to use the other type of facility afterwards. For example, there is a target user who uses a "gym" and then uses a "pub."

[0089] The acquisition unit 11 can generate the information by identifying combinations of various facilities that are used in combination for each target user based on the facility usage history (lifestyle pattern information) of the target user within a predetermined period of time.

[0090] For example, the acquisition unit 11 may specify two types of facilities that satisfy the condition that "the use date is the same and the difference in use time is within a threshold value" as a combination of various facilities to be used in combination.

[0091] In addition, the acquisition unit 11 may identify two facility types that satisfy the condition that "the use date is the same and the difference in use time is within a threshold value" a specified number of times or more within a specified period as a combination of various facilities to be used in combination.

[0092] Alternatively, the acquisition unit 11 may specify, as a combination of various facilities to be used in combination, two types of facilities that satisfy the condition "the same date of use, and after using one type of facility, the user uses the other type of facility without returning home" and that are used a predetermined number of times or more within a predetermined period of time.

[0093] In these cases, the location of the target user's home is registered in advance in a storage device. The storage device may be provided in the processing device 10, or may be provided in an external device accessible from the processing device 10. The target user registers the location of his or her home by himself or herself. Then, the acquisition unit 11 can determine whether the condition "using one type of facility and then using the other type of facility without returning home" is satisfied based on the history of the target user's current location.

[0094] "Tendency of order of use of various facilities in combination" The acquisition unit 11 can generate the information by identifying a tendency of a usage order of various facilities that are used in combination for each target user based on the facility usage history (lifestyle pattern information) of the target user within a predetermined period of time.

[0095] For example, the acquisition unit 11 identifies a combination of various facilities to be used in combination using the above-mentioned method. Then, for each identified combination, the acquisition unit 11 can calculate "the ratio of using one facility after using the other facility" and "the ratio of using one facility after using the other facility."

[0096] "Means of transportation when using various facilities" The acquisition unit 11 can generate the information by identifying the means of transportation used when using various facilities for each target user based on the utilization history of the facilities (lifestyle pattern information) within a predetermined period of time of the target user.

[0097] "Information on routes to and from various facilities" The target user may bring certain belongings with him / her when using various facilities. For example, a target user who uses a golf driving range may bring golf equipment with him / her. The target user may also carry luggage after using various facilities. For example, a target user who uses a supermarket may carry products purchased there with him / her. For this reason, the target user may select a travel route depending on the type of facility to be used.

[0098] According to this information, it is possible to know what characteristics of routes the target user uses when visiting various facilities.

[0099] The acquisition unit 11 can identify the travel route when using various facilities for each target user based on the utilization history (lifestyle pattern information) of the facilities within a predetermined period of time of the target user. Then, the acquisition unit 11 can generate the information by acquiring information on the identified travel route from the map information.

[0100] Information regarding a travel route acquired from map information includes safety, pavement condition, whether there are slopes, whether there are stairs, road width, barrier-free, etc. The map information includes such information linked to each road.

[0101] "Tendencies in facility usage characteristics when people with the same attributes as the target user use various facilities" The above-mentioned facility usage characteristics may correspond to the attributes of the target user. The attributes include, but are not limited to, gender, age, height, weight, occupation, annual income, hobbies, whether or not the user has a partner, whether or not the user is married, whether or not the user has children, and the age of the children. The attribute information of the target user is stored in advance in a storage device. The storage device may be provided in the processing device 10, or may be provided in an external device accessible from the processing device 10.

[0102] The acquisition unit 11 can identify, as the facility usage characteristic of the target user, a tendency of facility usage characteristics of a person having the same attributes as the target user when using various facilities.

[0103] In this example, the attributes of the target user are stored in advance in a storage device. The storage device may be provided in the processing device 10, or may be provided in an external device accessible from the processing device 10. The target user registers his / her attributes at any time. The registration can be realized by using any widely known technology.

[0104] In this example, usage tendency information indicating the tendency of facility usage characteristics of various facilities for people with each attribute is generated in advance and stored in a storage device. The storage device may be provided in the processing device 10, or in an external device accessible from the processing device 10. The usage tendency information may be generated by a person and registered in the storage device. In addition, the processing device 10 may calculate the usage tendency information by statistical processing based on the facility usage characteristics of the target user and the attributes of the target user described above.

[0105] The acquisition unit 11 searches for utilization tendency information using the attributes of the target user and the type of the predetermined facility as keys, and acquires facility utilization tendency of a person having the same attributes as the target user for the predetermined type of facility. The type of the predetermined facility is "the type of facility used by the target user shown in the facility utilization history of the target user" or "the type of facility that is likely to be used by a person having the attributes of the target user."

[0106] Returning to FIG. 4, the estimation unit 13 estimates facilities that the target user may potentially use in the potential residence area, based on the facility usage characteristics and map information.

[0107] The "candidate residential area" is a candidate for a new residential area determined by the target user. The candidate residential area is an area different from the current residential area of ​​the target user. The target user can determine at least one area as a candidate residential area and input it to the processing device 10.

[0108] The estimation unit 13 can accept the designation of the potential residence area based on at least one of the following first to third designation methods.

[0109] ○ First method The target user specifies at least one candidate residence point on a UI (user interface) screen. A "candidate residence point" is a candidate for a new residence point. The target user may specify a candidate residence point by, for example, inputting an address, a building name, or the like, into the processing device 10. Alternatively, the target user may specify a candidate residence point by specifying a point on a map displayed on the UI screen.

[0110] The processing device 10 may display a UI screen. Then, the target user may perform various inputs via an input device provided in the processing device 10. Examples of the input device include, but are not limited to, a touch panel, a keyboard, a mouse, a microphone, etc. In addition, the processing device 10 may be a server. Then, the client terminal may display the UI screen. The client terminal may be, but is not limited to, a smartphone, a mobile phone, a smart watch, a tablet terminal, a wearable terminal, a personal computer, etc. Then, the target user may perform various inputs via the client terminal. These assumptions are applicable to all embodiments.

[0111] In this example, the estimation unit 13 identifies a candidate residence area based on the specified candidate residence point. For example, the estimation unit 13 may identify an area within a predetermined distance from the candidate residence point as the candidate residence area. Alternatively, the estimation unit 13 may identify a predetermined area (town, city, ward, etc.) including the candidate residence point as the candidate residence area.

[0112] ○ Second method of specification The target user designates at least one region as a candidate residence area. The regions are identified from each other by their addresses. In this example, the estimation unit 13 accepts an input designating at least one region. For example, the estimation unit 13 can accept designation of one region in any unit such as a prefecture, city, ward, town, or village. The target user designates at least one region as a candidate residence area in such a unit. Then, the estimation unit 13 identifies the designated region as the candidate residence area.

[0113] ○Third method of specification The target user specifies a predetermined area on the map displayed on the UI screen. The target user can specify an area of ​​a predetermined shape and a predetermined size on the map, regardless of the boundaries of the region. The shape can be exemplified by a circle, a rectangle, etc., but is not limited to these.

[0114] In this example, the estimation unit 13 receives an input specifying at least one area on the map, and identifies the specified area as a candidate residence area.

[0115] Next, a process for estimating facilities that the target user may potentially use in a potential residence area will be described.

[0116] The estimation unit 13 identifies facilities that satisfy at least one of the following first use conditions as facilities that the target user may potentially use in the potential residence area. Facilities that exist in the potential residential area and are of the same type as the facilities that have been indicated as having been used in the facility usage characteristics Facilities of the same type that exist in the potential residential area and that have been used a certain number of times or more in the facility usage characteristics Facilities of the same type that exist in the potential residential area and that are indicated to have been used at a frequency equal to or higher than a specified level in the facility usage characteristics Facilities of the same type that exist in the potential residential area and that are shown to be used by people with the attributes of the target user in terms of facility usage characteristics

[0117] In addition, the estimation unit 13 may identify a facility that satisfies at least one of the above-mentioned first usage conditions and also satisfies at least one of the following second usage conditions as a facility that the target user may potentially use in the potential residence area. - Distance from the proposed residence point is below the threshold - Travel time from the proposed residence point is below the threshold - Open during the hours when various facilities are in use, as indicated by the facility usage characteristics For facility types that are indicated to be used in combination with other types of facilities in the facility usage characteristics, the distance from other types of facilities is below the threshold. For facility types that are indicated to be used in combination with other types of facilities in the facility usage characteristics, the travel time from other types of facilities is below a threshold. For facilities where the facility usage characteristics indicate that cars are a means of transportation, there must be a parking lot at or near the facility. - The travel route from the proposed residence location meets the route conditions.

[0118] The estimation unit 13 can calculate the distance from the residence candidate point to each facility based on the map information. The residence candidate point is specified by the target user. The method of specification is the same as the first specification method described above.

[0119] The estimation unit 13 can also calculate the travel time from the candidate residence point to each facility based on map information. The travel time can be calculated using any well-known technology. For example, a travel time calculation technology in a navigation technology that provides route guidance from a starting point to a destination point can be used.

[0120] The estimation unit 13 can also determine whether each facility is open during a given time period based on facility information of each facility that is stored in advance in a storage device. The facility information of each facility indicates the business hours. The storage device may be provided in the processing device 10 or in an external device accessible from the processing device 10.

[0121] Furthermore, the estimation unit 13 can calculate the distance between two facilities that are used in combination based on map information, and can calculate the travel time between the two facilities.

[0122] The estimation unit 13 can also determine whether each facility has a parking lot based on facility information of each facility that is stored in advance in a storage device. The facility information of each facility indicates whether or not the facility has a parking lot. The storage device may be provided in the processing device 10, or may be provided in an external device that can be accessed from the processing device 10.

[0123] Furthermore, the estimation unit 13 can determine whether there is a parking lot around each facility based on map information. The "around each facility" is, for example, an area within a predetermined distance from each facility, but is not limited to this.

[0124] The "route conditions that the travel route from the candidate residence point must satisfy" are determined for each target user based on the "information on travel routes when using various facilities" in the facility usage characteristics. The "information on travel routes when using various facilities" in the facility usage characteristics indicates the characteristics of the travel routes when the target user uses each facility.

[0125] Specifically, the route condition is that at least one of the characteristics of the travel route indicated in the facility usage characteristics "information on travel routes when using various facilities" is satisfied. For example, the route condition may be that all of the characteristics of the travel route indicated in the facility usage characteristics "information on travel routes when using various facilities" are satisfied. Alternatively, the route condition may be that a predetermined percentage or more of the characteristics of the travel route indicated in the facility usage characteristics "information on travel routes when using various facilities" are satisfied. Alternatively, the route condition may be that a predetermined number or more of the characteristics of the travel route indicated in the facility usage characteristics "information on travel routes when using various facilities" are satisfied.

[0126] The output unit 14 outputs the estimation result of the estimation unit 13. That is, the output unit 14 outputs information indicating facilities that the target user may possibly use in the candidate residence area (hereinafter, may be referred to as "available facilities").

[0127] The output unit 14 may output a list of the names of the available facilities. The list may further include additional information about the available facilities. The additional information may include an address, a telephone number, a type of facility, whether or not there is a parking lot, etc.

[0128] Additionally, the output unit 14 may display a map and also output a screen indicating the positions of the available facilities on the map. Additional information on the available facilities may also be displayed on the screen. The additional information may include an address, a telephone number, a type of facility, whether or not there is parking, etc. For example, the additional information on each available facility is displayed in association with the position of each available facility indicated on the map.

[0129] In addition, the output unit 14 may further output information indicating the types of facilities that satisfy at least one of the following conditions but do not exist in the candidate residence area. The output unit 14 may output a list of the names of such types of facilities. -Type of facility that was shown to have been used in the facility usage characteristics -Type of facility that has been used a certain number of times or more in the facility usage characteristics -Type of facility that is shown to have been used at a certain level or more frequently in the facility usage characteristics -Types of facilities that are shown to be used by people with the attributes of the target user in terms of facility usage characteristics

[0130] The type of facility that satisfies at least one of the above conditions is the type of facility that the target user may use in the candidate residence area. Information indicating the type of facility that the target user may use in the candidate residence area but does not exist in the candidate residence area can be useful information when evaluating the candidate residence area.

[0131] The output unit 14 can output the above-mentioned information via an output device included in the processing device 10. The output device is, but is not limited to, a display, a projection device, etc. Alternatively, the processing device 10 may be a server. The output unit 14 may transmit the above-mentioned information to a client terminal and cause the client terminal to output the above-mentioned information. The client terminal outputs the above-mentioned information via an output device such as a display or a projection device.

[0132] Next, an example of the process flow of the processing device 10 will be described with reference to the flowchart of Fig. 7. Note that the purpose here is to explain the process flow. Details of each process have been described above, so the description here will be omitted.

[0133] In S20, the processing device 10 acquires user identification information of the target user and information indicating the candidate residence area.

[0134] For example, only target users who have registered as members in advance can use the services provided by the processing device 10. By registering as a member, the target users obtain information (user identification information, login password, etc.) for logging in to the processing device 10. Then, the target users who wish to use the services provided by the processing device 10 log in to the processing device 10 using the login information. In S20, the processing device 10 can obtain the user identification information of the target users entered at the time of this login.

[0135] Then, the processing device 10 accepts input of information indicating at least one candidate residence area (designation of the candidate residence area) from the target user on the screen after login. The processing device 10 can accept the designation of at least one candidate residence area based on at least one of the first to third designation methods described above.

[0136] In S21, the processing device 10 acquires lifestyle pattern information of the target user.

[0137] For example, life pattern information for each of a plurality of target users is generated in advance and stored in a storage device. The method of generating the life pattern information is as described above. The storage device may be provided in the processing device 10, or in an external device accessible from the processing device 10. Then, in S21, the processing device 10 acquires, from the storage device, the life pattern information of the target user identified by the user identification information acquired in S20.

[0138] In S22, the processing device 10 identifies the facility usage characteristics of the target user based on the life pattern information acquired in S21. The method of identifying the facility usage characteristics is as described above.

[0139] As a modified example, the facility usage characteristics of each of the multiple target users may be specified in advance based on the lifestyle pattern information of each of the multiple target users and stored in a storage device. The storage device may be provided in the processing device 10, or in an external device accessible from the processing device 10. Then, in S22, the processing device 10 may acquire the facility usage characteristics of the target user identified by the user identification information acquired in S20 from the storage device. In the case of this modified example, S21 may not be necessary. That is, the processing device 10 may perform S22 after S20 without performing S21.

[0140] In S23, the processing device 10 estimates facilities that the target user may use in the potential residence area indicated by the information acquired in S20, based on the facility usage characteristics and map information acquired in S22. Details of the estimation process are as described above.

[0141] In S24, the processing device 10 outputs the estimation result of S23.

[0142] <Action and effect> According to the processing apparatus 10 of this embodiment, the same operational effects as those of the processing apparatus 10 of the first embodiment are achieved.

[0143] Furthermore, the processing device 10 can generate lifestyle pattern information of the target user based on the above-mentioned characteristic information. According to such a processing device 10, it is possible to generate lifestyle pattern information of the target user with high accuracy.

[0144] Furthermore, the processing device 10 can identify facility usage characteristics including characteristic content for each target user. Then, the processing device 10 can estimate facilities that the target user may use in the candidate residence area based on such facility usage characteristics. With such a processing device 10, it is possible to accurately estimate facilities that the target user may use in the candidate residence area.

[0145] <<Third embodiment>> The processing device 10 further has a function of calculating the distance between a facility (available facility) that the target user may use in the residence candidate area and the residence candidate point, and outputting information about the distance. This will be described in detail below.

[0146] 8 shows an example of a functional block diagram of the processing device 10. As shown in the figure, the processing device 10 includes an acquisition unit 11, a specification unit 12, an estimation unit 13, an output unit 14, and an additional information provision unit 15.

[0147] The additional information providing unit 15 calculates the distance between the facility (available facility) that the target user may use in the residence candidate area and the residence candidate point. The additional information providing unit 15 then generates information on the calculated distance (hereinafter, sometimes referred to as the "calculated distance"). The output unit 14 then outputs the generated information.

[0148] The available facilities and the residence candidate points are identified by the method described in the second embodiment. The additional information providing unit 15 calculates the distance between each of the available facilities and the residence candidate points based on the map information. The calculation of the distance between the two points is realized by using any well-known technology.

[0149] The additional information providing unit 15 generates at least one of the following pieces of information as information related to the calculated distance: First information that identifies and displays a location on a map that is a calculated distance away from the target user's current residence location - Second information indicating a facility located at a calculated distance away from the target user's current residence point

[0150] The first information is an image that identifies and displays a location that is a calculated distance away from the target user's current residence location on a map. The second information is information (e.g., text information or image information) that indicates a facility that is located at a location that is a calculated distance away from the target user's current residence location.

[0151] In this example, the target user inputs his / her current location of residence into the processing device 10 .

[0152] If there are multiple available facilities, the additional information providing unit 15 can generate information on the calculated distance for each available facility. The output unit 14 may output information on the calculated distance for multiple available facilities simultaneously. That is, information on multiple available facilities may be superimposed simultaneously on the map. However, such display may result in an excessive amount of information, making it difficult to view. Therefore, the output unit 14 may display information on one available facility on the map, and switch the information to be displayed according to user input.

[0153] Other configurations of the processing apparatus 10 of this embodiment are similar to those of the processing apparatus 10 of the first and second embodiments.

[0154] According to the processing device 10 of this embodiment, the same action and effect as the processing device 10 of the first and second embodiments is realized. Moreover, the processing device 10 of this embodiment can calculate the distance between the available facility that the target user may use in the candidate residence area and the candidate residence point, and generate and output information on the distance. According to such a processing device 10, the target user can grasp the distance between the candidate residence point and the available facility based on the output information. Then, the target user can evaluate the candidate residence area based on the grasped content.

[0155] Furthermore, the processing device 10 can generate and output at least one of the first information and the second information as information related to the distance. According to the information, the target user can understand the distance between the residence candidate point and the available facility by comparing it with the distance from the current residence point. As a result, the target user can intuitively understand the distance between the residence candidate point and the available facility.

[0156] <<Fourth embodiment>> In a candidate residence area, there is a possibility that multiple target users will live together. In such a case, the processing device 10 of the present embodiment estimates facilities that multiple target users may use in the candidate residence area using a characteristic method. This will be described in detail below.

[0157] The acquisition unit 11 acquires lifestyle pattern information of each of a plurality of target users who plan to live together in a candidate residence area.

[0158] The identification unit 12 identifies the facility usage characteristics of each of a plurality of target users who plan to live together.

[0159] The estimation unit 13 estimates facilities that are likely to be used by multiple target users who plan to live together in the proposed residence area, based on the facility usage characteristics of each of the multiple target users who plan to live together.

[0160] The estimation unit 13 identifies at least one of the following facilities as a facility that is likely to be used by multiple target users who plan to live together in the potential residence area. Facilities that are located in the potential residential area and are estimated to be used by at least one of the multiple target users who plan to live together Facilities that are estimated to be in the potential residential area and that may be used by all of the multiple target users who plan to live together Facilities that are located in the potential residential area and are estimated to be used by a certain percentage or more of the multiple target users who plan to live together.

[0161] As a modified example, the estimation unit 13 may estimate facilities that are likely to be used by multiple target users who plan to live together in a potential residence area, through the following process.

[0162] In this modified example, the estimation unit 13 weights the multiple target users who plan to live together. Then, the estimation unit 13 estimates facilities that the multiple target users may use in the potential residence area, using the weighting values ​​of the multiple target users who plan to live together.

[0163] First, the process of weighting a plurality of target users who plan to live together will be described.

[0164] The weighting value of the target user may be determined by any of the target users and input to the processing device 10. In addition, the estimation unit 13 may determine the weighting value of the target user based on the relationship between the multiple target users who plan to live together and the attribute information of each of the multiple target users who plan to live together.

[0165] For example, the relationship between multiple target users who plan to live together is a relationship such as father, mother, and child. And, the weighting value of each of them is set in advance. The estimation unit 13 determines the weighting value of the target users based on the setting contents. For example, the weighting value of the mother is set to be the highest, the weighting value of the child is set to be the next highest, and the weighting value of the father is set to be the lowest. In addition, the relationship between multiple target users who plan to live together is a relationship such as friends, roommates, or lovers. The weighting values ​​of multiple target users who have such relationships may be set to the same value.

[0166] In addition, the estimation unit 13 may determine the weighting value of the target user based on the age and sex (attribute information) of multiple target users who plan to live together. For example, the estimation unit 13 can determine the weighting value of the target user based on a predetermined weighting value determination rule and the age and sex of each of the multiple target users who plan to live together. The weighting value determination rule may be such that the older the target user is, the higher the weighting value is determined. Also, the weighting value determination rule may be such that a higher weighting value is determined for a female target user than for a male target user.

[0167] Next, a process of estimating facilities that are likely to be used by multiple target users in a candidate residence area, using the weighting values ​​of each of multiple target users who plan to live together, will be described.

[0168] First, the estimation unit 13 identifies facilities that exist in the candidate residence area and that may be used by at least one of the target users who plan to live together. Then, the estimation unit 13 calculates the importance of each identified facility using a predetermined calculation formula.

[0169] The calculation formula is determined so that the importance of the facility increases as the weighting value of the target user estimated to use the facility increases. The calculation formula is also determined so that the importance of the facility increases as the number of target users estimated to use the facility increases. For example, the importance of each facility may be the sum of the weighting values ​​of at least one target user estimated to use the facility.

[0170] Then, the estimation unit 13 can estimate, among the identified facilities, the facilities whose importance meets a predetermined condition as facilities that may be used by multiple target users who plan to live together. The predetermined condition may be, but is not limited to, "importance is equal to or higher than a threshold" or "a predetermined rank or higher in the ranking of multiple facilities arranged in order of importance."

[0171] Next, an example of the process flow of the processing device 10 will be described with reference to the flowchart of Fig. 9. Note that the purpose here is to explain the process flow. Details of each process have been described above, so the description here will be omitted.

[0172] In S30, the processing device 10 acquires user identification information of a plurality of target users who plan to live together and information indicating a potential residence area.

[0173] For example, only target users who have registered as members in advance can use the services provided by the processing device 10. By registering as a member, the target users obtain information (user identification information, login password, etc.) for logging in to the processing device 10. Then, the target users who wish to use the services provided by the processing device 10 log in to the processing device 10 using the login information. In S30, the processing device 10 can obtain the user identification information of the target users entered at the time of this login.

[0174] Then, on the screen after login, the processing device 10 accepts, from the target user, input of user identification information of each of the multiple target users who plan to live together.

[0175] Furthermore, the processing device 10 accepts input of information indicating at least one candidate residence area (designation of the candidate residence area) from the target user on the screen after login. The processing device 10 can accept the designation of at least one candidate residence area based on at least one of the first to third designation methods described in the second embodiment.

[0176] In S31, the processing device 10 acquires lifestyle pattern information of each of a plurality of target users who plan to live together.

[0177] For example, lifestyle pattern information for each of the multiple target users is generated in advance and stored in a storage device. The storage device may be provided in the processing device 10, or may be provided in an external device accessible from the processing device 10. Then, in S31, the processing device 10 acquires, from the storage device, lifestyle pattern information for each of the multiple target users who plan to live together and are identified by the multiple user identification information acquired in S30.

[0178] In S32, the processing device 10 identifies the facility usage characteristics of each of the multiple target users who plan to live together, based on the life pattern information acquired in S31.

[0179] As a modified example, the facility usage characteristics of each of the multiple target users may be specified in advance based on the lifestyle pattern information of each of the multiple target users and stored in a storage device. The storage device may be provided in the processing device 10, or in an external device accessible from the processing device 10. Then, in S32, the processing device 10 may acquire the facility usage characteristics of each of the multiple target users who plan to live together and are identified by the multiple user identification information acquired in S30 from the storage device. In the case of this modified example, S31 may not be necessary. That is, the processing device 10 may perform S32 after S30 without performing S31.

[0180] In S33, the processing device 10 estimates facilities that may be used by each of the multiple target users who plan to live together in the candidate residence area indicated by the information acquired in S30, based on the facility usage characteristics and map information acquired in S32. Then, the processing device 10 estimates facilities that may be used by the multiple target users who plan to live together in the candidate residence area, based on the estimation result.

[0181] In S34, the processing device 10 outputs the estimation result of S33.

[0182] Other configurations of the processing apparatus 10 of this embodiment are similar to those of the processing apparatus 10 of the first to third embodiments.

[0183] According to the processing device 10 of this embodiment, the same action and effect as the processing device 10 of the first to third embodiments is realized. Furthermore, when multiple target users live together in a candidate residence area, the processing device 10 of this embodiment can estimate facilities that the multiple target users may use in the candidate residence area by a characteristic method.

[0184] In addition, when facilities that at least one of the multiple target users who plan to live together may use are output, the output information may be excessive. Therefore, the processing device 10 can estimate facilities that multiple target users may use in the candidate residential area, for example, by considering the relationships between the multiple target users and the attribute information of each of the multiple target users. With such a processing device 10, the facilities to be confirmed can be narrowed down to facilities with high importance.

[0185] <<Fifth embodiment>> The processing device 10 of this embodiment can estimate facilities that the target user is likely to use in the potential residence area, based on future life pattern information related to facilities that the target user will use in the future. This will be described in detail below.

[0186] The acquisition unit 11 predicts future events that will occur to the target user, and acquires future life pattern information related to facilities that the target user will use in the future based on the predicted events.

[0187] A "future event" is an event that may commonly occur in life, such as, but not limited to, getting a job, getting married, having a child, retiring, etc.

[0188] The acquisition unit 11 may predict events that may occur within the entire future period, or may predict events that may occur within a predetermined future period. The predetermined period is preferably close to the period during which the person will continue to live in the candidate residence area, and is, for example, several years to several decades, but is not limited thereto. The target user may input the predetermined period into the processing device 10 and specify it by himself / herself.

[0189] The acquisition unit 11 predicts future events that will occur to the target user based on at least one of the following: - Target user attribute information - Life plans created by the target users - Target users' past behavior patterns

[0190] “Predicting future events that will occur to a target user based on the target user's attribute information” The attributes include, but are not limited to, gender, age, height, weight, occupation, annual income, hobbies, whether or not the user has a partner, whether or not the user is married, whether or not the user has children, and the age of the children. The attribute information of the target user is stored in advance in a storage device. The storage device may be provided in the processing device 10, or may be provided in an external device accessible from the processing device 10.

[0191] In addition, an estimation model for predicting future events from the attributes of a target user is generated in advance. The acquisition unit 11 uses this estimation model to predict future events that will occur to a target user having a predetermined attribute. The estimation model may be generated by machine learning. In this case, the estimation model is generated by learning based on learning data that links at least one attribute with a future event that will occur to a target user having the attribute.

[0192] "Predicting future events that will occur to the target user based on the life plan created by the target user" A life plan is a life plan / prediction created by the target user himself / herself, and indicates what events will occur at what age. For example, a life plan may indicate marriage at age 30, childbirth at age 32, etc. The life plan of the target user is stored in advance in a storage device. The storage device may be provided in the processing device 10, or may be provided in an external device accessible from the processing device 10.

[0193] The acquisition unit 11 can predict future events that will occur to the target user based on such a life plan of the target user and the current age of the target user.

[0194] “Predicting future events that will occur to a target user based on the target user's past behavioral patterns” There are behavior patterns of people who have a certain event coming up soon or who may have a certain event occur in the near future. For example, an event called "birth" may occur for a person who has a behavior pattern of "visiting an obstetrician-gynecologist." The acquisition unit 11 can predict future events that will occur to the target user based on the relationship between such events and behavior patterns.

[0195] An estimation model for predicting future events based on the past behavioral patterns of a target user is generated in advance. The acquisition unit 11 uses this estimation model to predict future events that will occur to a target user who has performed a predetermined behavioral pattern in the past. The estimation model may be generated by machine learning. In this case, the estimation model is generated by learning based on learning data that links at least one behavioral pattern with a future event that will occur to a person who performed the behavioral pattern.

[0196] The target user's past behavioral patterns can be identified based on the target user's lifestyle pattern information and facility usage characteristics.

[0197] The acquisition unit 11 may predict future events that will occur to the target user based on behavioral patterns that occurred within all past periods. In addition, the acquisition unit 11 may predict future events that will occur to the target user based on behavioral patterns that occurred within a certain period of time in the past. The certain period is, for example, several months to several years, but is not limited thereto.

[0198] The acquisition unit 11 predicts future events that will occur to the target user, and then generates future life pattern information related to facilities that the target user will use in the future, based on the predicted events.

[0199] The "future lifestyle pattern information" indicates the types of facilities that the target user is likely to use in the future.

[0200] Facility information for each event that links an event with a facility used by a person in whom the event occurred is generated in advance and stored in a storage device. The storage device may be provided in the processing device 10, or may be provided in an external device accessible from the processing device 10. Then, the acquisition unit 11 identifies the type of facility that the target user may use in the future, based on the facility information for each event and the prediction result of a future event that will occur to the target user.

[0201] The identification unit 12 identifies the facility usage characteristics of the target user based on the future life pattern information. The identification unit 12 identifies the tendency of facility usage characteristics when a person having the same attributes as the target user uses various facilities by using the method described in the second embodiment. The various facilities are types of facilities that the target user may use in the future.

[0202] Next, an example of the process flow of the processing device 10 will be described with reference to the flowchart of Fig. 10. Note that the purpose here is to explain the process flow. Since the details of each process have been described above, the description here will be omitted.

[0203] In S40, the processing device 10 acquires user identification information of the target user and information indicating the candidate residence area.

[0204] For example, only target users who have registered as members in advance can use the services provided by the processing device 10. By registering as a member, the target users obtain information (user identification information, login password, etc.) for logging in to the processing device 10. Then, the target users who wish to use the services provided by the processing device 10 log in to the processing device 10 using the login information. In S40, the processing device 10 can obtain the user identification information of the target users entered at the time of this login.

[0205] Then, the processing device 10 accepts input of information indicating at least one candidate residence area (designation of the candidate residence area) from the target user on the screen after login. The processing device 10 can accept the designation of at least one candidate residence area based on at least one of the first to third designation methods described in the second embodiment.

[0206] In S41, the processing device 10 acquires life pattern information and future life pattern information of the target user.

[0207] For example, life pattern information and future life pattern information for each of a plurality of target users are generated in advance and stored in a storage device. The storage device may be provided in the processing device 10, or may be provided in an external device accessible from the processing device 10. Then, in S41, the processing device 10 acquires, from the storage device, life pattern information and future life pattern information of the target user identified by the user identification information acquired in S40.

[0208] As a modified example, in S41, the processing device 10 may generate future life pattern information of the target user identified by the user identification information acquired in S40. In this way, the future life pattern information may be generated each time, rather than being generated in advance.

[0209] In S42, the processing device 10 identifies the facility utilization characteristics of the target user based on the life pattern information and future life pattern information acquired in S41.

[0210] As a modified example, the facility usage characteristics of each of the multiple target users may be specified in advance based on the life pattern information and future life pattern information of each of the multiple target users, and stored in a storage device. The storage device may be provided in the processing device 10, or in an external device accessible from the processing device 10. Then, in S42, the processing device 10 may acquire the facility usage characteristics of the target user identified by the user identification information acquired in S40 from the storage device. In the case of this modified example, S41 may not be necessary. That is, the processing device 10 may perform S42 after S40 without performing S41.

[0211] In S43, the processing device 10 estimates facilities that the target user may use in the potential residence area indicated by the information acquired in S40, based on the facility usage characteristics acquired in S42 and the map information.

[0212] In S44, the processing device 10 outputs the estimation result of S43.

[0213] Other configurations of the processing apparatus 10 of this embodiment are similar to those of the processing apparatus 10 of the first to fourth embodiments.

[0214] The processing device 10 of this embodiment achieves the same effects as the processing device 10 of the first to fourth embodiments. The processing device 10 of this embodiment can predict future events that will occur to the target user, and estimate facilities that the target user may use in the candidate residence area based on the prediction results. Such a processing device 10 can reduce oversights when checking the environment of the candidate residence area.

[0215] <<Sixth embodiment>> The processing device 10 of the present embodiment evaluates a potential residence area based on the estimation results of facilities that the target user may possibly use in the potential residence area, etc. This will be described in detail below.

[0216] 11 shows an example of a functional block diagram of the processing device 10. As shown in the figure, the processing device 10 has an acquisition unit 11, a specification unit 12, an estimation unit 13, an output unit 14, and an evaluation unit 16. The processing device 10 may also have an additional information provision unit 15.

[0217] The evaluation unit 16 evaluates the candidate residence area based on the estimation result of facilities that the target user may potentially use in the candidate residence area.

[0218] The evaluation unit 16 can evaluate the candidate residence area based on at least one item value related to the candidate residence area. An evaluation model is generated in advance, in which at least one item value is input and an evaluation value is output. The evaluation unit 16 can calculate an evaluation value of the candidate residence area based on the evaluation model. The evaluation model may be an arithmetic formula, a learning model generated by machine learning, a table showing the relationship between at least one item value and the evaluation value, or other.

[0219] For example, the evaluation unit 16 converts each item value into an item evaluation value, which is a common index, based on a conversion rule previously determined for each item value. The item evaluation value is, for example, a value in the range of 0 to 100, but is not limited to this. The evaluation unit 16 then calculates an evaluation value of the candidate residence area based on the multiple item evaluation values. For example, the evaluation unit 16 can calculate a statistical value of the multiple item evaluation values ​​as the evaluation value of the candidate residence area. The statistical value is, for example, an average value, a mode value, a median value, a maximum value, a minimum value, etc., but is not limited to these.

[0220] Note that the weighting value for each item value may be determined in advance and stored in a storage device. The storage device may be provided in the processing device 10, or in an external device accessible from the processing device 10. The evaluation unit 16 may evaluate the candidate residence area based on the weighting value for each item value. For example, the above evaluation model is designed so that the influence of an item value with a high weighting value on the evaluation value is smaller than the influence of an item value with a low weighting value on the evaluation value. In one example, the evaluation unit 16 calculates a weighted average of the evaluation values ​​for each item as the evaluation value of the candidate residence area. Then, the weight in the weighted average is a value according to the weighting value for each item value.

[0221] Also, a weighting value for each type of facility may be determined in advance and stored in a storage device. The storage device may be provided in the processing device 10 or in an external device accessible from the processing device 10. The evaluation unit 16 may evaluate the candidate residence area based on the weighting value for each available facility. For example, the evaluation model is designed so that the influence of an item value related to an available facility with a high weighting value on the evaluation value is smaller than the influence of an item value related to an available facility with a low weighting value on the evaluation value. In one example, the evaluation unit 16 calculates a weighted average of the evaluation values ​​for each of a plurality of items as the evaluation value of the candidate residence area. Then, the weight in the weighted average is a value according to the weighting value for each available facility.

[0222] The item value includes at least one of the following. In addition, the causal relationship between each item value and the evaluation value / item-specific evaluation value in the above evaluation model is shown. -Distance from the proposed residence location to the available facilities (the smaller the distance, the higher the evaluation value / item-specific evaluation value) Travel time from the proposed residence location to the available facility (the shorter the travel time, the higher the evaluation value / evaluation value for each item) Whether the facility is open during the time period when the various facilities shown in the facility usage characteristics are used (if the facility is open during that time period, the evaluation value / item-specific evaluation value will be higher than if it is not open) - Distance from other facilities to usable facilities that are indicated to be used in combination with other types of facilities in the facility usage characteristics (the smaller the distance, the higher the evaluation value / item-specific evaluation value) - Travel time from other facilities to usability facilities that are indicated to be used in combination with other types of facilities in the facility usage characteristics (the shorter the travel time, the higher the evaluation value / item-specific evaluation value) · Availability of facilities where a car is indicated as a means of transportation in the facility usage characteristics Availability of parking lots at or near the facility (if there is a parking lot, the evaluation value / item-specific evaluation value will be higher than if there is no parking lot) Characteristics of the travel route from the proposed residential location to the available facility (evaluation value / item-specific evaluation value will be higher if the characteristics meet certain conditions) Number of available facilities (the higher the number, the higher the evaluation value / evaluation value for each item) The ratio of the number of facilities that can be used to the number of types of facilities whose usage by the target user, as indicated by the facility usage characteristics, satisfies the specified conditions (the higher the ratio, the higher the evaluation value / item-specific evaluation value)

[0223] The evaluation unit 16 can calculate the distance from the residence candidate point to the available facility based on the map information. The residence candidate point is specified by the target user. The method of specification is the same as the first specification method described in the second embodiment.

[0224] The evaluation unit 16 can also calculate the travel time from the proposed residence point to the available facility based on the map information. The travel time can be calculated using any well-known technology. For example, the travel time is calculated in a navigation technology that provides route guidance from a starting point to a destination point.

[0225] The evaluation unit 16 can also determine whether the available facility is open during a given time period based on facility information of each facility that is stored in advance in a storage device. The facility information of each facility indicates the opening hours. The storage device may be provided in the processing device 10 or in an external device that can be accessed by the processing device 10.

[0226] The evaluation unit 16 can also calculate the distance between two available facilities to be used in combination based on map information. The estimation unit 13 can also calculate the travel time between the two available facilities.

[0227] The evaluation unit 16 can also determine whether the available facility has a parking lot based on the facility information of each facility stored in advance in a storage device. The facility information of each facility indicates whether or not the facility has a parking lot. The storage device may be provided in the processing device 10, or in an external device accessible from the processing device 10.

[0228] Furthermore, the evaluation unit 16 can determine whether there is a parking lot around the available facility based on map information. The "around the available facility" is, for example, an area within a predetermined distance from the available facility, but is not limited to this.

[0229] Furthermore, the evaluation unit 16 can identify the characteristics of the travel route from the candidate residence point to the available facility based on the map information. The characteristics of the travel route include safety, pavement condition, presence or absence of slopes, presence or absence of stairs, road width, barrier-free, etc. The evaluation value is high when the characteristics satisfy a predetermined condition. The predetermined condition is determined in advance, and examples include safety above a predetermined level, paved, no slopes, no stairs, road width above a threshold, barrier-free, etc.

[0230] The evaluation unit 16 can also count the number of available facilities.

[0231] The evaluation unit 16 can also calculate the ratio of the number of available facilities to the number of types of facilities whose usage by the target user, indicated by the facility usage characteristics, satisfies a predetermined condition. The ratio indicates what percentage of the number of types of facilities whose usage by the target user satisfies a predetermined condition are present in the candidate residence area.

[0232] The predetermined condition is at least one of the following: -Indicates use / will use - The number of uses is above the threshold - Frequency of use is above the threshold

[0233] When multiple candidate residential areas are specified, the evaluation unit 16 can extract a candidate residential area whose evaluation result satisfies a selection condition from among the multiple candidate residential areas. The selection condition may be, but is not limited to, "an evaluation value equal to or greater than a threshold," "the highest evaluation value among the multiple candidate residential areas," or "a predetermined rank or higher in the order in which the multiple candidate residential areas are arranged in descending order of evaluation value."

[0234] The output unit 14 outputs the evaluation result by the evaluation unit 16. An example of a method for outputting the evaluation result will be described below, but the method is not limited to this example.

[0235] For example, as shown in FIG. 12, the output unit 14 can output a screen in which at least one proposed residence area is distinguishably displayed on a map in a display mode according to the evaluation result.

[0236] In FIG. 12, three candidate residential areas A to C are shown. Each candidate residential area is indicated by a circular frame. An evaluation value for each candidate residential area is also shown in association with the candidate residential area. The evaluation value for candidate residential area A is 95, the evaluation value for candidate residential area B is 68, and the evaluation value for candidate residential area C is 59. The candidate residential area A, whose evaluation result satisfies the selection condition, is highlighted. In the example shown in the figure, the selection condition is "evaluation value 80 or more."

[0237] Next, an example of the process flow of the processing device 10 will be described with reference to the flowchart of Fig. 13. Note that the purpose here is to explain the process flow. Since the details of each process have been described above, the description here will be omitted.

[0238] In S50, the processing device 10 acquires user identification information of the target user and information indicating the candidate residence area.

[0239] For example, only target users who have registered as members in advance can use the services provided by the processing device 10. By registering as a member, the target users obtain information (user identification information, login password, etc.) for logging in to the processing device 10. Then, the target users who wish to use the services provided by the processing device 10 log in to the processing device 10 using the login information. In S50, the processing device 10 can obtain the user identification information of the target users entered at the time of this login.

[0240] Then, the processing device 10 accepts input of information indicating at least one candidate residence area (designation of the candidate residence area) from the target user on the screen after login. The processing device 10 can accept the designation of at least one candidate residence area based on at least one of the first to third designation methods described in the second embodiment.

[0241] In S51, the processing device 10 acquires life pattern information of the target user.

[0242] For example, lifestyle pattern information for each of a plurality of target users is generated in advance and stored in a storage device. The storage device may be provided in the processing device 10, or may be provided in an external device accessible from the processing device 10. Then, in S51, the processing device 10 acquires, from the storage device, the lifestyle pattern information of the target user identified by the user identification information acquired in S50.

[0243] In S52, the processing device 10 identifies the facility usage characteristics of the target user based on the life pattern information acquired in S51.

[0244] As a modified example, the facility usage characteristics of each of the multiple target users may be specified in advance based on the lifestyle pattern information of each of the multiple target users and stored in a storage device. The storage device may be provided in the processing device 10, or in an external device accessible from the processing device 10. Then, in S52, the processing device 10 may acquire the facility usage characteristics of the target user identified by the user identification information acquired in S50 from the storage device. In the case of this modified example, S51 may not be necessary. That is, the processing device 10 may perform S52 after S50 without performing S51.

[0245] In S53, the processing device 10 estimates facilities that the target user may use in the potential residence area indicated by the information acquired in S50, based on the facility usage characteristics acquired in S52 and the map information.

[0246] In S54, the processing device 10 evaluates each of at least one potential residential area based on the estimation result in S53.

[0247] In S55, the processing device 10 outputs the evaluation result of S54. The processing device 10 may further output the estimation result of S53.

[0248] Other configurations of the processing apparatus 10 of this embodiment are similar to those of the processing apparatus 10 of the first to fifth embodiments.

[0249] According to the processing device 10 of this embodiment, the same action and effect as the processing device 10 of the first to fifth embodiments is realized. In addition, the processing device 10 of this embodiment can evaluate a candidate residence area based on the estimation result of facilities that the target user may use in the candidate residence area. The target user can evaluate the candidate residence area based on this evaluation result.

[0250] <<Modifications>> In the output mode of Fig. 12 described in the sixth embodiment, all candidate residential areas designated by the user are displayed on a map, and the evaluation results of each are shown. As a modified example, the processing device 10 may display only the candidate residential areas that satisfy the selection conditions. That is, in the example of Fig. 12, only the candidate residential area A that satisfies the selection conditions may be displayed on the map, and the candidate residential areas B and C that do not satisfy the selection conditions may not be displayed on the map.

[0251] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-mentioned embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be appropriately combined with other embodiments.

[0252] In addition, in the flowcharts used in the above description, multiple steps (processes) are described in order. However, the order of the steps performed in each embodiment is not limited to the order described. In each embodiment, the order of the steps shown in the drawings can be changed as long as it does not affect the content.

[0253] A part or all of the above-described embodiments can be described as, but is not limited to, the following supplementary notes. 1. A means for acquiring lifestyle pattern information related to facilities used by a target user; An identification means for identifying facility usage characteristics of the target user based on the life pattern information; an estimation means for estimating facilities that the target user may use in the potential residence area based on the facility usage characteristics and map information; A processing device having 2. The acquiring means is Location information of the target user; Surveillance camera images, Data provided by the facility to the target user who uses the facility; Information regarding payments at the facility; Visitor / user lists recording those who either visit or use the facility; and Attribute information of the target user; The processing device according to claim 1, which generates the lifestyle pattern information based on at least one of the above. 3. The identification means determines, as the facility usage characteristics of the target user, The type of facility used Number of times various facilities are used, Frequency of use of various facilities, Hours when various facilities can be used, Combinations of various facilities that can be used in combination, Tendencies in the order of use of various facilities in combination; Transportation when using various facilities, Information regarding routes taken when using various facilities, and Tendencies of facility usage characteristics when a person having the same attributes as the target user uses various facilities; 3. The processing device according to claim 1 or 2, which specifies at least one of the above. 4. The estimation means estimates, as facilities that the target user may use in the potential residence area, Facilities that are present in the residence candidate area and are of the same type as the facilities that have been used as indicated in the facility use characteristics; Facilities of the same type as the facilities that are present in the residence candidate area and that are indicated to have been used a predetermined number of times or more in the facility use characteristics; Facilities of the same type as the facility that exists in the residence candidate area and is indicated to have been used at a frequency equal to or higher than a predetermined level in the facility use characteristics; and Facilities of the same type as the type of facilities that are present in the candidate residence area and that are indicated to be tended to be used by persons having the attributes of the target user in the facility usage characteristics; 4. A processing device according to any one of 1 to 3, which specifies at least one of the following: 5. The acquisition means predicts future events that will occur to the target user, and acquires future life pattern information related to facilities to be used by the target user in the future based on the predicted events; 5. The processing device according to any one of 1 to 4, wherein the specification means specifies a facility usage characteristic of the target user further based on the future life pattern information. 6. The acquiring means acquires the lifestyle pattern information of each of the target users who plan to live together in the candidate residence area; The identification means identifies the facility usage characteristics of each of the plurality of target users, The processing device according to any one of 1 to 5, wherein the estimation means estimates facilities that are likely to be used by multiple target users in the potential residence area based on the facility usage characteristics of each of the multiple target users. 7. A processing device according to any one of 1 to 6, comprising an evaluation means for evaluating the candidate residence area based on estimation results of facilities that the target user may use in the candidate residence area. 8. The processing device according to 7, wherein the evaluation means extracts the candidate residence areas whose evaluation results satisfy a selection condition from among the plurality of candidate residence areas. 9. The lifestyle pattern information is The facility usage history of the target user; and The type of facility that a person having the attributes of the target user tends to use; 9. A processing device according to any one of 1 to 8, which exhibits at least one of the above. 10. The estimation means further estimates, as facilities that the target user may use in the potential residence area, The distance from the potential residence point is less than the threshold, The travel time from the proposed residence point is less than the threshold value, The facility is open during the hours when the various facilities shown in the facility usage characteristics are used. For a facility type that is indicated to be used in combination with other types of facilities in the facility use characteristics, the distance from the other types of facilities is equal to or less than a threshold value; For a facility type that is indicated to be used in combination with other types of facilities in the facility usage characteristics, the travel time from the other types of facilities is equal to or less than a threshold value; For the type of facility where the facility usage characteristics indicate that a car is a means of transportation, there is a parking lot at the facility or in its vicinity; and The travel route from the proposed residence location meets the conditions of use, 5. A processing device according to claim 4, which identifies a facility that satisfies at least one of the above. 11. The acquiring means Attribute information of the target user; A life plan created by the target user; and The target user's past behavioral patterns; 6. The processing device according to claim 5, further comprising: a processor for predicting future events that may occur to the target user based on at least one of the above. 12. The estimation means estimates, as facilities that the target users may use in the potential residence area, A facility that is present in the candidate residence area and is estimated as a facility that is likely to be used by at least one of the plurality of target users; A facility that is present in the candidate residence area and is estimated as a facility that is likely to be used by all of the plurality of target users; A facility that is present in the candidate residence area and is estimated as a facility that is likely to be used by a predetermined ratio or more of the plurality of target users; 7. A processing device according to claim 6, for estimating at least one of the following: 13. The estimation means weighting the plurality of target users; 13. The processing device according to claim 6 or 12, which estimates facilities that are likely to be used by the plurality of target users in the potential residence area by using weighting values ​​for each of the plurality of target users. 14. The processing device according to claim 13, wherein the estimation means performs the weighting based on the relationships between the plurality of target users and attribute information of each of the plurality of target users. 15. A processing device described in any one of 1 to 14, further comprising an additional information providing means for calculating a distance between a facility in the potential residence area that the target user may use and a potential residence point, and generating information relating to the distance. 16. The additional information providing means may provide the following as the information relating to the distance: Information on a map that identifies a location that is distant from the target user's current residence location, and Information indicating a facility located at a location away from the target user's current residence location; 15. A processing device according to claim 15, which generates at least one of 17. The evaluation means comprises: A distance from the residence candidate point to a usable facility estimated as a facility that the target user may use in the residence candidate area; Travel time from the proposed residence location to the available facility; Whether the available facility is open during the time period when the various facilities indicated by the facility usage characteristics are used; the distance from other facilities in the availability facility that are indicated in the facility use characteristics to be used in combination with other types of facilities; Travel times from other facilities in the availability facility that are indicated in the facility usage characteristics to be used in combination with other types of facilities; The presence or absence of parking lots at or near the availability facility where a car is indicated as a means of transportation in the facility usage characteristics; Characteristics of a travel route from a potential residence location to the available facility; The number of available facilities; and a ratio of the number of the available facilities to the number of types of facilities whose usage status of the target user, indicated by the facility usage characteristics, satisfies a predetermined condition; 8. The processing device according to claim 7, which evaluates the potential residential area based on at least one of the items above. 18. The evaluation means comprises: 17. The processing device according to claim 17, which evaluates the potential residence areas based on a weighting value for each of the item values. 19. The evaluation means comprises: 17. A processing device according to claim 17, which evaluates the potential residential area based on a weighting value for each of the available facilities. 20. A processing device as described in 7, having an output means for outputting a screen on which at least one of the candidate residential areas is identified and displayed on a map in a display mode corresponding to the evaluation result. 21. A processing device according to any one of 1 to 20, wherein the candidate residence area is different from the current residence area. 22. One or more computers: Acquire information on the lifestyle patterns of the target users related to the facilities they use, Identifying facility usage characteristics of the target user based on the life pattern information; A processing method for estimating facilities that the target user may use in a potential residence area based on the facility usage characteristics and map information. 23. Computers, An acquisition means for acquiring life pattern information related to facilities used by a target user; An identification means for identifying facility usage characteristics of the target user based on the life pattern information; an estimation means for estimating facilities that the target user may use in the potential residence area based on the facility usage characteristics and map information; A program that functions as a

[0254] Some or all of Appendices 2 to 21 that are dependent on the processing device of Appendix 1 described above may also be dependent on the processing method of Appendix 22 and the program of Appendix 23 in a similar dependent relationship to Appendix 1 and Appendices 2 to 21. Furthermore, within the scope of each of the above-mentioned embodiments, some or all of the configurations described as appendices may be realized in various hardware, software, various recording means for recording software, or systems. [Explanation of symbols]

[0255] 10 Processing equipment 11 Acquisition Department 12 Specific section 13 Estimation part 14 Output section 15 Additional Information Section 16 Evaluation Section 1A Processor 2A Memory 3A input / output I / F 4A Peripheral circuit 5A Bus

Claims

1. An acquisition means for acquiring life pattern information related to facilities used by a target user; An identification means for identifying facility usage characteristics of the target user based on the life pattern information; an estimation means for estimating facilities that the target user may use in the potential residence area based on the facility usage characteristics and map information; A processing device having

2. The acquisition means includes: Location information of the target user; Surveillance camera images, Data provided by the facility to the target user who uses the facility; Information regarding payments at the facility; Visitor / user lists recording those who either visit or use the facility; and Attribute information of the target user; The processing device according to claim 1 , wherein the life pattern information is generated based on at least one of the following:

3. The identification means is configured to identify the facility usage characteristics of the target user as follows: The type of facility used Number of times various facilities are used, Frequency of use of various facilities, Hours when various facilities can be used, Combinations of various facilities that can be used in combination, Tendencies in the order of use of various facilities in combination; Transportation when using various facilities, Information regarding routes taken when using various facilities, and Tendencies of facility usage characteristics when a person having the same attributes as the target user uses various facilities; The processing device of claim 1 , wherein the processing device specifies at least one of:

4. The estimation means is configured to estimate facilities that the target user may use in the potential residence area, Facilities that are present in the residence candidate area and are of the same type as the facilities that have been used as indicated in the facility use characteristics; Facilities of the same type as the facilities that are present in the residence candidate area and that are indicated to have been used a predetermined number of times or more in the facility use characteristics; Facilities of the same type as the facility that exists in the residence candidate area and is indicated to have been used at a frequency equal to or higher than a predetermined level in the facility use characteristics; and Facilities of the same type as the type of facilities that are present in the candidate residence area and that are indicated to be tended to be used by persons having the attributes of the target user in the facility usage characteristics; The processing device of claim 1 , wherein the processing device specifies at least one of:

5. the acquisition means predicts a future event that will occur to the target user, and acquires future life pattern information related to facilities to be used by the target user in the future based on the predicted event; The processing device according to claim 1 , wherein the specifying means specifies a facility usage characteristic of the target user further based on the future life pattern information.

6. The acquisition means acquires the lifestyle pattern information of each of the target users who plan to live together in the candidate residence area, The identification means identifies the facility usage characteristics of each of the plurality of target users, The processing device according to claim 1 , wherein the estimation means estimates facilities that are likely to be used by a plurality of the target users in the potential residence area, based on the facility usage characteristics of each of the plurality of the target users.

7. The processing device according to claim 1 , further comprising an evaluation unit that evaluates the potential residence area based on an estimation result of facilities that the target user is likely to use in the potential residence area.

8. The processing device according to claim 7 , wherein the evaluation means extracts the candidate residence areas whose evaluation results satisfy a selection condition from among the plurality of candidate residence areas.

9. One or more computers Acquire information on the lifestyle patterns of the target users related to the facilities they use, Identifying facility usage characteristics of the target user based on the life pattern information; A processing method for estimating facilities that the target user may use in a potential residence area based on the facility usage characteristics and map information.

10. Computer, An acquisition means for acquiring life pattern information related to facilities used by a target user; An identification means for identifying facility usage characteristics of the target user based on the life pattern information; an estimation means for estimating facilities that the target user may use in the potential residence area based on the facility usage characteristics and map information; A program that functions as a

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