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

Abstract

To visualize the evaluation of individual activities and the activities of locations visited by individuals. [Solution] An information processing apparatus having a processor that executes a program and a storage device that stores the program, wherein the storage device stores a directed graph comprising a plurality of nodes indicating an individual and a location that the individual can visit, a first directed edge from the plurality of nodes indicating a first relationship between the individual and the location, and a second directed edge from the plurality of nodes indicating a second relationship between the individuals by the connection of two nodes from the plurality of nodes indicating two of the individuals, and the processor executes a calculation process that calculates an evaluation index value to evaluate at least one of the first relationship and the second relationship based on the directed graph, and an output process that outputs the calculation result from the calculation process.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and an information processing program for processing information.

Background Art

[0002] In local cities, shopping districts have become "shutter streets", leading to the decline of central urban areas and the increasing dilution of real human relationships among residents. For this reason, revitalization of shopping districts as bases for local economies and community spaces for residents has become an issue to be addressed.

[0003] Regarding the regeneration process of shopping districts, there is a model in which stores with a sense of crisis and initiative play a central role, the number of peer stores around them increases through open dialogue, and once a turning point is crossed, social relationships between the shopping district and residents are synergistically fostered, enabling the central urban area including the shopping district to develop sustainably. This model was analyzed through experience in shopping district revitalization activities, and while the relationships between stores and residents, among residents, and between stores hold important keys, the analysis remains only qualitative.

[0004] Patent Document 1 below discloses a referral system. This referral system includes a member customer management unit that registers only customers who have used physical stores as store members, a referral information generation unit that causes a store terminal device to display a setting screen for freely setting the type and content of referral success rewards and usage benefits, and generates referral information in accordance with the input content, a my page generation unit that generates a my page on which only referral information issued by member stores where the referrer is registered as a store member is displayed in a list for each store, and causes the my page to be displayed on a referrer terminal device, and a store visit processing unit that, when a referred person presents a store visit processing code at a physical store and store visit processing is performed, executes at least the process of granting the referral success reward.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

[0006] The referral system described in Patent Document 1 is intended to provide benefits, and therefore does not consider the relationship between the referrer and stores other than the store where the referrer became a member, the relationship between the referrer and an unspecified person, or the relationship between stores. In order to revitalize shopping districts, as the revitalization model shows, it is necessary to collect and evaluate relationship data regarding the relationship between stores and customers, the relationship between customers and referrers and those who are referred, and the relationship between stores, but currently, sufficient relationship data has not been collected.

[0007] The present invention aims to visualize the evaluation of individual activities and the activities of locations visited by individuals. [Means for solving the problem]

[0008] An information processing device that represents one aspect of the invention disclosed in this application is an information processing device having a processor that executes a program and a storage device that stores the program, wherein the storage device stores a directed graph comprising a plurality of nodes indicating an individual and a location that the individual can visit, a first directed edge from the plurality of nodes indicating a first relationship between the individual and the location, and a second directed edge from the plurality of nodes indicating a second relationship between the individuals by the connection of two nodes from the plurality of nodes indicating two of the individuals, and the processor is characterized in that it performs a calculation process to calculate an evaluation index value that evaluates at least one of the first relationship and the second relationship based on the directed graph, and an output process that outputs the calculation result from the calculation process. [Effects of the Invention]

[0009] According to a typical embodiment of the present invention, it is possible to visualize the evaluation of an individual's activities and the activities of the locations visited by that individual. Other issues, configurations, and effects not mentioned above will be clarified by the following description of the embodiments. [Brief explanation of the drawing]

[0010] [Figure 1] Figure 1 is a block diagram showing an example of the system configuration of an information processing system. [Figure 2] Figure 2 is a sequence diagram showing an example of a referral processing procedure in an information processing system. [Figure 3] Figure 3 is an explanatory diagram showing an example of operation and processing on the referrer's terminal. [Figure 4] Figure 4 is an explanatory diagram showing an example of information display on the referred person's terminal (step S210). [Figure 5] Figure 5 is a sequence diagram showing an example of a customer visit processing procedure in an information processing system. [Figure 6] Figure 6 is an explanatory diagram showing an example of a store visit history database. [Figure 7] Figure 7 is an explanatory diagram showing an example of a referral history database. [Figure 8] Figure 8 is a flowchart showing an example of an evaluation process procedure executed by an information processing device. [Figure 9] Figure 9 is an explanatory diagram showing an example of the first relationship data. [Figure 10] Figure 10 is an explanatory diagram showing an example of second-level relationship data. [Figure 11] Figure 11 is an explanatory diagram showing an example of a directed graph generation (date and time ta1). [Figure 12] Figure 12 is an explanatory diagram showing an example of the generation of a directed graph (date and time tb1). [Figure 13] Figure 13 is an explanatory diagram showing an example of the generation of a directed graph (date and time tb2). [Figure 14] Figure 14 is an explanatory diagram showing an example of a directed graph generation (date and time tx). [Figure 15] Figure 15 is an explanatory diagram showing example calculation result 1. [Figure 16] Figure 16 is an explanatory diagram showing example calculation result 2. [Figure 17] Figure 17 is an explanatory diagram showing example calculation result 3. [Figure 18]Figure 18 is an explanatory diagram illustrating Calculation Result Example 4. [Figure 19] Figure 19 is an explanatory diagram illustrating Calculation Result Example 5. [Figure 20] Figure 20 is an explanatory diagram illustrating an example of another directed graph. [Figure 21] Figure 21 is an explanatory diagram illustrating an adjacency matrix of the directed graph in Figure 20. [Figure 22] Figure 22 is an explanatory diagram illustrating a distance matrix calculated based on the adjacency matrix in Figure 21. [Figure 23] Figure 23 is an explanatory diagram illustrating Calculation Result Example 6. [Figure 24] Figure 24 is an explanatory diagram illustrating a community division example. [Figure 25] Figure 25 is an explanatory diagram illustrating Calculation Result Example 7. [Figure 26] Figure 26 is an explanatory diagram illustrating Output Screen Example 1. [Figure 27] Figure 27 is an explanatory diagram illustrating Output Screen Example 2. [Figure 28] Figure 28 is an explanatory diagram illustrating Output Screen Example 3. [Mode for Carrying Out the Invention]

[0011] <Figure 1 System Configuration Example> Figure 1 is a block diagram illustrating a system configuration example of an information processing system. The information processing system 100 includes an information processing apparatus 110, a database 130, and terminals 140 and 141. The information processing apparatus 110, the database 130, and the terminals 140 and 141 are communicatively connected via a network 150 such as the Internet, a LAN (Local Area Network), or a WAN (Wide Area Network).

[0012] The information processing apparatus 110 is a computer including a processor 111, a storage 112, a memory 113, a user interface 114, and a network interface 115.

[0013] The information processing device 110 communicates with the database 130, terminals 140 and 141 via the network interface 115 and the network 150.

[0014] The processor 111 executes the program read from the storage 112 into the memory 113. Specifically, for example, the processor 111 performs calculations using the data acquisition program 121, the network generation program 122, the network analysis program 123, and the result output program 124, and outputs the evaluation results using the result output program 124. The details and procedures of the calculation process will be described later.

[0015] [Program Group] Storage 112 stores a group of programs, such as data acquisition program 121 to result output program 124. Data acquisition program 121 is a program that acquires data from terminal 140 and extracts relational data. Network generation program 122 is a program that generates a directed graph as a network consisting of nodes and edges from the relational data.

[0016] The network analysis program 123 is a program that analyzes the generated directed graph based on evaluation metrics. The results output program is a program that outputs the calculation results from the network analysis program 123 to a display device, which is an example of the user interface 114, or transmits them from the network interface 115 via the network 150 to the display device of terminal 141 so that they can be displayed.

[0017] The processing results of the program group are stored in storage 112, for example, in table format. The processing results of the program group are also stored in database 130 via network 150 from network interface 115, for example, in table format.

[0018] [Store Information DB125] Furthermore, storage 112 stores store information DB (database) 125. Store information DB 125 includes, for example, store ID (identification), store name, and location information. Of the store ID, store name, and location information of a store, at least the store ID is referred to as the store information of that store. Store information DB 125 may be stored in database 130.

[0019] The store ID is an identifier that uniquely identifies the store. The store name is the name of the store. Location information is information that indicates the location of the store, such as address or location information (latitude and longitude). Location information may also include map data that includes the store's location.

[0020] In this embodiment, we will use a store as an example, but it is not limited to stores. For example, it could be a city hall, ward office, school, library, park, museum, art gallery, observation deck, tower, amusement park, zoo, aquarium, historical building, or even a natural tourist attraction such as a mountain or waterfall. Stores, facilities, and natural tourist attractions are collectively referred to as "locations."

[0021] [Personal information DB126] Furthermore, storage 112 stores personal information DB 126. Personal information DB 126 includes, for example, the personal ID (identification), personal name, contact address, point information, referred store information, visitable store information, and referred person information of the owner of terminal 140. Personal information DB 126 may also be stored in database 130.

[0022] Personal ID is identification information that uniquely identifies an individual. Personal Name is the individual's name (for example, full name or handle name). Contact Address is set on the individual's terminal 140 and is an address used for communication with the information processing device 110 and other people's terminals 140, and can be an email address, SNS (Social Networking Service) ID, or messaging app account.

[0023] Point information refers to information about points awarded to each individual. Points are reward values ​​that are awarded to and can be used by individuals. For example, points are awarded by the information processing device 110 when an individual visits a store, when an individual refers a store to someone else, and when an individual visits a store referred by someone else. Points can be used, for example, for preferential treatment at shopping street events, discounts on goods and services, and the gift of benefits. When points are used in this way, the number of points is deducted accordingly. The number of points is the total value of the points. Point information also includes the history of points awarded, the history of points used, and the latest number of points.

[0024] The referred store information is the store information of a store that an individual was referred to by another person (referred store). In addition to the store information of the referred store, the referred store information includes, for example, the referrer's personal ID and name, the referred person's personal ID and name, the date and time of the referral (referral date and time), and a message from the referrer.

[0025] The list of stores an individual can visit contains information about the stores they are able to visit (stores that can be visited). For example, this list includes information about all stores registered in the store information database (DB125). For stores that an individual has visited, a flag indicating that the store has been visited is set in the list of stores that can be visited.

[0026] Referred person information is the personal information of another person who can be referred to a store. Referred person information can be added, modified, and deleted by operating terminal 140.

[0027] [Visit History DB127] Furthermore, storage 112 stores the store visit history DB 127. The store visit history DB 127 is a database that stores store visit history, showing when and which individual visited which store. Details of the store visit history DB 127 will be described later.

[0028] [Referral History DB128] Furthermore, storage 112 stores the referral history DB 128. The referral history DB 128 is a database that stores referral history, showing when and to whom each individual referred which store. Details of the referral history DB 128 will be described later.

[0029] The storage 112, memory 113, and database 130 are non-temporary or temporary recording media that store various programs and data. Examples include ROM (Read Only Memory), RAM (Random Access Memory), SSD (Solid State Drive), and HDD (Hard Disk Drive). The storage 112 and memory 113 are collectively referred to as storage devices.

[0030] The user interface 114 consists of, for example, a display, keyboard, mouse, touch panel, speaker, microphone, camera, and sensor, and includes at least one of these. The user interface 114 displays input data or calculation processing results.

[0031] The network interface 115 is connected to a network 150 consisting of the Internet or a communication line, and transmits and receives various types of data between it and the database 130 and the terminal 140.

[0032] [Terminals 140, 141] Terminals 140 and 141 have a configuration similar to that of the information processing device 110, and may be, for example, a smartphone, a tablet or notebook personal computer, or a client terminal. Furthermore, terminals 140 and 141 may have a GPS receiver, and the location information of terminal 140 may be calculated using data received by the GPS receiver from GPS satellites.

[0033] Furthermore, terminals 140 and 141 may implement a camera and a two-dimensional code recognition program such as QR Code (registered trademark). The camera captures images of the two-dimensional code. Terminals 140 and 141 analyze (image processing and decoding) the two-dimensional code captured by the camera using the two-dimensional code recognition program and obtain the data embedded in the two-dimensional code.

[0034] Terminal 140 implements a referral application. The referral application is application software that manages store visits and store referrals. Through the referral application, Terminal 140 acquires data on individuals visiting stores, data on individuals who have referred stores to others, and data on individuals who have been referred to stores (referred persons) visiting stores. The acquired data is collectively referred to as relationship data. Terminal 140 transmits this relationship data to the information processing device 110 via the network 150 through the referral application.

[0035] In this case, the information processing device 110 acquires this relationship data using the data acquisition program 121, calculates the evaluation result using the network generation program 122, the network analysis program 123, and the result output program 124, and outputs the evaluation result using the result output program 124.

[0036] Terminal 141 sends and receives data to and from the information processing device 110 via the network 150 in order to operate the information processing device 110. For example, an analyst uses terminal 141 to access the information processing device 110.

[0037] <Figure 2 Introduction Process> Figure 2 is a sequence diagram showing an example of a referral process in the information processing system 100. In Figure 2, the terminal 140 of the individual who is the referrer 200A is referred to as referrer terminal 140A, and the terminal 140 of the individual who is referred 200B is referred to as referred terminal 140B. If referrer 200A and referrer terminal 140A are not distinguished, they are simply referred to as "individual." Similarly, if referrer terminal 140A and referred terminal 140B are not distinguished, they are simply referred to as "terminal 140."

[0038] (Steps S201~S203) The referrer terminal 140A performs the following: selection of the referring store and the person to be referred (step S201), generation of referral data (step S202), and transmission of the referral data (step S203). Steps S201 to S203 are explained using Figure 3.

[0039] [Figure 3: Example of operation and processing on referrer terminal 140A] Figure 3 is an explanatory diagram showing an example of operation and processing on the referrer terminal 140A. When terminal 140 launches the referral app, the main screen 300 is displayed on terminal 140. In Figure 3, the main screen 300 is displayed on the referrer terminal 140A as an example. The main screen 300 has a point display unit 301, a referred store selection unit 302, and a referral store display unit 303.

[0040] The points display unit 301 displays the most recent number of points awarded to the individual by the information processing device 110.

[0041] The referred store selection unit 302 is a user interface that displays a list of store names that the individual has been referred to by others (referred stores) and allows the user to select a referred store name from that list.

[0042] The store referral display unit 303 is a user interface that displays a list of store names that the individual can refer to others (referred stores) and allows the user to select a store name from the list. In the example in Figure 3, the store name "eeeee" is shown as selected.

[0043] Specifically, for example, when terminal 140 launches the referral app, it sends a data acquisition request to information processing device 110. Upon receiving the data acquisition request, information processing device 110 retrieves the latest available store information from store information DB 125, and retrieves the individual's latest point balance, latest referred store information, and latest referred person information from personal information DB 126. Information processing device 110 then sends the updated data thus acquired to the user's terminal 140.

[0044] With this update data, terminal 140 updates the number of points stored in the memory device with the latest number of points included in the update data, adds the latest referred store information included in the update data to the referred store information stored in the memory device, and updates the available store information stored in the memory device with the latest available store information included in the update data.

[0045] As a result, the latest point balance is displayed on the point balance display unit 301 of the main screen 300 of the terminal 140. In addition, a list of store names of the latest referred store information is displayed on the referred store selection unit 302. The terminal 140 may exclude referred store information for which the "visited" flag has been set from this list. In addition, a list of store names of the latest visitable store information is displayed on the referred store display unit 303. The terminal 140 may also display store information for which the "visited" flag has been set higher up in this list.

[0046] In step S201, when the referrer terminal 140A receives a selection of a store name displayed on the referral store display unit 303 by the referrer 200A, the referrer terminal 140A transitions from the main screen 300 to the referral screen 310.

[0047] The referral screen 310 includes a referral store information display unit 311, a referral list selection display unit 312, a message input unit 313, and a send button 314.

[0048] The store information display unit 311 displays store information. Store information refers to the store information of the store selected from the store information display unit 303 on the main screen 300. The displayed information includes at least the store name and location information, among the store ID, store name, and location information. The referrer terminal 140A may also display a map of the area surrounding the location information in cooperation with a map server that provides map information. In the example in Figure 3, the state in step S201 where the store information for the selected store name "eeeee" is displayed is shown.

[0049] The referral list selection display unit 312 is a user interface that displays a list of referrals and accepts the selection of referrals to be introduced to the store from this list. Figure 3 shows the state in step S201 where "P" and "Q" have been selected as referrals. Referral "P" is referred to as referral 200B in Figure 2.

[0050] The message input unit 313 is a user interface that accepts and displays arbitrary string inputs from the operator.

[0051] The transmit button 314 is a user interface that accepts data transmission to the information processing device 110 based on the operator's input.

[0052] In step S202, when the press of the send button 314 is detected, the referrer terminal 140A generates referral data. The referral data includes, for example, the date and time when the press of the send button 314 was detected (referral date and time), the store ID of the store name "eeeee" displayed on the referral store information display unit 311, the personal ID of the referrer 200A, the personal IDs of the referred persons "P" and "Q" selected on the referred person list selection display unit 312, and the message, which is a string entered on the send message input unit 313.

[0053] In step S203, the referrer terminal 140A transmits the referral data generated in step S202 to the information processing device 110.

[0054] (Step S204) In Figure 2, the information processing device 110 receives referral data transmitted from the referrer terminal 140A in step S203.

[0055] (Step S205) The information processing device 110 performs a data update based on the referral data received in step S204. The data update includes updating the personal information DB 126 and updating the referral history DB 128.

[0056] Regarding the updating of the personal information database 126, specifically, for example, the information processing device 110 identifies the personal ID of the referrer 200A and the personal IDs of the referred persons "P" (200B) and "Q" included in the referral data.

[0057] The information processing device 110 adds a predetermined number of points corresponding to the store's referral to the latest point count in the referrer 200A's point information. The information processing device 110 also identifies the personal IDs of the referred persons "P" (200B) and "Q" included in the referral data. The information processing device 110 adds a predetermined number of points corresponding to the store's referral to the latest point count in the point information of each referred person "P" (200B) and "Q".

[0058] Furthermore, the information processing device 110 adds the personal ID of the referrer 200A, the store information of the store referred by referrer 200A, the date and time of the referral, and a message to the store information of each referred person "P" (200B) and "Q".

[0059] Specifically, when updating the referral history DB128, for example, the information processing device 110 adds the referral date and time included in the referral data, the store ID of the store referred by referrer 200A, the personal ID of referrer 200A, and the personal IDs of the referred persons "P" (200B) and "Q".

[0060] (Step S206) The information processing device 110 retrieves the contact addresses of the referred persons "P" (200B) and "Q" from the personal information DB 126. The information processing device 110 sets the retrieved contact addresses as destination addresses and sends a referral notification to the respective terminals 140 of the referred persons "P" (200B) and "Q". The referral notification informs the referred persons "P" (200B) and "Q" that they have received a store referral from the referrer.

[0061] (Step S207) For example, the referred person 200B operates the referred person terminal 140B in response to the referral notification and launches the referral application. When the referral application is launched by the referred person 200B's operation, the referred person terminal 140B sends a data acquisition request to the information processing device 110 via the referral application.

[0062] (Step S208) When the information processing device 110 receives the data acquisition request in step S207, it performs the acquisition of updated data. Specifically, for example, as described in Figure 3, the information processing device 110 acquires the latest available store information from the store information DB 125, and acquires the latest point count, latest referred store information, and latest referred person information of the referred person 200B from the personal information DB 126.

[0063] (Step S209) The information processing device 110 transmits the data acquired in step S208 as update data to the referred person terminal 140B of the referred person 200B.

[0064] (Step S210) The referred terminal 140B receives the update data transmitted in step S209.

[0065] (Step S211) When the referred terminal 140B receives the update data transmitted in step S210, it performs a data update. Specifically, for example, the referred terminal 140B updates the number of points held in its memory device with the latest number of points included in the update data. The referred terminal 140B also adds the latest referred store information to the referred store information held in its memory device. Furthermore, the referred terminal 140B updates the information of stores that can be visited, held in its memory device, with the latest information of stores that can be visited included in the update data.

[0066] (Step S212) The referred user terminal 140B performs information display. This information display is explained in detail in Figure 4.

[0067] <Figure 4 Information display on the referred person's terminal 140B (step S210)> Figure 4 is an explanatory diagram showing an example of information display (step S210) on the referred terminal 140B. In Figure 4, for example, the latest point count updated in step S210 is displayed in the point count display unit 301 of the main screen 300 of the referred terminal 140B, the store name of the latest referred store information updated in step S210 is displayed in the referred store selection unit 302, and the store name of the latest visitable store information updated in step S210 is displayed in the referred store display unit 303.

[0068] Figure 4 shows, as an example, the state in the store selection unit 302 where the store name "bbbbb" has been selected by the referred person 200B.

[0069] When the store name "bbbbb" is selected for the store to be referred, the referred terminal 140B displays the referred screen 400. The referred screen 400 includes a referred store information display unit 401, a referrer information display unit 402, a received message display unit 403, and a browse back button 404.

[0070] The referred store information display unit 401 displays the referred store information. In the example in Figure 4, the referred store information is the store information for the referred store named "bbbbb" selected in the referred store selection unit 302 on the main screen 300.

[0071] The referrer information display unit 402 displays referrer information. The referrer information displayed in the referrer information display unit 402 is, for example, the personal name of referrer 200A.

[0072] The received message display unit 403 displays the received message. The received message is the message from referrer 200A included in the latest referred store information received in step S210, that is, the message entered in the sent message input unit 313.

[0073] The browse back button 404 is a user interface that returns to the main screen 300 when an action by the referred person 200B is detected.

[0074] The referred person's terminal 140B can prompt the referred person 200B to go to the referred store "bbbbb" by displaying the referred person screen 400.

[0075] <Figure 5: Store Visit Processing> Figure 5 is a sequence diagram showing an example of a store visit processing procedure in the information processing system 100. Store visit processing is information processing performed between the terminal 140 and the information processing device 110.

[0076] (Step S501) The referred user terminal 140B detects a store visit. The stores targeted for store visit detection are not limited to whether they are the referred store or not. Store visit detection methods include, for example, store visit detection D1 using location information, store visit detection D2 by acquiring store information at the visited store, and store visit detection D3 by payment at the visited store. When a store visit is detected, the referred user terminal 140B sets information indicating that the store has been visited in the store visitable store information in its storage device. The information indicating that the store has been visited may also include information indicating the store visit detection method that was executed (any of the above store visit detection methods D1 to D3).

[0077] [Store visit detection using location information D1] Terminal 140 is set to a state where it can acquire current location information. The referred person's terminal 140B uses the referral app to determine whether the current location information matches the store's location information or if the difference is within a predetermined acceptable range. If the current location information matches the store's location information or if the difference is within a predetermined acceptable range, terminal 140 uses the referral app to detect that the individual has visited the store.

[0078] This allows the terminal 140 to automatically detect an individual's visit to a store without the individual having to operate the terminal 140. Therefore, even if the individual is unaware that the store they visited is one of the stores listed in the list of stores that can be visited, the terminal 140 will still detect the visit, improving the convenience of store visit detection.

[0079] [Store visit detection D2 by acquiring store information at visited stores] The store displays a 2D code containing the store's information. An individual visits the store and uses the camera of their terminal 140 to scan the 2D code displayed in the store. The terminal 140 analyzes the 2D code (image processing and decoding) and obtains the store's information embedded in the 2D code.

[0080] Terminal 140 determines whether the store information acquired by the referral app matches the information of stores that can be visited stored in the memory device. If a match is determined, terminal 140 detects that the individual has visited the store using the referral app. Thus, the individual's visit is detected when they actually visit the store.

[0081] [Store visit detection D3 based on payment at the visited store] Terminal 140 is equipped with a payment application that allows for payment when purchasing goods or receiving services at stores registered in the store information DB 125, and also allows for the acquisition of store information for those stores.

[0082] An individual visits a store, purchases goods or receives services at that store, makes a payment using the payment app on terminal 140, and obtains the store's information. The obtained store information includes at least one of the following: the store ID, store name, and store location information.

[0083] Terminal 140 determines whether the store information of the store acquired by the referral app matches the information of stores that can be visited stored in the memory device. If a match is determined, the referred person's terminal 140B detects that the individual has visited the store using the referral app.

[0084] As a result, terminal 140 can detect an individual's visit to a store when a payment is made, enabling store visit detection even if the terminal is unaware that the store visited is one of the stores listed in the list of stores that can be visited. Furthermore, since terminal 140 does not need to acquire current location information, it is possible to reduce the power consumption of terminal 140 compared to store visit detection D1 which uses location information.

[0085] (Step S502) The referred user terminal 140B generates visit data for the visited store detected in step S501. The visit data includes the date and time of the visit, the store ID of the visited store (visited store ID), and the personal ID of the individual who visited the store. The date and time of the visit is the date and time the visit was detected in step S501. The visit data may also include information (any of D1 to D3) indicating the method of visit detection that was performed.

[0086] (Step S503) The referred customer terminal 140B transmits the store visit data generated in step S502 to the information processing device 110.

[0087] (Step S504) The information processing device 110 receives the store visit data transmitted in step S503.

[0088] (Step S505) The information processing device 110 performs a data update based on the store visit data received in step S504. Specifically, for example, the information processing device 110 adds the store visit data to the store visit history DB 127. The information processing device 110 also updates the individual's point count in the personal information DB 126 by adding the points earned as a reward for the individual's store visit to the individual's point count. The information processing device 110 may also add the points earned as a reward for the individual's store visit to the individual's point count depending on the information indicating the store visit detection method.

[0089] Furthermore, the information processing device 110 refers to the referral history DB 128 to determine whether the store visited by the individual is a store that was referred by another person. If the visited store is a store that was referred, the information processing device 110 records in the referral history DB 128 information indicating that the individual (referred person 200B) visited that store. In this case, the information processing device 110 may also update the referrer's (200A) point count in the personal information DB 126 by adding the points for the reward received for the referred person's (200B) visit to the store.

[0090] <Figure 6 Store Visit History DB127> Figure 6 is an explanatory diagram showing an example of the store visit history DB127. The store visit history DB127 has the following fields: store visit date and time 601, store visit ID 602, visitor ID 603, and store visit detection method 604. The value of store visit date and time 601 is the store visit date and time included in the store visit data. The value of store visit ID 602 ​​is the store visit ID included in the store visit data. The value of visitor ID 603 is the personal ID included in the store visit data. The value of store visit detection method 604 is information (one of D1 to D3) indicating the store visit detection method that was executed, included in the store visit data.

[0091] In the following descriptions of databases or tables, the value of AAbbb (where AA is the field name and bbb is the code) may be written as AAbbb. For example, the value ta1 for visit date and time 601 may be written as visit date and time ta1.

[0092] <Figure 7 Referral History DB128> Figure 7 is an explanatory diagram showing an example of the referral history DB128. The referral history DB128 has the following fields: referral date and time 701, referring store ID 702, referrer ID 703, referred person ID 704, and referred person's store visit 705.

[0093] The value of Referral Date and Time 701 is the date and time (referral date and time) included in the referral data received in step S204. The value of Referral Store ID 702 is the store ID included in the referral data received in step S204. The value of Referrer ID 703 is the personal ID of Referrer 200A included in the referral data received in step S204. The value of Referred Person ID 704 is the personal ID of Referred Person 200B included in the referral data received in step S204.

[0094] The value for "Visited Stores by Referred Person 705" indicates whether the individual with Referred Person ID 704 (Referred Person 200B) visited the store with Referred Store ID 702, which was referred by the individual with Referrer ID 703 (Referrer 200A). "0" is set if the store has not been visited, and "1" if it has been visited. The default value is "0" for unvisited stores.

[0095] The information processing device 110 refers to the referral history DB 128 and the store visit history DB 127 to set the value of the referred person's store visit 705. Specifically, for example, the information processing device 110 identifies an entry in the referral history DB 128 where the combination of the values ​​of the store visited ID 602 ​​and the visitor ID 603 matches the combination of the values ​​of the referring store ID 702 and the referred person ID 704. Once the information processing device 110 identifies such an entry, it updates the value of the referred person's store visit 705 in that entry from "0" to "1".

[0096] <Figure 8 Evaluation Process> Figure 8 is a flowchart showing an example of an evaluation process procedure performed by the information processing device 110.

[0097] (Step S801) The information processing device 110 obtains the evaluation period set from the user interface 114 using the data acquisition program 121. Specifically, for example, the information processing device 110 obtains the evaluation period by accepting input of the evaluation period through the operation of an input device, which is an example of the user interface 114. Alternatively, the information processing device 110 obtains the evaluation period by receiving it via the network interface 115 through the operation of a terminal 140. In step S801, the information processing device 110 may also obtain the store ID of the store to be evaluated and the personal ID of an individual by operating an input device.

[0098] (Step S802) The information processing device 110 extracts relationship data from the store visit history DB 127 and the referral history DB 128 within the evaluation period, which was acquired in step S801, using the data acquisition program 121. Note that if the store ID of the store being evaluated or the personal ID of an individual has been acquired in step S801, the relationship data will be limited to that evaluation target. The relationship data will now be explained.

[0099] [Figure 9: First Relationship Data] Figure 9 is an explanatory diagram showing an example of the first relationship data. The first relationship data 900 is data that shows the relationship between a store and an individual, and the relationship between individuals. The first relationship data 900 has the fields Date and Time 901, Source 902, and Target 903. The value of Date and Time 901 is either the value of the store visit date and time 601 or the referral date and time 701 obtained in step S801 within the evaluation period. The value of Source 902 is the node to which the start of an edge is connected in the directed graph. The value of Source 902 is either the value of the store visit ID 602 ​​or the individual ID (either the visitor ID 603, referrer ID 703, or referred person ID 704). The value of Target 903 is the node to which the end of an edge is connected in the directed graph.

[0100] When the information processing device 110 extracts a combination of values ​​for visit date and time 601, visited store ID 602, and visitor ID 603 from the visit history DB 127, it sets that combination of values ​​as the combination of values ​​for date and time 901, source 902, and target 903, and also sets that combination of values ​​as the combination of values ​​for date and time 901, target 903, and source 902.

[0101] For example, when entry 611 (visit date and time 601: ta1, visited store ID 602: s1, visitor ID 603: i1) is extracted from the visit history DB127 shown in Figure 6, entry 911 (date and time 901: ta1, source 902: s1, target 903: i1) is set in the first relationship data 900, and entry 912 (date and time 901: ta1, source 902: i1, target 903: s1) is also set.

[0102] Furthermore, when the information processing device 110 extracts a combination of values ​​for referral date and time 701, referrer ID 703, and referred person ID 704 from the referral history DB 128, it sets that combination of values ​​as the combination of values ​​for date and time 901, source 902, and target 903.

[0103] For example, when entry 711 (referral date and time 701: tb1, referrer ID 703: i1, referred person ID 704: i2) is extracted from the referral history DB128 shown in Figure 7, entry 913 (date and time 901: tb1, source 902: i1, target 903: i2) is set in the first relationship data 900.

[0104] Similarly, when entry 712 (introduction date and time 701: tb1, referrer ID 703: i1, referred person ID 704: i3) is extracted from the referral history DB128 shown in Figure 7, entry 914 (date and time 901: tb1, source 902: i1, target 903: i3) is set in the first relationship data 900.

[0105] Furthermore, when entry 612 (visit date and time 601:ta2, visited store ID 602:s1, visitor ID 603:i3) is extracted from the visit history DB127 shown in Figure 6, entry 915 (date and time 901:ta2, source 902:s1, target 903:i3) and entry 916 (date and time 901:ta2, source 902:i3, target 903:s1) are set in the first relationship data 900.

[0106] Furthermore, since store s1, which individual i3 visited, was introduced by individual i1 on the introduction date and time tb1 (entry 712), this visit results in entry 917 (date and time 901: ta2, source 902: i3, target 903: i1) being set in the first relationship data 900. Entry 917 is generated to indicate that individual i3 responded to individual i1's introduction by visiting store s1, which was introduced by individual i1, the introducer.

[0107] [Figure 10 Second Relationship Data] Figure 10 is an explanatory diagram showing an example of second relationship data. Second relationship data 1000 is data that shows the relationship between source 902 and target 903. Second relationship data 1000 has the fields source 902, target 903, and aggregation count 1001. The value of aggregation count 1001 is the aggregated value of the number of entries for combinations of values ​​of source 902 and target 903.

[0108] In other words, the aggregation count 1001 indicates the number of times a store has been visited, the number of times a store has been referred, or the number of times a store has been referred. For example, if the value of source 902 is the visited store ID 602, then the value of aggregation count 1001 is the number of times a store has been visited. For example, entry 1011 indicates that store s1 was visited n1 times by individual i1.

[0109] If the value of source 902 is referrer ID 703 and the value of target 903 is visited store ID 602, then the value of aggregation count 1001 is the number of visits. For example, entry 1012 indicates that individual i1 visited store s1 n1 times.

[0110] If the value of source 902 is referrer ID 703 and the value of target 903 is referred person ID 704, then the value of aggregation count 1001 is the number of referrals. For example, entry 1013 indicates that individual i1 referred individual i2 to stores n2 times (in other words, n2 different types of stores), and entry 1014 indicates that individual i1 referred individual i3 to stores n3 times (in other words, n3 different types of stores).

[0111] If the value of source 902 is referred person ID 704 and the value of target 903 is referrer ID 703, then the value of aggregation count 1001 is the number of times the referred person visited the store. For example, entry 1015 indicates that individual i3 visited the store (whether the same store or different stores) n5 times in response to a referral from individual i1.

[0112] Furthermore, if the combination of Source 902 and Target 903 is an individual, there may be cases where both parties refer each other, so the total count 1001 will be the sum of the number of referrals and the number of times the referred person visited the store.

[0113] (Step S803) Returning to Figure 8, the information processing device 110 executes a generation process to generate a directed graph using the network generation program 122. Specifically, for example, the information processing device 110 uses the network generation program 122 to refer to the first relationship data 900, making the source 902 and target 903 nodes, and generates directed edges between the source 902 and target 903 to generate a directed graph, which is then stored in the storage device. Specifically, for example, the information processing device 110 generates the nodes and directed edges in chronological order of the date and time 901, from oldest to newest. In the first relationship data 900, the date and time 901 is oldest with the date and time ta1 of the top entry 911, and becomes newer as you go down.

[0114] [Figure 11 Example of a directed graph generation (date and time ta1)] Figure 11 is an explanatory diagram showing an example of the generation of a directed graph (date and time ta1). The information processing device 110 uses entry 911 to generate a directed edge e(s1,i1) connecting source s1 and target i1. The directed edge e(x,y) (where x is source 902 and y is target 903) is a directed edge with source x as the start and target y as the end. Similarly, the information processing device 110 uses entry 912 to generate a directed edge e(i1,s1) connecting source i1 and target s1.

[0115] At date and time ta1, the nodes s1 and i1, along with the directed edges e(s1,i1) and e(i1,s1), form a directed graph G(ta1).

[0116] [Figure 12 Example of a directed graph generation (date and time tb1)] Figure 12 is an explanatory diagram showing an example of the generation of a directed graph (date and time tb1). Date and time tb1 is a newer date and time than date and time ta1. The information processing device 110 uses entry 913 to generate a directed edge e(i1,i2) connecting source i1 and target i2. Similarly, the information processing device 110 uses entry 914 to generate a directed edge e(i1,i3) connecting source i1 and target i3.

[0117] At time tb1, the directed graph G(tb1) is constructed by nodes s1, i1-i3, and directed edges e(s1,i1), e(i1,s1), e(i1,i2), and e(i1,i3).

[0118] [Figure 13 Example of a directed graph generation (date and time tb2)] Figure 13 is an explanatory diagram showing an example of the generation of a directed graph (date and time tb2). Date and time tb2 is a date and time newer than dates and times ta1 to ta3. The information processing device 110 uses entry 915 to generate a directed edge e(s1,i3) connecting source s1 and target i3. The information processing device 110 uses entry 916 to generate a directed edge e(i3,s1) connecting source i3 and target s1. The information processing device 110 uses entry 917 to generate a directed edge e(i3,i1) connecting source i3 and target i1. The information processing device 110 uses entry 918 to generate a directed edge e(s2,i2) connecting source s2 and target i2. The information processing device 110 uses entry 919 to generate a directed edge e(i2,i2) connecting source i2 and target i2. The information processing device 110 uses the entry 920 to generate a directed edge e(i3,i4) that connects the source i3 and the target i4.

[0119] Using entries 911-917 up to date ta2, a directed graph G(ta2) is constructed with nodes s1, i1-i3 and directed edges e(s1,i1), e(i1,s1), e(i1,i2), e(i1,i3), e(s1,i3), and e(i3,s1).

[0120] Using entries 911-919 up to date ta3, a directed graph G(ta3) is constructed with nodes s1, i1-i3 and directed edges e(s1,i1), e(i1,s1), e(i1,i2), e(i1,i3), e(s1,i3), e(i3,s1), e(s2,i2), and e(i2,i2).

[0121] Using entries 911-920 up to date tb2, a directed graph G(tb2) is constructed with nodes s1, s2, i1-i4 and directed edges e(s1,i1), e(i1,s1), e(i1,i2), e(i1,i3), e(s1,i3), e(i3,s1), e(i3,i1), e(s2,i2), e(i2,i2), and e(i3,i4).

[0122] Thus, directed edges e(x,y) include bidirectional directed edges between an individual and a store indicating that the individual visited the store, directed edges indicating that an individual (referrer) referred a store to another individual (referred person), and directed edges from the referred person to the referrer indicating that the referred person responded to the inquiry when they visited the store referred by the referrer.

[0123] [Figure 14 Example of a directed graph generation (date and time tx)] Figure 14 is an explanatory diagram showing an example of the generation of a directed graph (datetime tx). Datetime tx is the latest datetime 901. The information processing device 110 generates a directed graph G(tx) by repeatedly executing the processes shown in Figures 11 to 13 until the datetime tx entry of the first relationship data 900. Note that when the directed graphs G(ta1), G(tb1), G(ta2), G(ta3), G(tb2), and G(tx) described above are not distinguished, they are referred to as directed graph G.

[0124] (Step S804) Returning to Figure 8, the information processing device 110 executes a calculation process to calculate evaluation index values ​​using the network analysis program 123. Evaluation index values ​​are values ​​that evaluate the network structure of the directed graph G. The evaluation index includes a first evaluation index that evaluates the overall structure of the directed graph G, and a second evaluation index that evaluates the nodes within the directed graph G.

[0125] The first evaluation metrics include the number of nodes, number of directed edges, graph diameter (or radius), graph distance, graph density, link efficiency, reciprocity, homogeneity, and clustering coefficient of the directed graph G.

[0126] A large number of nodes is proportional to the number of individuals who make and receive referrals, and the number of stores visited and referred. A large number of directed edges is proportional to the number of stores visited and the number of referrals.

[0127] Graph diameter is the longest distance (eccentricity) from each node to the furthest node that can be reached via a directed edge in a directed graph G. Graph radius is the shortest distance (eccentricity). A larger graph diameter or graph radius indicates a greater chain of referrals, where the referred person becomes a referrer, introducing the store to others, who then become referrers themselves, introducing the store to yet another individual.

[0128] Graph density is an indicator of how many directed edges exist in a directed graph G. Specifically, for example, it is the ratio of directed edges to the number of directed edges where all nodes in a directed graph G are connected by directed edges. A higher graph density indicates a graph that is closer to a complete graph. In other words, it indicates the number of referrals an individual makes to others or the number of visits an individual makes to a store.

[0129] Link efficiency is proportional to the sum of the reciprocals of the eccentricity and is an index that evaluates the ease of referring a store from one person to another and the ease of visiting a store. A higher link efficiency indicates a directed graph G that is easier to refer and visit.

[0130] Reciprocity is an index in a directed graph G that indicates the number of bidirectional directed edges between two nodes. When an individual visits a store, the individual's node and the store's node are connected by bidirectional directed edges. Also, when two individuals recommend a store to each other, the nodes of those two individuals are connected by bidirectional directed edges. Furthermore, when one individual recommends a store to another individual, and that other individual visits the store, the nodes of those two individuals are connected by bidirectional directed edges (see entry 917). Higher reciprocity indicates a higher number of store visits and greater intimacy between individuals.

[0131] Homogeneity is an index value that indicates the correlation between two nodes in a directed graph G. A higher homogeneity indicates a higher correlation between nodes in the directed graph G as a whole. For example, if both nodes are stores, it indicates that the same individual has visited the store many times. If both nodes are individuals, it indicates that the same individual has referred the store many times. If one node is an individual and the other is a store, it indicates that the person who referred that store has visited the store many times.

[0132] The clustering coefficient is an index that indicates the actual number of relationships between nodes relative to the number of possible relationships between them. A higher clustering coefficient indicates that the groups of nodes in a directed graph G are more closely related. In other words, a higher clustering coefficient indicates that an individual has visited many stores, that an individual has recommended stores to others more often, and that those who were recommended have visited stores recommended by the referrer.

[0133] Thus, the first evaluation indicator value shows how often individuals visit a store, or how individuals refer the store to other individuals, indicating their relationships with others.

[0134] Secondary evaluation metrics include centrality and similarity. Centrality is an index value that indicates the importance of nodes and directed edges within a directed graph G. Specifically, for example, centrality includes degree centrality, betweenness centrality, and adjacent centrality. Degree centrality includes out-degree centrality and in-degree centrality. Similarity is an index value that indicates the similarity between two specific nodes within a directed graph G, and is calculated using cosine similarity or the Jaccard coefficient.

[0135] The higher the out-degree centrality, the more the node (source 902) is perceived to have a large number of individual visitors if it is a store, or a large number of store visits and a large number of people referred by that individual if it is an individual.

[0136] The higher the in-degree centrality, the more the node (target 903) has been visited by a large number of individuals (including referrers and referred individuals) if it is a store, or by a large number of referrers who referred the store to that individual if it is an individual.

[0137] The higher the betweenness centrality, the more the node is indicated by the number of visits by both referrers and referred individuals (for stores), and the more the number of stores visited, the number of individuals who referred stores, and the number of individuals who referred stores (for individuals including both referrers and referred individuals).

[0138] The higher the proximity centrality, the more likely that node is to be visited by both referrers and referred individuals (in the case of a store), or by individuals (including both referrers and referred individuals) who have visited a large number of stores, referred stores, and received referrals from other individuals.

[0139] In other words, the second evaluation index value is an index value that shows the visit status of a specific store by the referrer and the referred person, or the number of times an individual has referred another person or the number of times they have accepted a referral.

[0140] The information processing device 110 calculates at least one of these evaluation index values. The evaluation index value to be adopted is predetermined and can be changed by operating an input device, which is an example of a user interface 114.

[0141] In step S804, the first evaluation index value is calculated to represent the store visit status of the referrer or referred person (the relationship between the store and the visitor), for example, as the number of bidirectional directed edges connected to each store's node, or the sum of the 1001 counts of bidirectional directed edges. For example, if a store is connected to 5 individuals (referrers and referred persons) by bidirectional directed edges, the number of bidirectional directed edges is 5. Also, if the number of visits by these 5 individuals (i.e., the counts of 1001) are 1, 2, 3, 4, and 5 respectively, the first evaluation index value representing the store visit status is 15.

[0142] The relationship between the referrer and the referred person is evaluated using a first evaluation metric, such as the number of directed edges, the sum of 1001 directed edge counts, graph distance, reciprocity, or similarity. For example, if the sum of 1001 directed edge counts is used to represent the relationship between the referrer and the referred person, and the referrer introduces 3 stores to the referred person, the first evaluation metric representing the relationship between the referrer and the referred person would be 3. As for the relationships between stores, since stores are not directly connected by edges, a second evaluation metric is calculated using, for example, graph distance or similarity.

[0143] Furthermore, when the information processing device 110 calculates the graph distance (graph diameter or graph radius) as the first evaluation index, it may calculate it by setting the weight of the directed edge to 1 (as an unweighted graph) without using the aggregation count 1001. Alternatively, the information processing device 110 may use the aggregation count 1001 or its converted value as the weight of the directed edge to calculate the graph distance as a weighted directed graph G.

[0144] In the latter weighted directed graph G, the weight of a directed edge is interpreted as distance, so the weight of a directed edge and the count of 1001 (the frequency of the relationship) are inversely related. For this reason, instead of using the count of 1001 directly, the weight of a directed edge is, for example, the reciprocal of the count of 1001 (however, numerical operations such as adding an integer value of 1 or more to the denominator so that the denominator is not 0) or a predetermined value minus the count of 1001.

[0145] (Step S805) The information processing device 110, using the result output program 124, performs output processing to output the calculation result from step S804 in a displayable format. Specifically, for example, the information processing device 110 displays the calculation result on a display, which is an example of the user interface 114. Furthermore, if the information processing device 110 receives the evaluation period via the network interface 115 through an operation of the terminal 140, it sends the calculation result to the terminal 140 via the network interface 115 for display on the terminal 140. This completes the series of processes.

[0146] <Figure 15 Example of calculation result 1> Figure 15 is an explanatory diagram showing example calculation result 1. Calculation result 1500 is an example of a calculation result using the number of nodes and the number of edges of a directed graph G as the first evaluation indicator. Specifically, for example, calculation result 1500 is a graph showing the change over time in the number of nodes and the number of edges of a directed graph G. The horizontal axis of the graph is time, and the vertical axis is the number of nodes and the number of edges.

[0147] The horizontal axis, from Phase 1 to Phase 4, represents the period corresponding to date and time 901. Phase 1 is the period from the start date and time of the evaluation period to date and time 901: "ta1". Phase 2 is the period from date and time 901: "ta1" to date and time 901: "tb1". Phase 3 is the period from the period up to date and time 901: "tb1" to date and time 901: "tb2". Phase 4 is the period from date and time 901: "tb2" to date and time 901: "tx" (end date and time of the evaluation period) (and so on).

[0148] The calculation result of 1500 shows that the number of edges (relationships between stores and visitors, and relationships between visitors and between referrers and referred individuals) is increasing more than the number of nodes (number of stores and number of individuals). Note that in Figure 15, the calculation result of 1500 includes both the number of nodes and the number of edges, but it may also include either the number of nodes or the number of edges.

[0149] <Figure 16 Example of calculation result 2> Figure 16 is an explanatory diagram showing example calculation result 2. Calculation result 1600 is an example of a calculation result using the graph density, cluster coefficient, and reciprocity of a directed graph G as the first evaluation indicators. Specifically, for example, calculation result 1600 is a graph showing the changes over time in the graph density, cluster coefficient, and reciprocity of a directed graph G. The horizontal axis of the graph is time, and the vertical axis is graph density, cluster coefficient, and reciprocity.

[0150] The calculation result 1600 shows that, excluding the special Phase 1 which consists of only one store and one individual, graph density, cluster coefficient, and reciprocity increase from Phase 2 to Phase 4. Note that in Figure 16, the calculation result 1600 includes graph density, cluster coefficient, and reciprocity, but it may include at least one of these three.

[0151] The calculation results of 1500 in Figure 15 and 1600 in Figure 16 both show a similar increasing trend. However, the calculation result of 1600 in Figure 16 is calculated with a maximum value of 1, which is useful, for example, when comparing shopping streets of different sizes.

[0152] Furthermore, the calculation results of 1500 in Figure 15 and 1600 in Figure 16 are data calculated for the overall structure of the directed graph G, and can be said to be data that comprehensively evaluates the relationship between stores and visitors, the relationship between visitors and between referrers and referred persons, and the relationship between stores.

[0153] <Figure 17 Example of calculation result 3> Figure 17 is an explanatory diagram showing example calculation result 3. Calculation result 1700 is an example of a calculation result using the number of bidirectional directed edges in a directed graph G as the first evaluation index. Specifically, for example, calculation result 1700 is a graph showing the change over time in the number of bidirectional directed edges in a directed graph G. The horizontal axis of the graph is time, and the vertical axis is the number of bidirectional directed edges, or the total value of the aggregation counts of those directed edges (1001).

[0154] In calculation result 1700, store visit status represents the number of directed edges between stores and individuals, or the total number of aggregations of those directed edges (1001), while relationships represent the number of directed edges between individuals, or the total number of aggregations of those directed edges (1001). It can be seen that both store visit status and relationships increase from Phase 1 to Phase 4. Note that in Figure 17, calculation result 1700 includes both store visit status and relationships, but it may also include either store visit status or relationships.

[0155] <Figure 18 Example of calculation result 4> Figure 18 is an explanatory diagram showing calculation result example 4. Calculation result 1800 is the adjacency matrix of the directed graph G(tx) in Figure 14. In the matrix component ajk of calculation result 1800, if its value is "1", it indicates a directed edge e(j,k) from source j to target k, and if its value is "0", it indicates that there is no directed edge e(j,k). The store visit status in Phase 4 in calculation result 1700 is the sum of components 1801 and 1802 enclosed by the dotted line in calculation result 1800. The human relationships in Phase 4 in calculation result 1700 are the sum of components 1803 enclosed by the dotted line in calculation result 1800.

[0156] <Figure 19 Example of calculation result 5> Figure 19 is an explanatory diagram showing calculation result example 5. Calculation result 1900 is an example of a calculation result using the graph distance and similarity of two nodes as the second evaluation indicator. Specifically, for example, calculation result 1900 is a graph showing the change over time in the graph distance and similarity between store s1 and store s2. The horizontal axis of the graph is time, and the vertical axis is graph distance and similarity.

[0157] In phases 1 and 2, there is no data because only store s1 exists. From phase 3 to phase 4, the graph distance decreases (the relationship between store s1 and store s2 becomes closer) and the similarity increases (the relationship between the later store s2 catches up with that of store s1). Note that in Figure 19, the calculated result 1900 includes the graph distance and similarity of the two nodes, but it could also be either the graph distance or similarity of the two nodes.

[0158] In step S805, calculation results 1500 to 1900 will be output. In this way, as the phases progress, it is possible to visualize the time-series relationship between stores and visitors, the time-series relationship between visitors and between referrers and referred persons, and the time-series relationship between stores, based on the evaluation indicator values.

[0159] Thus, this embodiment makes it possible to visualize the time-series relationship between stores and customers, the time-series relationship between individuals and those referred, or the time-series relationship between stores. This makes it possible to understand the situation in the shopping district and evaluate the effectiveness of revitalization measures by comparing the situation before and after the measures are implemented.

[0160] Specifically, for example, by visualizing the revitalization status of a shopping street over time, it becomes possible to evaluate the effectiveness of revitalization measures by comparing the situation before and after a phase if measures are implemented between phases. In other words, it becomes possible to quantitatively verify shopping street revitalization models, which were previously limited to qualitative evaluations. For the sake of explanation, the above example uses a small-scale example with 2 stores and 4 people, but it is possible to calculate and evaluate similarly even in the case of a larger scale. The following explanation will be given using Figure 20.

[0161] <Figure 20: Examples of other directed graphs> Figure 20 is an explanatory diagram showing an example of another directed graph. Directed graph G(ty) is a directed graph of datetime ty consisting of stores s1~s3 (3 stores) and individuals i1~i10 (10 people). Each node is assigned a store ID or individual ID, and each directed edge is assigned a value of 1001 for the total number of visits, referrals, or referrals. Directed graph G(ty) is output in step S208.

[0162] <Figure 21 Adjacency Matrix> Figure 21 is an explanatory diagram showing the adjacency matrix of the directed graph G(ty) in Figure 20. In adjacency matrix 2100, the matrix component ajk represents the aggregation count of 1001. The individual store visit status is calculated by the sum of components 2101 and 2102 (edge ​​aggregation count of 1001), enclosed by the dotted line, and its value is "102". The relationship between individuals is calculated by the sum of components 2103 (edge ​​aggregation count of 1001), enclosed by the dotted line, and its value is "100". Adjacency matrix 2100 is output in step S208. By comparing these values ​​over time or before and after revitalization measures, it is possible to contribute to the regeneration of the shopping street.

[0163] <Figure 22 Distance Matrix> Figure 22 is an explanatory diagram showing the distance matrix calculated based on the adjacency matrix 2100 in Figure 21. Here, the information processing device 110 sets the reciprocal of the adjacency matrix 2100 component ajk (count count 1001), 1 / ajk, as the weight of the directed edge e(j,k) and calculates the distance matrix 2200. The component bjk of the distance matrix 2200 represents the graph distance from node j to node k. Regarding the relationships between stores, the graph distance between store s1 and store s2 is smallest at 0.45 (closest relationship), followed by the graph distance between store s2 and store s3 at 0.58, and the graph distance between store s1 and store s3 at 0.7. For example, a measure to link store s1 and store s3 is considered effective for revitalizing the shopping street. The distance matrix 2200 is output in step S208.

[0164] In Figure 22, in addition to the relationships between stores, we can also individually determine the relationships between stores and individuals, and the relationships between individuals. Since the relationship between a store and an individual is a bidirectional directed edge, the components bjk and bkj are equal. In the relationship between individuals, the components bjk and bkj are not necessarily equal. For example, the component b(i3)(i5) = 0.83 and the component b(i5)(i3) = 0.5, indicating that the relationship from individual i5 to individual i3 is closer than the relationship from individual i3 to individual i5.

[0165] <Figure 23 Example of calculation result 6> Figure 23 is an explanatory diagram showing example calculation result 6. Figure 23 shows Table 2300, which is the calculation result of evaluation index values ​​calculated based on the adjacency matrix 2100 in Figure 21. Similar to Figure 22, Table 2300 is the calculation result of evaluation index values ​​for evaluating the overall structure of the weighted directed graph G(ty), by setting the reciprocal of the adjacency matrix 2100 component ajk as the weight of the directed edge e(j,k). Table 2300 is output in step S208.

[0166] Similar to the calculation results 1500 in Figure 15 and 1600 in Figure 16, comparing the values ​​in Table 2300 over time from date 901 to ty can be useful in considering revitalization measures.

[0167] <Figure 24 Community Division> Figure 24 is an explanatory diagram showing an example of community partitioning. For the weighted directed graph Gw(ty) obtained by converting the 1001 counts of aggregation of the directed graph G(ty) in Figure 20 into weights of directed edges e(j,k), the information processing device 110 performs partitioning so that within the same community, nodes are connected by directed edges with as many nodes as possible and close distances (weights of directed edges e(j,k)), and between communities, nodes are connected by directed edges with few nodes. The community partitioning result is output in step S208.

[0168] The weighted directed graph Gw(ty) can be seen to be divided into three communities C1-C3, each centered around a store s1, s2, and s3, and a community C4 consisting of individuals i2, i3, and i4. For example, measures connecting communities C1-C4 are considered effective for revitalizing a shopping street. Note that community division may also be performed for each phase (date and time). This will yield community division results for each phase (date and time).

[0169] <Figure 25 Example of calculation result 7> Figure 25 is an explanatory diagram showing calculation result example 7. Figure 25 shows the calculation result 2500 for centrality index values ​​for stores and individuals. Calculation result 2500 is a graph with stores s1-s3 and individuals i1-i10 on the horizontal axis and the centrality index value on the vertical axis. In calculation result 2500, it can be seen that the approach centrality of individuals i7 and i10 is relatively large, and the betweenage centrality of individuals i7 and i9 is large. Referring also to Figure 24, it can be seen that individuals i7 and i10 (and community C1 including store s1 and individual i5) occupy important positions within the whole, and that individuals i7 and i9 are key people connecting communities C1 and C3. Calculation result 2500 is output in step S208.

[0170] Thus, by displaying the weighted directed graph Gw(ty) and the calculation result 2500, which take into account the number of aggregations shown in Figure 24, it becomes possible to evaluate the relationship between stores and visitors, the relationship between visitors, the relationship between referrers and referred individuals, and the relationship between stores, in conjunction with the evaluation index values. Furthermore, by using community segmentation graphs and centrality indicators, it becomes easier to grasp the current state of the shopping district.

[0171] <Figure 26 Example of output screen 1> Figure 26 is an explanatory diagram showing example output screen 1. The output screen 2600 includes a start date and time display unit 2601, an end date and time display unit 2602, a store list setting unit 2603, a personal list setting unit 2604, an output format selection unit 2605, an execution button 2606, a calculation result display unit 2607, and a file output button 2608.

[0172] The start date and time display unit 1201 displays the start date and time of the evaluation period obtained in step S801. The end date and time display unit 1202 displays the end date and time of the evaluation period obtained in step S801.

[0173] The store list setting unit 2603 is a user interface that displays a list of stores to be evaluated by operating an input device, and accepts the input of store IDs of stores to be evaluated into the store list by operating an input device, thereby setting the store list.

[0174] The personal list setting unit 2604 is a user interface that displays a list of individuals to be evaluated based on the operation of an input device, and accepts the input of the personal IDs of the individuals to be evaluated into the personal list based on the operation of the input device, thereby setting the personal list.

[0175] The output format selection unit 2605 is a user interface that accepts the selection of the output format of the calculation results displayed in the calculation result display unit 2607. The output formats that can be selected include the directed graph G shown in Figures 11 to 14 and Figure 20, the adjacency matrix shown in Figures 18 and 21, the distance matrix shown in Figure 22, and the community partitioning graph shown in Figure 24.

[0176] The execution button 2606 is a user interface that accepts the execution of steps S802 to S805 in Figure 8 for the evaluation period and the stores and individuals to be evaluated, based on the operation of the input device.

[0177] The calculation result display unit 2607 is a display area that displays the calculation result output in step S805 in the output format selected in the output format selection unit 2605. The calculation result in the calculation result display unit 2607 is displayed in step S208.

[0178] The file output button 2608 is a user interface that outputs the calculation result output in step S805 to a file based on the operation of the input device.

[0179] <Figure 27 Example of output screen 2> Figure 27 is an explanatory diagram showing example output screen 2. The output screen 2700 includes a start date and time display unit 1201, an end date and time display unit 2602, an interval setting unit 2703, an interval / cumulative setting unit 2704, a first evaluation index setting unit 2705, an execution button 2606, a calculation result display unit 2607, and a file output button 2608.

[0180] The interval setting unit 2703 is a user interface that accepts the setting of the time interval of the evaluation period by operating an input device.

[0181] The interval / cumulative setting unit 2704 is a user interface that accepts settings via an input device to determine whether the evaluation index value is calculated on an interval-by-interval basis or cumulatively within the time intervals of the evaluation period set by the interval setting unit 2703.

[0182] The first evaluation index setting unit 2705 is a user interface that accepts the setting of the first evaluation index through operation of an input device. When displaying the calculation result 1500 shown in Figure 15, the number of nodes and the number of edges of the directed graph G are set as the first evaluation index. When displaying the calculation result 1600 shown in Figure 16, the graph density, cluster coefficient, and reciprocity of the directed graph G are set as the first evaluation index.

[0183] When displaying the calculation result 1700 shown in Figure 17, the number of bidirectional directed edges in the directed graph G is set as the first evaluation metric. When displaying the calculation result 1900 shown in Figure 19, the graph distance and similarity between the two stores are set as the first evaluation metrics. In this case, the first evaluation metric setting unit 2705 also accepts the setting of the store IDs of the two stores to be evaluated by the information processing device 110.

[0184] By displaying the first evaluation indicator, the revitalization process of the shopping district can be evaluated over time.

[0185] <Figure 28 Example of output screen 3> Figure 28 is an explanatory diagram showing example output screen 3. The output screen 2800 includes a start date and time display unit 2601, an end date and time display unit 2602, a store list setting unit 2603, a personal list setting unit 2604, a second evaluation index setting unit 2805, an execution button 2606, a calculation result display unit 2607, and a file output button 2608.

[0186] The second evaluation index setting unit 2805 is a user interface that accepts the setting of the second evaluation index through operation of an input device. When displaying the calculation result 2500 shown in Figure 25, approach centrality and betweenness centrality are set as the second evaluation index.

[0187] By displaying various calculation results in this way, it becomes possible to evaluate shopping district revitalization measures and verify regeneration models.

[0188] As described above, according to this embodiment, the information processing device 110 can collect relationship data regarding the relationship between stores and customers, the relationship between customers and those referred, and the relationship between stores. Therefore, the information processing device 110 can visualize the situation of a shopping street, which is a collection of stores, by generating a directed graph G from the relationship data and calculating evaluation index values. This makes it possible for analysts to evaluate the effectiveness of shopping street revitalization measures.

[0189] It should be noted that the present invention is not limited to the embodiments described above, but includes various modifications and equivalent configurations within the spirit of the attached claims. For example, the embodiments described above are described in detail to make the present invention easier to understand, and the present invention is not necessarily limited to having all of the described configurations. Furthermore, some of the configurations of one embodiment may be replaced with those of another embodiment. Furthermore, some of the configurations of one embodiment may be added to those of another embodiment. Furthermore, some of the configurations of each embodiment may be added, deleted, or replaced with other configurations.

[0190] Furthermore, each of the aforementioned configurations, functions, processing units, and processing means may be implemented in hardware, for example, by designing them as integrated circuits, or they may be implemented in software by having a processor interpret and execute programs that realize each function.

[0191] Information such as programs, tables, and files that implement each function can be stored in memory, hard disks, SSDs (Solid State Drives), or on recording media such as IC (Integrated Circuit) cards, SD cards, and DVDs (Digitally Versatile Discs).

[0192] Furthermore, the control lines and information lines shown are those deemed necessary for explanation purposes and do not necessarily represent all control lines and information lines required for implementation. In reality, it can be assumed that almost all components are interconnected. [Explanation of Symbols]

[0193] 100 Information Processing Systems 110 Information Processing Device 111 processors 112 storage 121 Data Acquisition Program 122 Network Generation Program 123 Network Analysis Program 124 Result Output Program 125 Store Information Database 126 Personal information DB 127 Store Visit History Database 128 Referral History Database 130 Databases 140 devices G directed graph

Claims

1. An information processing apparatus having a processor for executing a program and a storage device for storing the program, The storage device stores a directed graph comprising: a plurality of nodes indicating an individual and a location that the individual can visit; a first directed edge from the plurality of nodes indicating a first relationship between the individual and the location; and a second directed edge from the plurality of nodes indicating a second relationship between the individuals through the connection of two nodes representing two of the individuals. The aforementioned processor, A calculation process that calculates an evaluation index value for evaluating at least one of the first relationship and the second relationship based on the directed graph, An output process that outputs the calculation result obtained by the calculation process described above, An information processing device characterized by performing the following actions.

2. An information processing apparatus according to claim 1, The first directed edge includes the number of visits the individual has made to the location. The second directed edge includes the number of times one individual introduced the location to the other individual, or the number of times the other individual who was introduced to the location visited the location. An information processing device characterized by the following:

3. An information processing apparatus according to claim 1, The storage device stores multiple directed graphs with different dates and times. In the calculation process described above, the processor calculates the evaluation index value for each of the plurality of directed graphs, In the output processing, the processor outputs information showing the change over time of the evaluation index value for each date and time. An information processing device characterized by the following:

4. An information processing apparatus according to claim 1, The aforementioned evaluation index value is a first evaluation index value relating to the visit status of the individual to the aforementioned location. An information processing device characterized by the following:

5. An information processing apparatus according to claim 1, The aforementioned evaluation index value is a first evaluation index value relating to the relationship between the individual who introduced the location to another individual, or the relationship between the other individual who visited the location after being introduced to it. An information processing device characterized by the following:

6. An information processing apparatus according to claim 1, The aforementioned evaluation index value is a second evaluation index value related to the visitor status to a specific location. An information processing device characterized by the following:

7. An information processing apparatus according to claim 1, The aforementioned evaluation index value is a second evaluation index value relating to the number of people referred by a specific individual to another individual, or the number of times the referred individual visited the aforementioned location. An information processing device characterized by the following:

8. An information processing apparatus according to claim 1, The memory device stores a visit history database showing which locations the individual has visited, and a referral history database showing which locations the individual has referred to other individuals. The aforementioned processor, A generation process that generates the directed graph by referring to the visit history database and the referral history database, and stores the directed graph in the storage device, An information processing device characterized by performing the following actions.

9. An information processing apparatus according to claim 8, In the generation process, the processor generates the directed graph by referring to the visit history database and connecting the node representing the individual and the node representing the location visited by the individual with the first directed edge in both directions. An information processing device characterized by the following:

10. An information processing apparatus according to claim 8, In the generation process, the processor generates the directed graph by referring to the referral history database and connecting a first node representing the individual and a second node representing other individuals who referred the location to the individual with a second directed edge indicating the direction from the first node to the second node. An information processing device characterized by the following:

11. An information processing apparatus according to claim 10, In the generation process, the processor generates the directed graph by referring to the referral history database and connecting the second node and the third node, which indicates the location where the other individual was referred and visited, with the first directed edge in both directions. An information processing device characterized by the following:

12. An information processing apparatus according to claim 10, In the generation process, the processor refers to the referral history database and generates the directed graph by connecting the second directed edge, which indicates the direction from the second node to the first node, when the other individual who was referred to the location visits the location. An information processing device characterized by the following:

13. An information processing apparatus according to claim 3, The memory device stores a visit history database showing when and where the individual visited, and a referral history database showing when and where the individual referred other individuals to different locations. The aforementioned processor, A generation process that generates multiple directed graphs with different dates and times by referring to the visit history database and the referral history database, and stores the multiple directed graphs in the storage device, An information processing device characterized by performing the following actions.

14. An information processing method performed by an information processing apparatus having a processor for executing a program and a storage device for storing the program, The storage device stores a directed graph comprising: a plurality of nodes indicating an individual and a location that the individual can visit; a first directed edge from the plurality of nodes indicating a first relationship between the individual and the location; and a second directed edge from the plurality of nodes indicating a second relationship between the individuals through the connection of two nodes representing two of the individuals. The aforementioned processor, A calculation process that calculates an evaluation index value for evaluating at least one of the first relationship and the second relationship based on the directed graph, An output process that outputs the calculation result obtained by the calculation process described above, An information processing method characterized by performing the following.

15. An information processing program that causes a processor to perform information processing, The processor has access to a storage device that stores a directed graph comprising: a plurality of nodes indicating an individual and a location that the individual can visit; a first directed edge from the plurality of nodes indicating a first relationship between the individual and the location; and a second directed edge from the plurality of nodes indicating a second relationship between the individuals through the connection of two nodes representing two of the individuals. The aforementioned processor, A calculation process that calculates an evaluation index value for evaluating at least one of the first relationship and the second relationship based on the directed graph, An output process that outputs the calculation result obtained by the calculation process described above, An information processing program characterized by causing the execution of [a specific action].

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