Program, information processing method, and information processing apparatus.

Abstract

To provide a program that outputs new store information generated by a language model along with already generated store information. [Solution] The program acquires property information that is a candidate for store development, provides the acquired property information to a language model, generates candidate store information by extracting item data for each item, and causes the computer to perform a process of outputting the newly generated store information together with the already generated store information group. Preferably, the program outputs confirmation information indicating that the item data is being checked in association with the new store information.

Description

[Technical Field]

[0001] This disclosure relates to a program, an information processing method, and an information processing device. [Background technology]

[0002] Patent Document 1 discloses an information processing method that supports real estate transaction brokerage services by reducing the effort required to change the labels on real estate property documents. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2025-31505 [Overview of the project] [Problems that the invention aims to solve]

[0004] However, the invention described in Patent Document 1 has the problem that it is not possible to output new store information generated by the language model together with the already generated group of store information.

[0005] One aspect of this project is to provide a program that outputs new store information generated by a language model, along with already generated store information. [Means for solving the problem]

[0006] One aspect of the program involves acquiring property information that could be used as a candidate for store development, and by providing this property information to a language model, it generates candidate store information by extracting item data for each item. Based on the accuracy information stored for each provider of acquired property information, the system determines whether or not to output "under review" information indicating that the item data is being verified. If it is determined that "under review" information should be output, the newly generated store information and the "under review" information are output together with the already generated store information. Have the computer perform the process. [Effects of the Invention]

[0007] One aspect of this is that it becomes possible to output new store information generated by the language model together with the already generated store information set.

Brief Description of the Drawings

[0008] [Figure 1] It is an explanatory diagram for explaining the configuration of an information processing system. [Figure 2] It is a block diagram showing a configuration example of a server and a computer. [Figure 3] It is an explanatory diagram showing an example of a record layout of an accuracy information DB. [Figure 4] It is an explanatory diagram showing an example of a record layout of a confirmation DB. [Figure 5] It is an explanatory diagram showing an example of a record layout of a store information DB. [Figure 6] It is an explanatory diagram showing an example of a generated prompt. [Figure 7] It is an explanatory diagram showing an example of a screen example. [Figure 8] It is an explanatory diagram showing an example of a screen example. [Figure 9] It is a flowchart showing the processing procedure of an information processing system. [Figure 10] It is a flowchart showing the processing procedure of an information processing system. [Figure 11] It is an explanatory diagram showing an example of a record layout of a store information DB according to Embodiment 2. [Figure 12] It is an explanatory diagram showing an example of a record layout of a settlement DB, a card DB, and a franchise store DB. [Figure 13] It is an explanatory diagram showing an example of a record layout of a people flow information DB and a road section DB. [Figure 14] It is an explanatory diagram showing an example of a screen example. [Figure 15] It is a flowchart showing the processing procedure of an information processing system according to Embodiment 2. [Figure 16] It is a flowchart showing the processing procedure of a subroutine related to the generation processing of people flow information in a corresponding area. [Figure 17]It is a flowchart showing the processing procedure of a subroutine related to the generation process of the settlement population, etc. in the corresponding area. [Figure 18] It is a flowchart showing the processing procedure of a subroutine related to the generation process of a comparison graph. [Figure 19] It is an explanatory diagram showing an example of a screen example. [Figure 20] It is a flowchart showing the processing procedure of the information processing system according to Embodiment 3. [Figure 21] It is a flowchart showing the processing procedure of a subroutine related to the specific process of the store opening score.

Mode for Carrying Out the Invention

[0009] (Embodiment 1) FIG. 1 is an explanatory diagram for explaining the configuration of an information processing system. The information processing system displays new store information generated by a language model together with a group of already generated store information. The information processing system includes an information processing device 10 and one or more information processing devices 20. The information processing device 10 and the information processing device 20 transmit and receive information via a network N.

[0010] The information processing device 10 is an information processing device that performs processing, storage, and transmission and reception of various information. The information processing device 10 is, for example, a server device, a personal computer, or a general-purpose tablet PC (personal computer), etc. Note that the information processing device 10 may be a cloud server device that provides the functions included in the information processing device 10 as a cloud service. In the present embodiment, the information processing device 10 is described as the server 10.

[0011] The information processing device 20 is, for example, a server device, a smartphone, a tablet, a personal computer (hereinafter referred to as a computer), or a general-purpose tablet PC, etc. In the present embodiment, the information processing device 20 is described as the computer 20.

[0012] Figure 2 is a block diagram showing an example configuration of a server and a computer. The server 10 includes a control unit 11, a storage unit 12, a communication unit 13, a large-capacity storage unit 14, and a read unit 15. Each of the above-mentioned units is interconnected via a bus. The control unit 11 is configured using one or more processors such as a CPU (Central Processing Unit), an MPU (Micro-Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-purpose computing on graphics processing units), a TPU (Tensor Processing Unit), or an AI chip (AI semiconductor). The control unit 11 performs various information processing and control processing related to the server 10 by appropriately executing a control program 12P (program product) stored in the storage unit 12.

[0013] The storage unit 12 includes RAM (Random Access Memory) or ROM (Read Only Memory), etc. The storage unit 12 stores various data necessary for the control program 12P executed by the control unit 11. The storage unit 12 temporarily stores data generated when the control unit 11 executes the control program 12P.

[0014] Furthermore, the memory unit 12 stores the language model 12M. The language model 12M is a general-purpose large language model (LLM: Large Language Model) constructed by performing unsupervised pre-training using, for example, a large set of texts and images. Examples of language models 12M include GPT (Generative Pre-trained Transformer)-3, GPT-3.5, GPT-4, RWKV (Receptance Weighted Key Value), PaLM2, or LLaMa (Large Language Model Meta AI).

[0015] When the language model 12M receives input such as property information for potential store development, it performs calculations to generate candidate store information by extracting item data for each item, and outputs the generated store information. Property information is, for example, information on potential stores for store development provided by a real estate agent, and includes at least one item of data such as store identification information (e.g., store ID and store number), store name, name of the company providing the store, store rent, information on initial store costs, store floor area, store price per tsubo, information on store access, store floor number, store contract type (e.g., fixed-term lease and regular lease), and floor plan. Hereafter, store identification information will be described as store ID.

[0016] Information regarding the initial costs of a store includes, for example, at least one of the store's security deposit, key money, and guarantee deposit. Information regarding the store's access includes, for example, at least one of the store's nearest train station and the walking time from the store to the nearest station. Property information has a different format depending on the provider. Store information is, for example, information that includes the same information as property information, and where the item data included in property information is standardized for each item.

[0017] The language model 12M may be constructed by combining multiple algorithms. Instead of storing the language model 12M in the memory unit 12, the control unit 11 may access an external server (not shown) that stores the language model 12M and use the language model 12M.

[0018] The communication unit 13 is a communication module that sends and receives information to and from the computer 20 via the network N. The large-capacity storage unit 14 includes RAM or ROM, etc. The large-capacity storage unit 14 stores accuracy information DB 141, confirmation information DB 142, store information DB 143, payment DB 144, card DB 145, merchant DB 146, pedestrian flow information DB 147, and road section DB 148. Accuracy information DB 141, confirmation information DB 142, and store information DB 143 will be described later. Payment DB 144, card DB 145, merchant DB 146, pedestrian flow information DB 147, and road section DB 148 will be described in Embodiment 2 and later.

[0019] In this embodiment, the storage unit 12 and the large-capacity storage unit 14 may be configured as a single storage device. The large-capacity storage unit 14 may be composed of multiple storage devices. The large-capacity storage unit 14 may also be an external storage device connected to the server 10.

[0020] The reading unit 15 reads information stored in the portable storage medium 1a. The portable storage medium 1a is, for example, a CD (Compact Disc), DVD (Digital Versatile Disc), USB (Universal Serial Bus) memory, or SD (Secure Digital). The reading unit 15 reads the control program 12P from the portable storage medium 1a. The control unit 11 stores the read control program 12P in the storage unit 12.

[0021] The control unit 11 may download the control program 12P from another computer via the network N. In that case, the control unit 11 stores the downloaded control program 12P in the storage unit 12. Alternatively, the control unit 11 may store the read control program 12P in the large-capacity storage unit 14.

[0022] In this embodiment, server 10 may be composed of multiple servers. Server 10 may be a virtual machine virtually constructed by software within a single device. Server 10 may be a local server installed in the facility where server 10 is located. Server 10 may be a cloud server connected via network N. Furthermore, the control program 12P may run on a single server, or it may be distributed and run on multiple servers interconnected via network N.

[0023] The computer 20 includes a control unit 21, a storage unit 22, a communication unit 23, a display unit 24, and an input unit 25. The above-mentioned units are interconnected via a bus. The control unit 21 is configured using one or more processors such as a CPU, MPU, or GPU. The storage unit 22 includes RAM or ROM. The storage unit 22 stores various data necessary for the control program 22P (program product) executed by the control unit 21. The storage unit 22 temporarily stores data generated when the control program 22P is executed. The control unit 21 performs information processing and control processing related to the computer 20 by appropriately executing the control program 22P stored in the storage unit 22.

[0024] The communication unit 23 is a communication module that sends and receives information to and from the server 10 via the network N. The input unit 25 is, for example, an input interface such as a mouse and a keyboard. The display unit 24 is, for example, a liquid crystal panel or an organic EL (electro-luminescence) panel.

[0025] Figure 3 is an explanatory diagram showing an example of the record layout of the accuracy information database. The accuracy information database 141 stores accuracy information for each provider of property information. The accuracy information is stored in association with the provider's email address or the domain name included in the provider's website URL. The following section will explain an example in which the accuracy information is stored in association with the provider's email address.

[0026] Accuracy information is an indicator of the accuracy and reliability of the property information provider, and is shown as "accurate" or "inaccurate" from 0% to 100%, or as "high," "medium," or "low," etc. When accuracy information is shown from 0% to 100%, the closer the accuracy information is to 100%, the higher the accuracy and reliability of the property information provider, and the closer the accuracy information is to 0%, the lower the accuracy and reliability of the property information provider.

[0027] When accuracy information is indicated as "accurate" or "inaccurate," a high accuracy rating indicates high accuracy and reliability of the property information provider, while a low accuracy rating indicates low accuracy and reliability. When accuracy information is indicated as "high," "medium," or "low," a high accuracy rating indicates high accuracy and reliability of the property information provider, a medium accuracy rating indicates average accuracy and reliability, and a low accuracy rating indicates low accuracy and reliability. The following sections will explain examples where accuracy information is indicated from 0% to 100%.

[0028] The accuracy information is calculated by comparing the number of items in the training data, which contains all the item data included in the property information, with the number of items in the property information sent from the provider's email address. Specifically, the control unit 11 calculates the ratio between the number of items in the training data, which contains all the item data included in the property information, and the number of items in the property information for each store sent from the provider's email address.

[0029] The control unit 11 identifies the extraction success rate for each store by converting the calculated ratio into a percentage. Specifically, the control unit 11 identifies the accuracy information (from 0% to 100%) by calculating the average, median, or mode of the extraction success rate for each store.

[0030] Furthermore, the control unit 11 may determine the accuracy information by comparing the number of properties read from one property listing by the server 10 with the number of properties counted from the same property listing by the administrator of the information processing system. A property listing is a document containing information on multiple properties. Specifically, if the server 10 reads 4 properties and the administrator of the information processing system counts 5 properties, the control unit 11 may use the ratio of "0.8" between the number of properties read by the server 10 ("5") and the number of properties read by the administrator of the information processing system ("4") as the accuracy information. Moreover, the administrator of the information processing system may determine the accuracy information at their discretion without using the above method of determination.

[0031] The accuracy information DB141 includes columns for email address, provider name, and accuracy information. The email address column stores the email address of the provider of the property information. The provider name column stores the name of the provider company corresponding to the email address stored in the email address column. The accuracy information column stores accuracy information (from 0% to 100%). In Figure 3, the email address "info@○○○○.jp", provider name "A1 Real Estate", and accuracy information "75%" are stored in association.

[0032] The accuracy information DB141 may include a telephone number column and a URL (Uniform Resource Locator) column. In this case, the telephone number column stores the telephone number of the property information provider, and the URL column stores the URL of the property information provider. The control unit 11 may re-identify the accuracy information at predetermined time intervals (for example, every month). In this case, the control unit 11 updates the accuracy information stored in the accuracy information DB141 as appropriate by using the re-identified accuracy information.

[0033] Figure 4 is an explanatory diagram showing an example of the record layout of the verification DB. The verification information DB142 stores, for each store ID, verification information (hereinafter referred to as "verification information") indicating that the item data included in the newly generated store information is being verified, verified information (hereinafter referred to as "verified information") indicating that the item data included in the newly generated store information has been verified, or no verification information (hereinafter referred to as "no verification information") indicating that there is no need to verify the item data included in the newly generated store information. The verification information DB142 includes a store ID column, a provider name column, and a verification column. The provider name column is the same as in the accuracy information DB141.

[0034] The Store ID column stores the Store ID used to identify each record stored in the Confirmation Information DB142. The Confirmation column stores information that is being confirmed, confirmed, or not confirmed. The information stored in the Confirmation column changes depending on the accuracy information of the property information provider and whether or not the information processing system administrator has confirmed it. Specifically, if the accuracy information of the property information provider is 80% or higher, the Confirmation column stores information that is not confirmed (not confirmed), and if the accuracy information of the property information provider is less than 80%, the Confirmation column stores information that is being confirmed (being confirmed). For property information for which the Confirmation column stores information that is being confirmed (being confirmed), once the information processing system administrator has completed the confirmation, the Confirmation column stores information that is confirmed (confirmed).

[0035] The information stored in the confirmation information DB142 is updated as needed by the administrator of the information processing system. When the administrator completes the confirmation process for store information that is stored as "Under Confirmation" in the confirmation information DB142, the control unit 11 updates the record containing the store ID corresponding to the store information for which the confirmation process has been completed. Specifically, when the confirmation process for the store information corresponding to store ID "T001" is completed, the control unit 11 updates the confirmation column of the record containing store ID "T001" from "Under Confirmation" to "Confirmed".

[0036] Figure 5 is an explanatory diagram showing an example of the record layout of the store information database. The store information database 143 stores store information generated by the language model 12M, item by item. The store information database 143 includes columns for store ID, store name, provider name, date and time information, status, rent, price per square meter, floor area, location information, nearest station, access, contract type, floor plan, and remarks. The store ID column stores the store ID to identify the store information.

[0037] The store name column stores the store name, which is part of the store information. The provider name is the same as in the accuracy information DB141. The date and time information column stores the date and time when the target store information was added to the store information DB143. The status column stores the status indicating the selection status from when the target store information was provided until it was accepted or rejected. The status includes at least one of the following, for example, new, under consideration, application in progress, viewing in progress, negotiation under consideration, and accepted. The status is updated as appropriate by the administrator of the information processing system, etc., according to the status of the store information.

[0038] The rent column stores the rent of the property, which is part of the store information. The price per tsubo column stores the price per tsubo of the property, which is part of the store information. The floor area column stores the floor area of ​​the store, which is part of the store information. The location information column stores the location information of the store (e.g., address or latitude and longitude, etc.), which is part of the store information. The nearest station column stores the nearest station to the store. The access column stores the walking time from the nearest station to the store. The contract type column stores the contract type to the store. The floor plan column stores the floor plan, which is part of the store information. The floor plan is stored in image format (e.g., BMP, JPEG, or PNG, etc.) or file format (e.g., DXF or JWC, etc.). The remarks column stores information such as security deposit, key money, guarantee deposit, or simple memo.

[0039] The processing of Embodiment 1 will now be described. The control unit 11 receives emails or the like from property information providers such as real estate agents to obtain property information that is a candidate for store development, as well as the email address of the property information provider. The control unit 11 may also obtain property information that is a candidate for store development, as well as the telephone number of the property information provider, by receiving SMS (Social Networking Service) or RCS (Rich Communication Services) from real estate agents or the like. In that case, the accuracy information DB 141 stores the telephone number of the property information provider. The control unit 11 reads the email address corresponding to the acquired telephone number of the property information provider from the accuracy information DB 141.

[0040] The control unit 11 may extract property information presented in HTML format on the web pages of real estate agents, etc., to obtain property information that is a candidate for store development, as well as the URL of the property information provider. In that case, the accuracy information DB 141 stores the URL of the property information provider. The control unit 11 reads the email address corresponding to the acquired property information provider URL from the accuracy information DB 141.

[0041] The control unit 11 may obtain property information that is a candidate for store development, as well as the email address of the provider of the property information, by reading the paper media containing the property information using OCR (Optical Character Recognition) processing. In this embodiment, an example is described in which the control unit 11 obtains property information that is a candidate for store development, as well as the email address of the provider of the property information, by receiving emails sent from the provider of the property information, such as a real estate agent.

[0042] The control unit 11 generates a prompt 30 that includes property information and instructions for generating store information from the property information (hereinafter referred to as "generation instructions"). The generation instructions are pre-stored in the storage unit 12 or the large-capacity storage unit 14.

[0043] Figure 6 is an explanatory diagram showing an example of a generated prompt. Prompt 30 includes a property information field 31 and a generation instruction field 32. The property information field 31 contains property information received via email or other means from real estate agents, etc.

[0044] The above generation instructions are entered in the generation instruction field 32. In the generation instruction column 32 of Figure 6, "Extract item data from the above property information and generate store information." The data items to be extracted include, for example, "Store Name," "Rent," "Price per Square Meter," "Floor Area," "Location Information," "Nearest Station," "Access," "Contract Type," "Floor Plan," and "Remarks." It is stated.

[0045] The control unit 11 generates candidate store information by extracting item data for each item by providing the generated prompt 30 to the language model 12M. The control unit 11 reads the name of the provider of the property information from the accuracy information DB 141, which corresponds to the email address of the provider of the acquired property information. Specifically, if the email address of the provider of the acquired property information is "info@○○○○.jp", the control unit 11 reads the name of the provider, "A1 Real Estate", from the accuracy information DB 141.

[0046] The control unit 11 associates the status "New" and the name of the provider with the new store information. The control unit 11 stores the new store information, the status "New" associated with the new store information, and the name of the provider in the store information DB 143, associating them with the provider ID and date and time information. The control unit 11 reads the accuracy information corresponding to the acquired provider's email address from the accuracy information DB 141. Based on the read accuracy information, the control unit 11 decides whether or not to output the "Under Verification" information. Specifically, the control unit 11 decides whether or not the read accuracy information satisfies predetermined conditions. These predetermined conditions include, for example, the accuracy information being 80% or higher, the accuracy information being "accurate", or the accuracy information being "high" or "medium".

[0047] The control unit 11 determines to output "Under Verification" information if the read accuracy information does not meet predetermined conditions. The control unit 11 assigns "Under Verification" information to the new store information. When the control unit 11 determines to output "Under Verification" information, it stores the "Under Verification" information in the Verification Information DB 142, associating it with the store ID and the name of the provider. The control unit 11 may also notify the information processing device (e.g., a computer) of the administrator of the information processing system via the communication unit 13 of the corresponding new store information and an order to verify the new store information (hereinafter referred to as a verification order). After the administrator completes the verification work following the notification of the new store information and the verification order, the control unit 11 updates the record containing the store ID corresponding to the store information whose verification work has been completed from "Under Verification" to "Verified".

[0048] The control unit 11 determines not to output "under review" information if the read accuracy information meets predetermined conditions. If the control unit 11 determines not to output "under review" information, it stores the "not reviewed" information in the "review not available" database 142, associating it with the store ID and the name of the provider. If there are multiple new store entries, the control unit 11 repeats the above process for all of the new store entries.

[0049] In this embodiment, an example is shown in which the control unit 11 decides not to output the "checking" information if the read accuracy information satisfies predetermined conditions, but it is not limited to this. Even if the read accuracy information satisfies predetermined conditions, the control unit 11 may decide to output the "checking" information if at least one of the item data included in the newly generated store information is missing. In that case, the control unit 11 notifies the information processing device of the administrator of the information processing system of the corresponding new store information and a confirmation command via the communication unit 13.

[0050] In the above case, the generation instruction field 32 of the prompt 30 will contain, in addition to the contents shown in Figure 6, "If at least one item of data extracted from the property information is missing, identify the missing item and respond with 'Item data does not exist.'" The following will be added.

[0051] The control unit 11 stores the information being verified or the information not being verified in the verification information DB 142, and then reads the already generated store information group from the store information DB 143. The control unit 11 reads records from the verification information DB 142 that contain the same vendor name as the vendor name included in each of the already generated store information groups. The control unit 11 determines whether or not each read record contains "information being verified (verification in progress)". If the control unit 11 determines that each read record contains "information being verified (verification in progress)", it associates the information being verified with each of the target already generated store information groups. If the control unit 11 determines that each read record does not contain "information being verified (verification in progress)", it does not associate the information being verified with each of the target already generated store information groups. The control unit 11 repeats the above process for all the read records.

[0052] The control unit 11 generates a screen (hereinafter referred to as the "display screen") for displaying a list of new store information, previously generated store information, and information under review by combining new store information, previously generated store information, and information under review. Specifically, the control unit 11 generates a store information field d20 (see Figures 7 and 8) by sorting the new store information and previously generated store information with the information under review attached, as well as the new store information and previously generated store information without the information under review attached, from top to bottom in order of newest date and time information. The control unit 11 generates the display screen by placing an object field d10 (see Figures 7 and 8) above the store information field d20. The control unit 11 transmits the generated display screen to the computer 20 via the communication unit 13.

[0053] The control unit 21 receives the display screen transmitted from the server 10 via the communication unit 23. The control unit 21 displays the new store information along with the already generated store information group via the display screen displayed on the display unit 24. The control unit 21 displays confirmation information on the display unit 24, indicating that the item data is being confirmed, in association with the new store information, via the display screen displayed on the display unit 24.

[0054] Figures 7 and 8 are explanatory diagrams showing examples of screens. The display screen d1 shown in Figure 7 and the display screen d2 shown in Figure 8 include an object field d10 and a store information field d20. The store information field d20 is a field for displaying newly generated store information and already generated store information groups. In the store information field d20 of Figure 7, "N4 Building," "N3 Building," and "N2 Building" are displayed as examples of newly generated store information, and "N1 Building" to "H2 Building" are displayed as examples of already generated store information groups. Furthermore, if a floor plan is selected from the store information displayed in the store information field d20, the control unit 21 overlays the floor plan (not shown) of the corresponding store information onto the store information field d20.

[0055] The object column d10 displays multiple selectable objects for color-coding and associating the status of each store's information, including new listings, under consideration, applications being submitted, viewings in progress, negotiations under consideration, or completed transactions, with the store information column d20. The multiple objects include the first object O1 (all), the second object O2 (new listings), the third object O3 (under consideration), the fourth object O4 (applications being submitted), the fifth object O5 (viewings in progress), the sixth object O6 (negotiations under consideration), and the seventh object O7 (completed transactions). In the display screen d1 shown in Figure 7, the first object O1 is selected. In the display screen d2 shown in Figure 8, the second object O2 is selected.

[0056] When the first object O1 is selected, the control unit 21 displays the store information in the store information field d20 as shown on the display screen d1, and displays the status of each store information, including new, under consideration, application in progress, viewing, negotiation under consideration, or completed, in corresponding colors. On the display screen d1, each status is displayed in different colors, such as white, grayscale, or hatching. The control unit 21 also displays "under review" information to indicate that the item data is being reviewed, corresponding to the new store information "N4 Building" and "N3 Building".

[0057] If any of the objects provided for each status different from the first object O1 is selected, the control unit 21 displays a list of store information that matches the corresponding status. Specifically, if the second object O2 is selected, the control unit 21 identifies the store information that matches the status "New" corresponding to the second object O2 from the list of store information displayed in the store information field d20. As shown in the display screen d2, the control unit 21 displays a list of store information "N4 Building", "N3 Building", "N2 Building", "N1 Building", and "M4 Building" that matches the identified status "New". The control unit 21 also displays confirmation information indicating that the item data is being checked, associated with the new store information "N4 Building", "N3 Building", and "N1 Building".

[0058] Figures 9 and 10 are flowcharts illustrating the processing procedure of the information processing system. The control unit 11 receives emails or other communications from property information providers such as real estate agents, thereby obtaining property information that can be used as a store development candidate, as well as the email address of the property information provider (step S101). The control unit 11 generates a prompt that includes the property information and a generation instruction (step S102). The control unit 11 provides the generated prompt 30 to the language model 12M, thereby generating candidate store information by extracting item data for each item (step S103).

[0059] The control unit 11 reads the name of the provider of the acquired property information from the accuracy information DB 141, corresponding to the email address of the provider (step S104). The control unit 11 associates the status "New" and the provider's name with the new store information (step S105). The control unit 11 stores the new store information, the status "New" associated with the new store information, and the provider's name in the store information DB 143, associating them with the provider ID and date / time information (step S106).

[0060] The control unit 11 reads accuracy information corresponding to the acquired provider's email address from the accuracy information DB 141 (step S107). Based on the read accuracy information, the control unit 11 decides whether or not to output "under review" information (step S108). Specifically, the control unit 11 determines whether or not the read accuracy information satisfies predetermined conditions. If the read accuracy information does not satisfy the predetermined conditions (step S108: NO), the control unit 11 decides to output "under review" information (step S109). The control unit 11 adds "under review" information to the new store information (step S110).

[0061] The control unit 11 stores the information being verified in the verification information DB 142, associating it with the store ID and the name of the provider (step S111). After step S111, the control unit 11 may also notify the information processing device (e.g., a computer) of the administrator of the information processing system via the communication unit 13 of the corresponding new store information and verification command. After the administrator completes the verification process following the notification of the new store information and verification command, the control unit 11 updates the record containing the store ID corresponding to the store information for which the verification process has been completed.

[0062] If the read accuracy information satisfies predetermined conditions (step S108: YES), the control unit 11 decides not to output the information under review (step S112). The control unit 11 stores the unreviewed information in the review information DB 142, associating it with the store ID and the name of the provider (step S113). If there are multiple new store entries, the control unit 11 repeatedly performs the processes from steps S107 to S113 for each of the new store entries.

[0063] Even if the read accuracy information satisfies predetermined conditions (step S108: YES), the control unit 11 may decide to output "Under Verification" information if at least one of the item data included in the newly generated store information is missing. In that case, the control unit 11 proceeds to step S110. The control unit 11 also notifies the information processing device of the administrator of the information processing system via the communication unit 13 of the corresponding new store information and a verification command including the missing item in the new store information.

[0064] After step S111 or after step S113, the control unit 11 reads the already generated store information group from the store information DB 143 (step S114). The control unit 11 reads the records containing the same vendor name as the vendor name included in each of the already generated store information groups from the verification information DB 142 (step S115). The control unit 11 determines whether each read record contains "verification information (verification in progress)" (step S116). If the control unit 11 determines that each read record contains "verification information (verification in progress)" (step S116: YES), it adds the verification information to each of the target already generated store information groups (step S117).

[0065] After step S117, or if the control unit 11 determines that none of the read records contain "Information under review (under review)" (step S116: NO), it determines whether the processing from step S116 to step S117 has been completed for all the read records (step S118). If the control unit 11 determines that the processing from step S116 to step S117 has not been completed for all the read records (step S118: NO), it returns to step S116.

[0066] If the control unit 11 determines that the processing from steps S116 to S117 has been completed for all the records it has read (step S118: YES), it generates a display screen by combining the new store information, the already generated group of store information, and the information being checked (step S119). The control unit 11 transmits the generated display screen to the computer 20 via the communication unit 13 (step S120).

[0067] The control unit 21 receives the display screen transmitted from the server 10 via the communication unit 23 (step S201). The control unit 21 displays the new store information together with the already generated store information group via the display screen displayed on the display unit 24 (step S202). Specifically, if the first object O1 is selected, the control unit 21 displays the store information in a list in the store information field d20, as shown in the display screen d1, and displays the status of each store information, including new, under consideration, application in progress, viewing, negotiation under consideration, or contract concluded, color-coded accordingly.

[0068] Furthermore, if any of the objects provided for each status different from the first object O1 is selected, the control unit 21 displays a list of store information that matches the corresponding status. The control unit 21 then displays confirmation information on the display unit 24, indicating that the item data is being checked, in association with the new store information (step S203).

[0069] According to Embodiment 1, the information processing system can acquire property information that is a candidate for store development, provide the acquired property information to a language model to generate candidate store information by extracting item data for each item, and output the newly generated store information together with the already generated store information group.

[0070] According to Embodiment 1, the information processing system can output verification information indicating that item data is being verified, in association with new store information.

[0071] According to Embodiment 1, the information processing system can determine whether or not to display the "under review" information based on the accuracy information stored for each provider of the acquired property information.

[0072] According to Embodiment 1, when a first object is selected, the information processing system can output a list of store information and color-code the status of each store, including new, under consideration, application in progress, viewing, negotiation under consideration, or completed.

[0073] According to Embodiment 1, when one of the objects provided for each status different from the first object is selected, the information processing system can output a list of store information that matches the corresponding status.

[0074] (Embodiment 2) Embodiment 2 describes a method by which an information processing system outputs a map of the area corresponding to the location information of selected store information, along with pedestrian flow information for the corresponding area and the number of people making payments for the corresponding area.

[0075] Figure 11 is an explanatory diagram showing an example of the record layout of the store information DB according to Embodiment 2. The store information DB 143 includes columns for store ID, store name, provider name, date and time information, status, rent, price per tsubo, floor area, location information, nearest station, access, contract type, floor plan, and remarks, as well as a map column. The map column stores a map of the area corresponding to the location information of the store. The format of the map of the corresponding area can be, for example, an image format (e.g., PNG, JPEG, or BMP), a map data format (e.g., KML, KMZ, or GPX), a PDF format, or a Google My Maps format.

[0076] Figure 12 is an explanatory diagram showing an example of the record layout of the payment database, card database, and merchant database. The payment database 144 includes columns for merchant ID, payment date and time, card number, and payment amount. The merchant ID column stores the merchant ID to identify the merchant that made the payment with a credit card. The payment date and time column stores the date and time the payment was made with a credit card. The card number column stores the card number of the credit card. The payment amount column stores the payment amount of the credit card.

[0077] Card DB145 includes columns for Credit Card ID, Card Number, Name, Gender, Age Group, and Annual Income. The Credit Card ID column stores the Credit Card ID to identify multiple credit cards. The Card Number column stores the card number of the credit card. The Name column stores the name of the credit card holder. The Gender column stores the gender of the credit card holder. The Age Group column stores the age group of the credit card holder. The Annual Income column stores the annual income of the credit card holder.

[0078] Merchant DB146 includes a Merchant ID column, a Location Information column, and an Industry column. The Merchant ID column stores a Merchant ID to identify a merchant that accepts multiple credit cards. The Location Information column stores the location information (longitude, latitude, address, etc.) of the credit card merchant. The Industry column stores the industry of the credit card merchant. Payment DB144, Card DB145, and Merchant DB146 are created by the credit card management company.

[0079] Figure 13 is an explanatory diagram showing an example of the record layout of the pedestrian flow information DB and the road section DB. The pedestrian flow information DB 147 stores pedestrian flow information corresponding to location information. The pedestrian flow information DB 147 is created by a specialized company that collects pedestrian flow information. The pedestrian flow information corresponding to location information is, for example, the pedestrian flow (number of people) for each road section at a certain time on a certain date. The pedestrian flow information DB 147 includes a date column, a road section ID column, a time column, and a pedestrian flow column. The date column stores the date. The road section ID column stores road section IDs for identifying multiple road sections. The time column stores the time. The pedestrian flow column stores the pedestrian flow (number of people) for each road section at a certain time on a certain date.

[0080] The Road Section DB148 stores information such as the route name, starting intersection, and ending intersection for each road section. The Road Section DB148 includes columns for Road Section ID, Road Section Name, Starting Intersection, and Ending Intersection. The Road Section ID column is the same as that of the Pedestrian Flow Information DB147. The Road Section Name column stores the official name of the road section. The Starting Intersection column stores the location information (longitude and latitude) of the intersection that serves as the starting point for each road section (hereinafter referred to as the starting intersection). The Ending Intersection column stores the location information (longitude and latitude) of the intersection that serves as the ending point for each road section (hereinafter referred to as the ending intersection).

[0081] The road section DB148 may store location information for the four corners of each road section instead of location information for the starting and ending intersections. If sidewalks are provided on both sides of the roadway for each road section, the road section DB148 associates a separate road section ID with each of the sidewalks on both sides of the road section. Specifically, the road section name, starting intersection, and ending intersection for each of the sidewalks on both sides of the road section are associated with the road section ID and stored.

[0082] The processing of Embodiment 2 will now be described. The processing that is the same as in Embodiment 1 will be omitted from the description. The control unit 11 associates the status "New" with the provider's name and the map of the area corresponding to the location information of the new store information for the new store information generated by the language model 12M. Instead of the map of the area corresponding to the location information of the new store information, the control unit 11 may associate the "Google Maps API" of the area corresponding to the location information of the new store information for the new store information generated by the language model 12M.

[0083] The area corresponding to the location information of the store information is, for example, the area within a 100m radius centered on the location information of the store information. The control unit 11 stores the associated new store information, the status "New," the name of the provider, and a map of the area corresponding to the location information of the new store information in the store information DB 143 of the large-capacity storage unit 14. The control unit 11 reads the already generated group of store information from the store information DB 143.

[0084] The control unit 11 acquires pedestrian flow information corresponding to location information by reading information stored in the pedestrian flow information DB 147 created by a specialized company that collects pedestrian flow information. Alternatively, the control unit 11 may acquire pedestrian flow information corresponding to location information by using an information processing terminal equipped with GPS (Global Positioning System) functionality (for example, a smartphone or car navigation system), or a visible light camera or infrared camera.

[0085] Furthermore, the control unit 11 may acquire pedestrian flow information corresponding to location information by using mobile phone base stations, Wi-Fi access points, BLE (Bluetooth Low Energy) beacons, or 3D-LiDAR (Light Detection And Ranging) located in each area. In this embodiment, an example is described in which the control unit 11 acquires pedestrian flow information corresponding to location information by reading information stored in a pedestrian flow information DB 147 created by a specialized company or the like.

[0086] The control unit 11 obtains credit card data by reading information recorded in the payment DB 144, card DB 145, and merchant DB 146 created by the credit card management company. The credit card data includes the merchant ID, date and time of payment by credit card, card number, payment amount, credit card ID, credit card number, credit card payment amount, name, gender, age and annual income of the credit card holder, merchant ID, and the location information and industry of the credit card merchant.

[0087] Furthermore, the control unit 11 acquires credit card data by receiving credit card data from multiple merchants that accept the target credit card, or by receiving credit card data from the target credit card company. In this embodiment, an example is described in which the control unit 11 acquires credit card data by reading information recorded in the payment DB 144, card DB 145, and merchant DB 146 created by the credit card management company.

[0088] The control unit 11 transmits new store information, the status of the new store information, a map of the area corresponding to the location information of the new store information, and the already generated store information group to the computer 20 via the communication unit 13. The control unit 21 receives the new store information, the status of the new store information, a map of the area corresponding to the location information of the new store information, and the already generated store information group transmitted from the server 10 via the communication unit 23.

[0089] The control unit 21 displays the new store information on the display unit 24 along with the already generated store information group. Specifically, the control unit 21 displays the display screen d1 (Figure 7) on the display unit 24. The control unit 21 accepts the selection of store information by the user through touch or click or other selection of the store information displayed in the store information field d20.

[0090] The control unit 21 may display a map of the area corresponding to the location information of the new store, or a map of the area corresponding to the already generated group of store information. In this case, the control unit 21 accepts the selection of store information by the user through touch or click on the map of the area corresponding to the location information of the new store, or the map of the area corresponding to the already generated group of store information.

[0091] The control unit 21 accepts the user's selection via the input unit 25, allowing the user to choose a specified period (for example, the past 1 day, the past 3 days, the past 1 week, or the past 1 month). Specifically, the control unit 21 accepts the selection of the specified period via a pull-down menu or the like (not shown) that is additionally displayed on the display screen d1. The control unit 21 transmits the location information of the store information and the specified period, which have been accepted as a selection, to the server 10 via the communication unit 23.

[0092] The control unit 11 receives location information and a specified period of store information transmitted from the computer 20 via the communication unit 13. Based on the pedestrian flow information corresponding to the acquired location information, the control unit 11 generates pedestrian flow information for the area corresponding to the location information of the selected store information (hereinafter referred to as pedestrian flow information for the corresponding area).

[0093] This section describes how to generate pedestrian flow information for a corresponding area. The pedestrian flow information for a corresponding area includes, for example, the distribution of total or average numbers of people in each road section located within a 100m radius centered on the location information of the selected store, during a specified period, and the distribution of total or average numbers of people by time of day in each road section located within that area during a specified period. The pedestrian flow information for a corresponding area also includes, for example, the trend of total numbers by time of day in each road section located within that area during a specified period, and the trend of average numbers by time of day in each road section located within that area during a specified period.

[0094] Furthermore, the pedestrian flow information for the corresponding area is, for example, the unit area (m²) of each road section located within a 100m radius centered on the location information of the selected store during a specified period. 2 This includes the distribution of the total number of people per unit area or the distribution of the average number of people. Hereafter, as a concrete example of pedestrian flow information for the corresponding area, we will use "the distribution of the total number of people over the past week in each road section located within a 100m radius centered on the location information of the selected store information."

[0095] The control unit 11 identifies records from the road section DB 148 that include at least one of the location information of the starting intersection and the location information of the ending intersection within a 100m radius centered on the location information of the received store information. If the area within a 100m radius centered on the location information of the received store information includes the location information of the starting intersection "X1,Y1" and "X3,Y3" and the location information of the ending intersection "X33,Y33", the control unit 11 identifies records from the road section DB 148 that include road section IDs "R001" and "R003".

[0096] The control unit 21 reads from the pedestrian flow information DB 147 records that contain the same road section ID as the record identified from the road section DB 148. If the road section IDs included in the record identified from the road section DB 148 are "R001" and "R003", the control unit 21 reads from the pedestrian flow information DB 147 records that contain either "R001" or "R003" as the road section ID.

[0097] The control unit 11, by referring to the date column, extracts records from the pedestrian flow information DB 147 that include a date within one week of the date on which the location information of the selected store was received. If the date on which the location information of the selected store was received is "2025 / 2 / 27", the control unit 11 extracts records from the pedestrian flow information DB 147 whose dates range from "2025 / 2 / 27" to "2025 / 2 / 20".

[0098] The control unit 11 calculates the total number of people for each road section over the past week by aggregating the pedestrian flow (number of people) included in the extracted records for each road section ID. The control unit 21 repeats the same process for all road sections corresponding to the identified road section ID to calculate the total number of people for each road section located within a 100m radius centered on the location information of the selected store information over the past week. The control unit 11 refers to the date column and extracts records from the pedestrian flow information DB 147 that include a date within one week of the date the location information of the selected store information was received.

[0099] The control unit 11 aggregates the pedestrian traffic (number of people) included in the extracted records for each road section ID to identify the total number of people for each road section nationwide over the past week. The control unit 11 then aggregates the total number of people for each identified road section nationwide over the past week to calculate the average value of the total number of people for each road section nationwide over the past week (hereinafter referred to as the national average value).

[0100] The control unit 21 calculates a score (hereinafter referred to as the degree score) that indicates the degree of the total number of people in each road section over the past week relative to the national average by determining the ratio of the total number of people in each road section over the past week over the past week to the national average. Specifically, if the total number of people in the road section corresponding to road section ID "R001" over the past week is "900" and the national average is "1000", the control unit 11 calculates a degree score of "900 / 1000=0.9". The control unit 21 repeats the same process for all road sections located within a 100m radius centered on the location information of the selected store information, thereby calculating the degree score for all road sections located in that area.

[0101] The control unit 11 generates the distribution of the total number of people (people flow information for the corresponding area) for the past week in each road section located within a 100m radius centered on the location of the selected store information by color-coding each road section on the map of the area corresponding to the location of the selected store information according to the calculated degree score of all road sections in the area. Specifically, the control unit 11 outputs the road section on the map of the corresponding area in white if the degree score is "less than 1.0", outputs the road section on the map of the corresponding area in grayscale if the degree score is "1.0 or more and less than 1.5", and outputs the road section on the map of the corresponding area in black if the degree score is "1.5 or more", thereby generating the distribution of the total number of people (people flow information for the corresponding area) for the past week in each road section located within a 100m radius centered on the location of the selected store information.

[0102] Furthermore, as a concrete example of pedestrian flow information for the corresponding area, "the unit area (m²) of each road section located within a 100m radius centered on the location information of the selected store information over the past day" 2 You may also use the "distribution of the total number of people per )". The processing in that case will be explained below. Road section DB148 further includes an area column. The area column contains the area (m²) of each road section. 2 The control unit 21 stores the total number of people (people) in each road section of the area over the past week, and the area (m²) of each road section of the area. 2 By dividing by ), the total number of people per unit area (people / m²) in each road section of the area over the past week can be calculated. 2 Identify )

[0103] The control unit 21 calculates the total number of people per unit area (people / m²) for each road section in the identified area over the past week. 2 By dividing ) by 7 (days), the total number of people per unit area (people / m²) for each road section in the area over the past day can be calculated. 2 The control unit 11 calculates the total number of people per unit area (people / m²) for each road section in the area over the past day. 2According to , by outputting each road section in the map of the area corresponding to the location information of the selected store information in different colors, the distribution of the total number of people per unit area (m 2 ) in the area within a radius of 100 m centered on the location information of the selected store information (the pedestrian flow information of the corresponding area) is generated.

[0104] Specifically, the control unit 11 calculates the total number of people per unit area (person / m 2 ) in each road section of the area in the past one day. If the total number of people per unit area is less than the first criterion (for example, 100 (person / m 2 )), the road section in the map of the corresponding area is output in white. If the degree score is greater than or equal to the first criterion and less than the second criterion (for example, 200 (person / m 2 )), the road section in the map of the corresponding area is output in grayscale. If the degree score is greater than or equal to the second criterion, the road section in the map of the corresponding area is output in black. The first criterion and the second criterion are stored in advance in the storage unit 12 or the mass storage unit 14. As described above, the control unit 11 can generate the distribution of the total number of people per unit area (m 2 ) in each road section of the area within a radius of 100 m centered on the location information of the selected store information in the past one week (the pedestrian flow information of the corresponding area).

[0105] Based on the acquired credit card data, the control unit 11 identifies the settlement population, settlement scale, settlement male-female ratio, settlement age ratio, and income-based ratio (hereinafter referred to as the settlement population, settlement scale, settlement male-female ratio, settlement age ratio, and income-based ratio of the corresponding area) of the area corresponding to the location information of the selected store information. The settlement population, settlement scale, settlement male-female ratio, settlement age ratio, and income-based ratio of the corresponding area are, for example, the settlement population, settlement scale (total settlement amount), settlement male-female ratio, settlement age ratio, and income-based ratio in the specified period (hereinafter referred to as the past one week) of the area within a radius of 100 m centered on the location information of the selected store information.

[0106] The details of how to generate the payment population, payment scale, payment gender ratio, payment age ratio, and income ratio for the corresponding area will be explained. The control unit 11 reads records from the merchant database 146 that include location information (longitude and latitude) for an area within a 100m radius centered on the location information of the selected store. The control unit 11 reads records from the payment database 144 that contain the same merchant ID as the merchant ID of the record read from the merchant database 146.

[0107] The control unit 11 refers to the settlement date and time and extracts records from the settlement DB 144 that contain the same merchant ID and include a date and time within one week of the date and time the location information of the selected store was received. The control unit 11 reads records from the card DB 145 that contain the same card number as the records extracted from the settlement DB 144 that include a date and time within one week. The control unit 11 identifies the payment population of the corresponding area by summing the number of records containing the same card number read from the card DB 145. The control unit 11 identifies the payment scale of the corresponding area by summing the settlement amounts of records containing the same merchant ID extracted from the settlement DB 144.

[0108] The control unit 11 identifies the male-female payment ratio for a corresponding area by calculating the ratio of the number of records containing the same card number read from the card DB 145 that have "Male" stored in the gender column to the number of records that have "Female" stored in the gender column. The control unit 11 identifies the age ratio of payments for a corresponding area by aggregating the number of records read from the card DB 145 for each generation in the generation column (e.g., "Teens", "20s", "30s", "40s", "50s", "60s", "70s", and "80s and over"). The control unit 11 identifies the income ratio for a corresponding area by aggregating the number of records containing the same card number read from the card DB 145 for each income in the income column (e.g., "5 million", "5.5 million", "6 million", and "10 million").

[0109] The control unit 11 generates a graph (hereinafter referred to as the "comparison graph") that compares the payment rate in the area corresponding to each industry with the payment rate nationwide, based on the acquired credit card data. The comparison graph includes a bar graph that compares the payment rate in the corresponding area and the payment rate nationwide by industry, and a line graph that compares the payment rate nationwide and the payment rate in the corresponding area by industry.

[0110] This section explains the details of how to generate comparison graphs. First, it explains how to calculate the nationwide settlement rate by industry. The nationwide settlement rate by industry is, for example, the nationwide settlement rate by industry over a specified period (hereinafter referred to as the past week). The control unit 11 reads the records for the past week from the settlement DB 144. The control unit 11 determines the total nationwide settlement amount for the past week by aggregating the settlement unit prices of the records read for the past week. The control unit 11 refers to the merchant DB 146 and extracts records by industry from the records read from the settlement DB 144 for the past week. Specifically, when extracting records for the industry "Medical," the control unit 11 refers to the merchant DB 146 and extracts records from the records read from the settlement DB 144 that contain a merchant ID for the industry "Medical."

[0111] The control unit 11 identifies the total settlement amount for each industry nationwide by aggregating the settlement unit prices of the extracted records for each industry. The control unit 11 calculates the nationwide settlement ratio for each industry by calculating the ratio of the total settlement amount for each industry nationwide to the total settlement amount nationwide. Specifically, when calculating the nationwide settlement ratio for the industry "healthcare," the control unit 11 calculates the nationwide settlement ratio for the industry "healthcare" by calculating the ratio of the total settlement amount for the industry "healthcare" nationwide to the total settlement amount nationwide.

[0112] Next, we will explain how to calculate the payment rate by industry in the area corresponding to the location information of the store information. The payment rate by industry in the area corresponding to the location information of the store information is, for example, the payment rate by industry in the past week in the area within a 100m radius centered on the location information of the selected store information. The control unit 11 refers to the merchant DB 146 and extracts records from the past week read from the payment DB 144 that contain the merchant ID of the area within a 100m radius centered on the location information of the selected store information.

[0113] The control unit 11 aggregates the extracted records to identify the total payment amount for the area corresponding to the location information of the store. The control unit 11 refers to the merchant DB 146 and extracts records by industry from the extracted records. Specifically, when extracting records of the industry "medical," the control unit 11 extracts records from the extracted records in which "medical" is stored in the industry column. The control unit 11 aggregates the extracted records by industry to identify the total payment amount by industry for the area corresponding to the location information of the store.

[0114] The control unit 11 calculates the payment ratio for each industry in the area corresponding to the location information of the store information by calculating the ratio of the total payment amount for each industry in the area corresponding to the location information of the store information to the total payment amount in the area corresponding to the location information of the store information. Specifically, when calculating the payment ratio for the industry "medical" in the area corresponding to the location information of the store information, the control unit 11 calculates the payment ratio for the industry "medical" in the area corresponding to the location information of the store information by calculating the ratio of the total payment amount for the industry "medical" in the area corresponding to the location information of the store information to the total payment amount in the area corresponding to the location information of the store information.

[0115] The control unit 11 calculates the payment rate for each industry in the area corresponding to the location information of the store for all industries. The control unit 11 combines the calculated nationwide payment rate for each industry and the payment rate for each industry in the area corresponding to the location information of the store for each industry to generate a bar graph (comparison graph) that compares the payment rate in the corresponding area and the nationwide payment rate for each industry.

[0116] The control unit 11 calculates the payment ratio for each industry in the area corresponding to the location information of the store information by dividing the payment ratio for each industry in the area corresponding to the location information of the store information by the payment ratio for each industry nationwide. The control unit 11 calculates the payment ratio for each industry in the area corresponding to the location information of the store information for all industries. The control unit 11 generates a line graph (comparison graph) comparing the payment ratio for each industry nationwide and the payment ratio for each industry in the corresponding area by combining the payment ratios calculated for all industries.

[0117] The control unit 11 transmits to the computer 20 via the communication unit 13 the pedestrian flow information for the area corresponding to the location information of the generated store information, the payment population, payment scale, payment gender ratio, payment age ratio, and payment income ratio for the identified corresponding area, and the generated comparison graph. The control unit 21 receives via the communication unit 23 the pedestrian flow information for the area corresponding to the location information of the generated store information, the payment population, payment scale, payment gender ratio, payment age ratio, and payment income ratio for the identified corresponding area, and the generated comparison graph.

[0118] The control unit 21 displays a map of the area corresponding to the location information of the selected store information, and also displays pedestrian flow information for the area corresponding to the received location information of the store information on the display unit 24. Thus, the display unit 24 can display a map of the area corresponding to the location information of the selected store information, and also display pedestrian flow information for the area based on the acquired pedestrian flow information. The control unit 21 displays the received payment population, payment scale, payment gender ratio, payment age ratio, and payment income ratio for the corresponding area, along with a comparison graph, via the display unit 24.

[0119] Figure 14 is an explanatory diagram showing an example of a screen. The display screen d3 shown in Figure 14 includes an object field d10, a map field d30, and a credit card data field d40. The object field d10 is the same as that of display screen d1. The map field d30 displays a map of the area corresponding to the location information of the store selected in the store information field d20 of display screen d1, along with pedestrian flow information for that area.

[0120] The map column d30 in Figure 14 displays a map of the area corresponding to the location information of the store "N4 Building," along with the pedestrian traffic for the past week within a 100m radius centered on the location information of "N4 Building." The pedestrian traffic for the past week is displayed in different colors according to the level of pedestrian traffic. Specifically, in Figure 14, locations with high pedestrian traffic in the corresponding area are displayed in black, locations with medium pedestrian traffic are displayed in grayscale, and locations with low pedestrian traffic are displayed in white.

[0121] The credit card data field d40 displays the payment population, payment volume, payment gender ratio, and income breakdown for the area corresponding to the location information of the store selected in the store information field d20 on display screen d1. On display screen d3, the credit card data field d40 displays the payment population and payment volume for the corresponding area as numerical values, while the payment gender ratio, payment age ratio, and income breakdown for the corresponding area are displayed as pie charts.

[0122] Additionally, the credit card data section d40 displays comparison graphs, including bar graphs and line graphs. The horizontal axis of the comparison graphs represents industries (e.g., healthcare, education, and food service). The vertical axis on the left side of the comparison graphs corresponds to the bar graphs and shows the nationwide payment percentage (from 0% to 100%) for each industry, or the payment percentage (from 0% to 100%) for each industry in the corresponding area.

[0123] The vertical axis on the right side of the comparison graph corresponds to the line graph and shows the payment ratio by industry (for example, from 0 to 10) for the area corresponding to the location information of the selected store. The line graph is generated by connecting the payment ratios by industry for the corresponding area, which are obtained by dividing the payment ratio by industry for the area corresponding to the location information of the selected store (hatched bar graph) by the payment ratio by industry nationwide (white bar graph). The bar graph displays, for example, the payment ratio by industry nationwide (right side) and the payment ratio by industry for the corresponding area (left side) as a set.

[0124] The line graph displays, for example, the payment rates for each industry in the corresponding area, arranged from left to right in descending order of industry. In Figure 14, in the credit card data column d40, the nationwide payment rates by industry are shown in white in the bar graph, and the payment rates by industry in the corresponding area are shown in hatching. In Figure 14, in the credit card data column d40, the line graph is shown as a black line.

[0125] Figure 15 is a flowchart showing the processing procedure of the information processing system according to Embodiment 2. In the flowchart shown in Figure 15, the processing after step S104 is changed to steps S301 to S311 and steps S401 to S408, respectively, in the processing shown in Figures 9 and 10. The steps that are the same as in Figure 9 are omitted from the explanation.

[0126] The control unit 11 associates the status "New" with the new store information generated in step S103, the name of the provider read in step S104, and a map of the area corresponding to the location information of the store information (step S301). The control unit 11 stores the newly generated store information, the status "New", the name of the provider, and the map of the area corresponding to the location information of the store information in the store information DB 143 of the large-capacity storage unit 14 (step S302). The control unit 11 reads the already generated group of store information from the store information DB 143 (step S303).

[0127] The control unit 11 obtains pedestrian flow information corresponding to location information by reading information stored in the pedestrian flow information DB 147 created by a specialized company, etc. (step S304). The control unit 11 obtains credit card data by reading information stored in the payment DB 144, card DB 145, and merchant DB 146 created by the credit card management company (step S305). The control unit 11 transmits new store information, the status of the new store information, a map of the area corresponding to the location information of the new store information, and the already generated group of store information to the computer 20 via the communication unit 13 (step S306).

[0128] The control unit 21 receives, via the communication unit 23, new store information, the status of the new store information, a map of the area corresponding to the location information of the new store information, and a group of already generated store information transmitted from the server 10 (step S401). The control unit 21 displays the new store information together with the group of already generated store information via the display unit 24 (step S402). The control unit 21 accepts the selection of store information by the user, such as by touching or clicking on the store information displayed in the store information field d20 (step S403). The control unit 21 accepts the selection of a specified period by accepting the selection via the input unit 25 from the user (step S404). The control unit 21 transmits the location information of the selected store information and the specified period to the server 10 via the communication unit 23 (step S405).

[0129] The control unit 11 receives the location information of the store information and the specified period transmitted from the computer 20 via the communication unit 13 (step S307). Based on the pedestrian flow information acquired in step S304, the control unit 11 executes a subroutine related to the generation of pedestrian flow information for the area corresponding to the location information of the selected store information (step S308). Based on the credit card data acquired in step S305, the control unit 11 executes a subroutine related to the generation of payment population, payment scale, payment gender ratio, payment age ratio, and income ratio for the corresponding area (step S309).

[0130] The control unit 11 aggregates the read credit card data and executes a subroutine related to the generation of a comparison graph (step S310). The control unit 11 transmits the pedestrian flow information for the corresponding area generated in step S308, the payment population for the corresponding area generated in step S309, and the comparison graph generated in step S310 to the computer 20 via the communication unit 13 (step S311).

[0131] The control unit 21 receives, via the communication unit 23, pedestrian flow information for the corresponding area, payment population for the corresponding area, and a comparison graph from the server 10 (step S406). The control unit 21 displays a map of the area corresponding to the location information of the selected store via the display unit 24, and displays pedestrian flow information for the area corresponding to the location information of the selected store based on the acquired pedestrian flow information (step S407). The control unit 21 displays, via the display unit 24, the payment population, payment scale, payment gender ratio, payment age ratio, and payment income ratio for the area corresponding to the location information of the selected store, along with a comparison graph (step S408).

[0132] Figure 16 is a flowchart showing the processing procedure of a subroutine related to the generation of pedestrian flow information for a corresponding area. Details of the subroutine related to step S308 will be explained below. The control unit 11 identifies a record from the road section DB 148 that contains at least one of the location information of the starting intersection and the location information of the ending intersection within an area within a 100m radius centered on the location information of the received store information (step S1). The control unit 21 reads a record from the pedestrian flow information DB 147 that contains the same road section ID as the record identified from the road section DB 148 (step S2).

[0133] The control unit 11, by referring to the date column, extracts records from the pedestrian flow information DB 147 that include a date within one week of the date on which the location information of the selected store was received (step S3). The control unit 11 calculates the total number of people for each road section over the past week by aggregating the pedestrian flow (number of people) included in the extracted records for each road section ID (step S4). The control unit 21 repeatedly performs the process in step S4 for all road sections included in the records extracted in step S3.

[0134] The control unit 11, by referring to the date column, extracts records from the pedestrian flow information DB 147 that include a date within one week of the date on which the location information of the selected store was received (step S5). The control unit 11 calculates the total number of people (people) for each road section nationwide over the past week by aggregating the pedestrian flow (number of people) included in the records extracted in step S6 for each road section ID (step S6). The control unit 21 repeatedly performs the process in step S6 for all road sections nationwide included in the records extracted in step S5.

[0135] The control unit 11 calculates the total number of people (people / m²) in each road section across the country identified in step S6 over the past week. 2 The control unit 21 calculates the national average by aggregating the data (step S7). The control unit 21 calculates a degree score by determining the ratio between the total number of people in each road section over the past week calculated in step S4 and the national average calculated in step S7 (step S8). The control unit 21 repeatedly performs the process in step S8 for all road sections located within a 100m radius centered on the location information of the selected store.

[0136] The control unit 11 generates the distribution of the total number of people in each road section located within a 100m radius centered on the location of the selected store information over the past week (pedestrian flow information for the corresponding area) by color-coding each road section on the map of the area corresponding to the location of the selected store information according to the degree score of all road sections calculated (step S9). After step S9, the control unit 11 returns the subroutine.

[0137] Figure 17 is a flowchart showing the processing procedure of a subroutine related to the generation of payment population, etc., for a corresponding area. Details of the subroutine related to step S309 will be explained below. The control unit 11 reads a record from the merchant DB 146 that contains location information (longitude and latitude) for an area within a 100m radius centered on the location information of the selected store (step S11). The control unit 11 reads a record from the payment DB 144 that contains the same merchant ID as the merchant ID of the record read in step S11 (step S12).

[0138] The control unit 11 extracts records from the records read in step S12 that contain date and time information within one week from the date and time the location information of the selected store information was received (step S13). The control unit 11 reads records from the card DB 145 that contain the same card number as the record extracted in step S13 (step S14). The control unit 11 identifies the payment population of the corresponding area by summing the number of records read in step S14 (step S15).

[0139] The control unit 11 determines the settlement scale (total settlement amount) of the corresponding area by summing the settlement amounts of the records extracted in step S13 (step S16). The control unit 11 determines the settlement gender ratio of the corresponding area by calculating the ratio of the number of records in which "male" is stored in the gender column to the number of records in which "female" is stored in the gender column among the records read in step S14 (step S17).

[0140] The control unit 11 identifies the settlement age ratio for the corresponding area by aggregating the number of records read in step S14 for each generation in the generation column (step S18). The control unit 11 identifies the income ratio for the corresponding area by aggregating the number of records read in step S14 for each income in the income column (step S19). After step S19, the control unit 11 returns the subroutine.

[0141] Figure 18 is a flowchart showing the processing procedure of a subroutine related to the process of generating a comparison graph. Details of the subroutine related to step S310 will be explained below. The control unit 11 reads records from the settlement DB 144 for the past week (step S21). The control unit 11 identifies the total settlement amount nationwide for the past week by aggregating the settlement unit prices of the records read for the past week (step S22). The control unit 11 refers to the merchant DB 146 and extracts records by industry from the records read from the settlement DB 144 (step S23).

[0142] The control unit 11 identifies the total settlement amount for each industry nationwide over the past week by aggregating the settlement unit prices of the extracted records for each industry (step S24). The control unit 11 calculates the nationwide settlement ratio for each industry over the past week by calculating the ratio of the total settlement amount for each industry nationwide to the total settlement amount nationwide (step S25). The control unit 11 refers to the merchant DB 146 and extracts records from the records read in step S21 over the past week that include merchant IDs in an area within a 100m radius centered on the location information of the selected store (step S26).

[0143] The control unit 11 aggregates the extracted records to identify the total settlement amount for the area corresponding to the store location information over the past week (step S27). The control unit 11 refers to the merchant DB 146 and extracts records by industry from the records extracted in step S26 (step S28). The control unit 11 aggregates the extracted records by industry to identify the total settlement amount by industry for the area corresponding to the store location information over the past week (step S29).

[0144] The control unit 11 calculates the ratio of the total settlement amount by industry in the area corresponding to the store location information to the total settlement amount in the area corresponding to the store location information for the past week (step S30). The control unit 11 repeats step S30 until it has calculated the settlement ratio by industry in the area corresponding to all industries. The control unit 11 combines the calculated nationwide settlement ratio by industry and the settlement ratio by industry in the area corresponding to the store location information for each industry to generate a bar graph (comparison graph) comparing the settlement ratio in the corresponding area and the nationwide settlement ratio by industry (step S31).

[0145] The control unit 11 calculates the payment ratio for each industry in the area corresponding to the location information of the store information by dividing the payment ratio for each industry in the area corresponding to the location information of the store information by the payment ratio for each industry nationwide (step S32). The control unit 11 repeats step S32 until it has calculated the payment ratio for each industry in the area corresponding to all industries. The control unit 11 generates a line graph (comparison graph) comparing the payment ratio for each industry nationwide and the payment ratio for each industry in the area corresponding to the industry by combining the payment ratios calculated for all industries (step S33). After step S33, the control unit 11 returns the subroutine.

[0146] According to Embodiment 2, the information processing system can acquire pedestrian flow information corresponding to location information, display a map of the area corresponding to the location information of the selected store, and output pedestrian flow information for the corresponding area based on the acquired pedestrian flow information.

[0147] According to Embodiment 2, the information processing system can acquire credit card data and, based on the acquired credit card data, output the payment population, payment scale, payment gender ratio, payment age ratio, and income ratio for the area corresponding to the location information of the selected store.

[0148] According to Embodiment 2, the information processing system can output a comparison graph based on the acquired credit card data.

[0149] (Embodiment 3) Embodiment 3 describes a method in which an information processing system receives a target business category for store development and outputs a store opening score for the received business category based on the acquired credit card data. The same processes as in Embodiment 2 will not be explained.

[0150] The control unit 21 displays a map of the area corresponding to the location information of the selected store via the display unit 24, and also displays pedestrian flow information for that area based on the acquired pedestrian flow information. In addition, the control unit 21 displays the received payment population, payment scale, payment gender ratio, payment age ratio, and income ratio for the corresponding area, along with a generated graph, via the display unit 24.

[0151] Figure 19 is an explanatory diagram showing an example of a screen. The display screen d3 shown in Figure 19 includes an object field d10, a map field d30, and a credit card data field d40. The object field d10 and the credit card data field d40 are the same as in Figure 14. The map field d30 further includes an industry field d31, a reference period field d32, and a store opening score field d33. The industry field d31 is a combo box or pull-down menu, etc., for accepting the industry to be developed by the user.

[0152] The Store Opening Score field d33 is for displaying the store opening score for the industry of the store development, as received in the Industry field d31. The store opening score is an indicator that shows the amount of potential customers for the target industry of the store development in the area corresponding to the location information of the selected store information. The Reference Period field d32 is a combo box or pull-down menu, etc., for receiving the period to be used as a reference when calculating the store opening score (hereinafter referred to as the reference period).

[0153] The reference periods are, for example, the past month, the summer period (for example, the period from the most recent June to the most recent August), the winter period (for example, the period from the most recent December to February), the past six months, the past year, and the past five years. In the industry field d31 of display screen d3, the selection of the store development industry "Food and Beverage" and the reference period "Past Month" is accepted. In the store opening score field d33 of display screen d3, the store opening score of "54" is displayed, based on the selection of "Food and Beverage" accepted in the industry field d31 and the selection of "Past Month" accepted in the reference period field d32.

[0154] The control unit 21 accepts the selection of the industry for store development via the industry field d31 on the display screen d3. The control unit 21 accepts the selection of the reference period via the reference period field d32 on the display screen d3. The control unit 21 transmits the selected industry for store development and reference period, along with a command to generate a store opening score, to the server 10 via the communication unit 23.

[0155] The control unit 11 receives, via the communication unit 13, the industry and reference period for store development, as well as instructions for generating a store opening score, transmitted from the computer 20. The control unit 11 refers to the card database 145 and the merchant database 146 and reads the credit card data stored in the payment database 144 for each industry and each user. Using the credit card data read for each industry and each user, and the received reference period, the control unit 11 identifies the usage history for each industry for each user.

[0156] Each user's usage history by industry includes, for example, the total monthly usage amount for each industry, the total summer usage amount for each industry, the total winter usage amount for each industry, the total semi-annual usage amount for each industry, the total annual usage amount for each industry, or the purchase history of each brand product by industry for each user. Hereafter, each user's usage history by industry will be explained as "each user's total monthly usage amount for each industry."

[0157] The control unit 11 identifies feature quantities using each user's total monthly spending for each industry. Specifically, if the breakdown of each user's total monthly spending for each industry includes "medical," "education," and "food and beverage," the control unit 11 identifies feature quantities (embedding vectors) for each user using each user's total monthly spending for medical, education, and food and beverage. The feature quantities identified for each user are linked to each user's credit card number or name. The control unit 11 identifies a store opening score based on the similarity between the average feature quantity of the user group that used the accepted industry and the feature quantity of the user group that made payments in the area corresponding to the location information of the selected store information.

[0158] The details of how the store opening score is determined are explained below. The control unit 11 refers to the payment DB 144, card DB 145, and merchant DB 146 to extract the features of each user who used the accepted business category from the identified features of each user. Specifically, if the accepted business category is "food and beverage," the control unit 11 extracts the features of each user who used the accepted business category "food and beverage" from the identified features of each user.

[0159] The control unit 11 calculates the average feature quantity of the user group that used the accepted industry by calculating the average value of the feature quantities of each extracted user. The control unit 11 calculates the standard deviation of the feature quantities of the user group that used the accepted industry by aggregating the feature quantities of each extracted user. Specifically, if the accepted industry is "food and beverage," the control unit 11 calculates the average feature quantity of the user group that used the accepted industry "food and beverage" and the standard deviation of the feature quantities of that user group by aggregating the feature quantities of each extracted user.

[0160] The control unit 11 refers to the payment DB 144, card DB 145, and merchant DB 146 to extract the features of users who made payments in the area corresponding to the location information of the selected store, from among the features of each identified user. Specifically, the control unit 11 refers to the payment DB 144, card DB 145, and merchant DB 146 to extract the features of users who made payments in an area within a 100m radius centered on the location information of the selected store, from among the features of each identified user.

[0161] The control unit 11 calculates the average value of the features of the extracted user group for each industry, thereby calculating the average value of the features of the user group that made a payment in the area corresponding to the location information of the selected store information for each industry. The control unit 11 identifies the store opening score using the average features of the user group that used the accepted industry, the standard deviation of the features of the user group that used the accepted industry, and the average value of the features of the user group that made a payment in the area corresponding to the location information of the selected store information. Specifically, if the accepted industry is "food and beverage," the control unit 11 identifies the store opening score using the average features of the user group that used the accepted industry "food and beverage," the standard deviation of the features of the user group that used the accepted industry "food and beverage," and the average value of the features related to the industry "food and beverage" of the user group that made a payment in the area corresponding to the location information of the selected store information.

[0162] Specifically, the control unit 11 identifies the store opening score using the following equation (1). Store opening score = 10 × (s - S) / SD + α ... (1) Here, (1) "s" represents the average feature of each industry for the user group that made a payment in the area corresponding to the location information of the selected store information. (1) "S" represents the average feature of the user group that used the accepted industry. (1) "SD" represents the standard deviation of the feature of the user group that used the accepted industry. (1) "α" is a coefficient, and for example, a value between 30 and 60 is substituted.

[0163] The control unit 11 transmits the identified store opening score to the computer 20 via the communication unit 13. The control unit 21 receives the store opening score transmitted from the server 10 via the communication unit 23. The control unit 21 displays the received store opening score on the display unit 24. Specifically, if the received store opening score is "54", the control unit 21 displays the store opening score "54" in the store opening score column d33 on the display screen d3.

[0164] In this embodiment, an example is shown in which the control unit 11 generates a store opening score based on the reference period selected in the reference period field d32, but it is not limited to this. If the control unit 11 receives a selection of a reference period after receiving a selection of a specified period, in addition to generating the store opening score, it may also regenerate pedestrian flow information for the corresponding area, payment population for the corresponding area, and a comparison graph. Specifically, if the control unit 11 receives a selection of "past 1 week" as the specified period, and then receives a selection of "past 1 month" as the reference period, the control unit 11 will regenerate pedestrian flow information for the corresponding area, payment population for the corresponding area, and a comparison graph based on the "past 1 month" reference period.

[0165] The control unit 11 transmits the generated store opening score, the regenerated pedestrian flow information for the corresponding area, the payment population for the corresponding area, and a comparison graph via the communication unit 13. The control unit 21 receives the store opening score, the pedestrian flow information for the corresponding area, the payment population for the corresponding area, and a comparison graph transmitted from the server 10 via the communication unit 23. The control unit 21 displays the received store opening score, the pedestrian flow information for the corresponding area, the payment population for the corresponding area, and a comparison graph via the display unit 24.

[0166] Figure 20 is a flowchart showing the processing procedure of the information processing system according to Embodiment 3. The flowchart in Figure 20 shows that, in the process shown in Figure 15, steps S501 to S505 and steps S601 to S605 are changed after step S407. The steps that are the same as in Figure 15 will not be explained.

[0167] The control unit 21 accepts the industry to be developed for store development via the industry field d31 on the display screen d3 (step S501). The control unit 21 accepts the selection of a reference period via the reference period field d32 on the display screen d3 (step S502). The control unit 21 sends the selected industry to be developed for store development and the reference period, along with a command to generate a store opening score, to the server 10 via the communication unit 23 (step S503).

[0168] The control unit 11 receives the industry and reference period for store development, as well as instructions for generating a store opening score, transmitted from the computer 20 via the communication unit 13 (step S601). The control unit 11 refers to the card DB 145 and the merchant DB 146 and reads the credit card data for each industry for each user stored in the payment DB 144 (step S602). Using the credit card data read for each industry and each user, the control unit 11 identifies the total monthly spending amount for each industry for each user (step S603).

[0169] The control unit 11 identifies feature quantities (embedding vectors) for each user using the total monthly usage amount for each industry (step S604). The feature quantities identified for each user are linked to each user's credit card number or each user's name, etc. Based on the similarity between the average feature quantity of the group of users who used the accepted industry and the feature quantity of the group of users who made payments in the area corresponding to the location information of the selected store information, the control unit 11 executes a subroutine for identifying the store opening score (step S605). The control unit 11 transmits the identified store opening score to the computer 20 via the communication unit 13 (step S606).

[0170] The control unit 21 receives the store opening score transmitted from the server 10 via the communication unit 23 (step S504). The control unit 21 displays the received store opening score via the display unit 24 (step S505).

[0171] Figure 21 is a flowchart showing the processing procedure of a subroutine related to the identification of store opening scores. Details of the subroutine related to step S605 will be explained below. The control unit 11 refers to the payment DB 144, card DB 145, and merchant DB 146 to extract the features of each user that utilize the accepted industry from the features of each identified user (step S41).

[0172] The control unit 11 calculates the average feature quantity of the user group that used the accepted industry by calculating the average value of the feature quantities of each extracted user (step S42). The control unit 11 calculates the standard deviation of the feature quantities of the user group that used the accepted industry by aggregating the feature quantities of each extracted user (step S43). The control unit 11 refers to the payment DB 144, card DB 145, and merchant DB 146 to extract the feature quantities of the user group that made payments in the area corresponding to the location information of the selected store information from the feature quantities of each identified user (step S44).

[0173] The control unit 11 calculates the average value of the features of the extracted user group for each industry, thereby calculating the average value of the features of the user group that made a payment in the area corresponding to the location information of the selected store information for each industry (step S45). The control unit 11 identifies the store opening score using the average features of the user group that used the accepted industry, the standard deviation of the features of the user group that used the accepted industry, and the average value of the features of the user group that made a payment in the area corresponding to the location information of the selected store information (step S46). After step S46, the control unit 11 returns the subroutine.

[0174] According to Embodiment 3, the information processing system can accept the industry for which store development is to be conducted and output a store opening score for the accepted industry based on the acquired credit card data.

[0175] According to Embodiment 3, the information processing system can identify feature quantities based on the usage history of each user in each industry of the acquired credit card data, and can identify a store opening score based on the similarity between the average feature quantity of the group of users who used the accepted industry and the feature quantity of the group of users who made payments in the corresponding area.

[0176] The matters described in each of the embodiments described above can be combined with one another. Furthermore, the independent claims and dependent claims described in the claims can be combined with one another in any combination, regardless of the form of reference. In addition, although the claims use a form in which claims referencing two or more other claims (multi-claim form), the claims are not limited to this. A form in which multi-claims referencing at least one multi-claim (multi-multi-claim) may also be used.

[0177] The embodiments disclosed herein should be considered in all respects to be illustrative and not restrictive. The scope of the invention is indicated by the claims, not in the sense described above, and all modifications within the sense and scope equivalent to the claims are intended. [Explanation of Symbols]

[0178] 10. Information Processing Equipment (Server) 11 Control Unit 12 Storage section 12P control program 12M language model 13 Communications Department 14 Mass storage 141 Accuracy Information Database 142 Verification Information Database 143 Store Information Database 144 Payment Database 145 Card DB 146 Member store DB 147 People flow information DB 148 Road section DB 15 Reading section 1a Portable storage medium 20. Information Processing Equipment (Computers) 21 Control Unit 22 Memory section 22P Control Program 23 Communications Department 24 Display section 25 Input section N Network 30 prompts 31 Property information column 32 Generation instruction field d1 Display screen d10 Object field d20 Store Information Section d2 display screen d3 display screen d30 Map section d40 Credit card data field d31 Industry field d32 Reference period field d33 Store Score Section O1 First Object O2 Second Object O3 Third Object O4 4th object O5 Fifth Object O6, Object 6 O7, Object 7

Claims

1. We obtain information on properties that are potential candidates for store development. By providing the acquired property information to the language model, candidate store information is generated by extracting item data for each item. Based on the accuracy information stored for each provider of acquired property information, it is determined whether or not to output "checking" information indicating that the item data is being verified. If it is determined that the aforementioned "under review" information should be output, the newly generated store information and the aforementioned "under review" information will be output together with the already generated group of store information. A program that instructs a computer to perform a process.

2. We obtain information on properties that are potential candidates for store development. By providing the acquired property information to the language model, candidate store information is generated by extracting item data for each item. If the first object is selected, the newly generated store information and the already generated store information will be output in a list, and the status of each store information, including new, under consideration, application in progress, viewing, negotiation under consideration, or sold, will be displayed in a corresponding color. If any of the objects provided for each status, different from the first object, is selected, the newly generated store information matching the corresponding status and the already generated store information will be output in a list. A program that instructs a computer to perform a process.

3. We obtain information on properties that are potential candidates for store development. By providing the acquired property information to the language model, candidate store information is generated by extracting item data for each item. The newly generated store information is output along with the already generated store information set. Obtain credit card data, Based on the acquired credit card data, the system outputs the payment population, payment volume, payment gender ratio, payment age ratio, and income ratio for the area corresponding to the location information of the selected store. Based on the aforementioned credit card data, a graph is generated comparing the payment rate in the aforementioned area by industry with the payment rate nationwide. A program that instructs a computer to perform a process.

4. By acquiring pedestrian flow information corresponding to location information, The system outputs a map of the area corresponding to the location information of the selected store, and also outputs pedestrian flow information for the area based on the acquired pedestrian flow information. The program according to any one of claims 1 to 3.

5. We accept applications for the types of businesses targeted for store development. Based on the aforementioned credit card data, the store opening score for the aforementioned industry is output. The program according to claim 3.

6. Based on the usage history of each user in the aforementioned credit card data, by industry, feature quantities are identified. The store opening score is determined based on the similarity between the average feature vector of the user group that used the accepted business category and the feature vector of the user group that made a payment in the aforementioned area. The program according to claim 5.

7. We obtain information on properties that are potential candidates for store development. By providing the acquired property information to the language model, candidate store information is generated by extracting item data for each item. Based on the accuracy information stored for each provider of acquired property information, it is determined whether or not to output "checking" information indicating that the item data is being verified. If it is determined that the aforementioned "under review" information should be output, the newly generated store information and the aforementioned "under review" information will be output together with the already generated group of store information. Let the computer perform the process. Information processing methods.

8. An information processing device comprising a control unit, The control unit, We obtain information on properties that are potential candidates for store development. By providing the acquired property information to the language model, candidate store information is generated by extracting item data for each item. Based on the accuracy information stored for each provider of acquired property information, it is determined whether or not to output "checking" information indicating that the item data is being verified. If it is determined that the aforementioned "under review" information should be output, the newly generated store information and the aforementioned "under review" information will be output together with the already generated group of store information. Information processing device.

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