Program, method, information processing device, and system

Abstract

There is a problem in that it is not possible to identify other user groups (potential customers) that are similar to the typical behavioral patterns (representative behavioral data) of a first user group related to a specific theme or event. [Solution] A program to be executed by a computer having a processor and a memory unit, the program executing the following steps: a behavior acquisition step in which the processor acquires behavioral data regarding the behavior of multiple users; a first extraction step in which, based on the behavioral data acquired in the behavior acquisition step, a first user group consisting of one or more users from the multiple users who are related to a specified theme; a representative acquisition step in which representative behavior data representative of the first user group extracted in the first extraction step; and a second extraction step in which, based on the representative behavior data of the first user group, a second user group consisting of one or more users from the multiple users.

Description

[Technical field]

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

[0002] Techniques for delivering online targeted advertisements are known. Patent Document 1 proposes a method of performing clustering using a user's movement history and preference information, and analyzing people's hobbies, preferences, behavioral patterns, and the like. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-197460 A Summary of the Invention [Problem to be solved by the invention]

[0004] Due to the decline in the domestic population and the diversification of people's lifestyles, hobbies and tastes, appropriate marketing activities that capture customer personas are more important than ever before, but data-based persona analysis and marketing activities based on the analysis results each require a high level of expertise, and the growing momentum for personal information protection, including the abolition of third-party cookies, makes it difficult to carry out individual tracking-type marketing that assumes the collection of large amounts of personal data across businesses.As a result, there is a need for marketing methods that match big data that has been statistically converted into various formats that can capture customer personas with personal data that can be obtained within the first party of a website, but there is an issue in that it is not possible to identify other user groups (potential customers) that are similar to the typical behavioral patterns (representative behavioral data) of the first user group related to a specific theme or event. Therefore, the present disclosure has been made to solve the above-mentioned problems, and its purpose is to provide a technology for identifying other user groups (potential customers) that are similar to the typical behavioral patterns (representative behavioral data) of a first user group related to a specific theme or event, thereby capturing a more accurate image of customers. [Means for solving the problem]

[0005] A program to be executed by a computer having a processor and a memory unit, the program executing the following steps: a behavior acquisition step in which the processor acquires behavioral data regarding the behavior of a plurality of users; a first extraction step in which, based on the behavioral data acquired in the behavior acquisition step, a first user group consisting of one or more users related to a predetermined theme from among the plurality of users; a representative acquisition step in which representative behavior data representative of the first user group extracted in the first extraction step; and a second extraction step in which, based on the representative behavior data of the first user group, a second user group consisting of one or more users from the plurality of users. Effect of the Invention

[0006] According to the present disclosure, it is possible to identify other user groups (potential customers) that are similar to the typical behavioral patterns (representative behavioral data) of a first user group related to a particular theme or event, and to identify user groups from user behavioral data. [Brief description of the drawings]

[0007] [Figure 1] FIG. 2 is a block diagram showing the functional configuration of the system 1. [Diagram 2] FIG. 2 is a block diagram showing the functional configuration of the server 10. [Diagram 3] 2 is a block diagram showing the functional configuration of a user terminal 20. FIG. [Figure 4] 2 is a block diagram showing a functional configuration of an administrator terminal 30. FIG. [Diagram 5] FIG. 13 is a diagram showing the data structure of a user table 1012. [Figure 6]FIG. 13 is a diagram showing the data structure of a behavior table 1013. [Figure 7] FIG. 13 is a diagram showing the data structure of a theme table 1014. [Figure 8] FIG. 13 is a diagram showing the data structure of a cluster table 1015. [Figure 9] FIG. 13 is a diagram showing the data structure of an advertisement table 1021. [Figure 10] 13 is a flowchart showing an operation of a cluster process. [Figure 11] 13 is a flowchart showing an operation of a user extraction process. [Figure 12] 13 is a flowchart showing an operation of an advertisement distribution process. [Figure 13] 11 is a screen example showing the operation of an advertisement distribution process. [Figure 14] FIG. 2 is a block diagram showing the basic hardware configuration of a computer 90. [Figure 15] FIG. 1 is a diagram showing the relationship between a certain spot as a starting point and spots before and after the certain spot. [Figure 16] This is a diagram showing the relationship between each spot taking into account the whole. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings. In all the drawings explaining the embodiment, the same reference numerals are given to common components, and repeated explanations are omitted. Note that the following embodiment does not unduly limit the contents of the present disclosure described in the claims. In addition, not all of the components shown in the embodiment are essential components of the present disclosure. In addition, each figure is a schematic diagram and is not necessarily illustrated strictly.

[0009] <System 1 Configuration> The system 1 in the present disclosure is an information processing system that extracts a predetermined user group based on behavioral data of users. The system 1 includes information processing devices, namely, a server 10, a user terminal 20, and an administrator terminal 30, which are connected via a network N. The administrator terminal 30 is not essential. FIG. 1 is a block diagram showing the functional configuration of the system 1. FIG. 2 is a block diagram showing the functional configuration of the server 10. As shown in FIG. FIG. 3 is a block diagram showing the functional configuration of the user terminal 20. As shown in FIG. FIG. 4 is a block diagram showing the functional configuration of the administrator terminal 30. As shown in FIG.

[0010] Each information processing device is configured by a computer equipped with a calculation device and a storage device. The basic hardware configuration of the computer and the basic functional configuration of the computer realized by the hardware configuration will be described later. For each of the server 10, the user terminal 20, and the administrator terminal 30, descriptions that overlap with the basic hardware configuration and basic functional configuration of the computer described later will be omitted.

[0011] The system 1 in the present disclosure can be used as a marketing system / advertising delivery system that analyzes users. Traditional marketing tools that perform user analysis generally do not include ad delivery processing. Marketing tools that perform normal user analysis generally show you what your customers are like. Conventional ad delivery systems do not include a processing unit for performing user analysis on the advertiser side. Conventional ad delivery systems generally only display advertisements to users corresponding to keywords entered by the advertiser. These could not be combined because they use different data. On the other hand, the system 1 of the present disclosure can function as a persona analysis tool and an advertisement delivery system by using location information that is common to users of both the marketing system and the advertisement delivery system and is easy to obtain.

[0012] Until now, when an ad distributor wanted to send information to a group of users, it was common to target users only based on specific behavior. However, this had problems such as a small number of target users, attributes and distribution being biased toward the user extraction point, and the users being specific in their behavior and not strictly speaking the user group that the ad distributor had in mind. For example, if you want to send information to the wealthy by targeting location information, since there is no data that indicates the wealthy user group, you can target users who have checked in to golf courses or luxury hotels, but if you set the condition that they have visited both golf courses and luxury hotels, the number of target users will be reduced, and if you set the condition that they have visited only one of them, the accuracy of the targeting will decrease.In addition, it is not clear from the data whether the wealthy people assumed by the ad distributor visit golf courses and luxury hotels in common. The system 1 disclosed herein can increase the number of subjects, reduce the geographical influence of the sampling location, and refine the user group to be considered to be wealthy.

[0013] <Server 10 Configuration> The server 10 is an information processing device that provides an information processing service for extracting a predetermined user group based on the behavioral data of the users. The server 10 includes a storage unit 101 and a control unit 104 .

[0014] <Configuration of the storage unit 101 of the server 10> The storage unit 101 of the server 10 includes an application program 1011 , a user table 1012 , a behavior table 1013 , a theme table 1014 , a cluster table 1015 , and an advertisement table 1021 .

[0015] The application program 1011 is a program for causing the control unit 104 of the server 10 to function as each functional unit. The application programs 1011 include applications such as a web browser application that runs on a user terminal.

[0016] User table 1012 is a table that stores and manages information about member users (hereinafter, users) who use the service. When a user registers to use the service, the user's information is stored in a new record in user table 1012. This allows the user to use the service according to the present disclosure. The user table 1012 is a table having columns of user IDs and user attribute data, with the user ID as the primary key. FIG. 5 is a diagram showing the data structure of the user table 1012. As shown in FIG.

[0017] The user ID is an item for storing user identification information for identifying a user. The user identification information is an item for which a unique value is set for each user. The user attribute data is an item that stores a character string indicating an arbitrary attribute of the user, such as the user's gender, age, occupation, etc. The user attribute data includes information unique to the user, such as the user's gender, age, etc., that is, unique data (unique information) of the user.

[0018] The behavior table 1013 is a table for storing and managing information related to behavior (behavior information). The behavior table 1013 is a table having columns for user ID, behavior data, and date and time. FIG. 6 is a diagram showing the data structure of the behavior table 1013. As shown in FIG.

[0019] The user ID is an item for storing user identification information for identifying a user. The behavior data is an item that stores information (behavior information) about a user's behavior at a specific date and time. The behavior information includes information about the user's behavior, such as location information, at each of one or more specific dates and times. Specifically, the information on the behavior includes information on the spot indicating the location of the user at a specific date and time. For example, the information on the behavior includes the following information: - Behavioral data includes the user's location information at a specific date and time. Location information includes geographic information such as latitude, longitude, altitude, and polygon data. Location information includes an arbitrary identification code assigned to each mesh-shaped geographical area divided by a specific latitude and longitude. Location information includes addresses and national local government codes assigned to each administrative division of municipalities such as cities, wards, towns, and villages. The behavioral data includes facility information about facilities visited by the user on a specific date and time. Specifically, the facility information includes the names of facilities visited by the user on a specific date and time and the location information of the facilities. For example, the facility information about the facilities may include the names and location information of specific stores such as ●● Ramen Funabashi Ekimae Store, as well as Tokyo Tower, Tokyo Station, Haneda Airport, Tokyo International Exhibition Center, and Ise Shrine. The behavioral data includes information on the type (genre) of facilities visited by the user on a specific date and time. For example, the type of facility may be a train station, airport, shrine, art gallery, museum, etc., as well as information on the type of store, such as a ramen shop or a chain store name. The behavioral data includes information about events that the user visited at a specific date and time. For example, the event information includes the name of the event, such as a live performance by a specific artist A or Comic Market, and location information about the location of the event. · Behavioral data includes the user's facility genre usage score and feature values. The behavior data includes information about the user's behavior before and after the specific date and time (hereinafter referred to as "before and after data") in addition to information about the user's behavior at a specific date and time. The before and after data includes information about the user's behavior before and after a specific event, etc. The date and time is an item for storing the date and time when new behavioral data is stored or updated in the behavior table. Instead of the date and time, character string information indicating a time such as morning, afternoon, or evening, and information such as a time period (1:00, 2:00) may be stored. The behavioral information may be configured to be stored at any time interval or granularity. Information regarding the user's behavior before and after a specific event or the like also includes information regarding which facility genre the user visited before and after visiting (checking in or checking out) a specific facility genre. The feature value is a value compared to the entire user population, and is high when the usage volume of a specific user group is high but not high for the entire user population. On the other hand, general advertising fees are set according to the overall usage volume. By using the feature values ​​of a user group, it is possible to deliver advertisements that select keywords, locations, websites, and web pages that are effective for that user group at a relatively low cost. The entire user population may be all users acquired by the system, or each prefecture or a sampled population may be used as the population. The spot may refer to an information mesh unit that can be expressed by latitude and longitude, polygon information, or a range of radius Xm.

[0020] Based on the behavioral data, the following statistics can be generated: Statistics on the population distribution of the spot (location information, facility information, facility genre, etc.) and the population distribution for each record before and after visiting the spot. By using location information for a specific date and time and the facility genre information that belongs to that location information, statistics on the features of the facility genre and statistics on the features of the facility genre by time, including data before and after the visit, can be obtained. Statistics on relationship values ​​for each spot (combinations of facility genre features x population occupancy rate x population, etc.). Statistics on relationship values ​​(combinations of facility genre feature value x population occupancy rate x population, etc.) for each spot on the schedule. Demographic statistics based on behavioral data other than the data before and after visiting the spot. Statistics on the feature quantities of the facility genre based on behavioral data other than the data before and after visiting the spot. Statistics on relationship values ​​for each spot (values ​​such as a combination of facility genre feature value x population occupancy rate x population) based on behavioral data other than data before and after visiting the spot. Statistics on relationship values ​​for each spot for each time period, excluding data before and after visiting the spot. Multiple statistical information can be created from behavioral data, such as: These can have statistics for the first behavior data, representative behavior data, second behavior data, and clusters.

[0021] It is also possible to create statistics on the behavior before and after visiting a facility category, starting from checking in or checking out of the facility category. For example, when checking in to a hospital, the specific hospital is not taken into consideration. Regarding which facility categories are visited before and after that, the specific facility is not taken into consideration.

[0022] Search information and purchase information can only be obtained from search sites and shopping sites, but location information can be obtained from any website. In other words, there are only a limited number of websites that can match statistical data on search information and purchase information, but statistical data on location information can be matched with any website. In addition, general advertising distribution systems are based on the activity history of users on websites that display advertisements, and cannot take into account user activity in the real world, so they are not suitable for analyzing and attracting users to tourist spots and stores. In other words, statistical data on location information, which is the activity history of users in the real world, can be used to analyze and attract users to tourist spots and stores in addition to conventional advertising activities.

[0023] <Example of calculating the relationship between clusters and spots> Each value of Bangya Feature value of the facility genre to which the spot belongs: 10 Population share of the spot: 30% Population of the spot: 120 When this happens, the relationship value between the band member and the spot will be 360. Each value of otaku Feature value of the facility genre to which the spot belongs: 1 Population share of the spot: 40% Population of the spot: 160 When this happens, the relationship value between the otaku and the spot will be 64. If the location information of a user obtained from a website is the spot in question, the cluster to which the user belongs is more likely to be band girls than otaku. In addition, the accuracy of cluster determination increases by acquiring multiple location information and accumulating the spot-related values.

[0024] The theme table 1014 is a table for storing and managing information (theme information) on themes related to a user group consisting of a plurality of users. The theme includes information on the interests, concerns, hobbies, and preferences of a user group. For example, the theme includes character string information indicating that a user group has interests, concerns, and preferences related to railways, anime, cosplay, and a specific artist A (anime nerds, cosplay nerds, etc.). In addition, the theme may include character string information indicating a user's gender, age, occupation, or other arbitrary user attributes. The theme table 1014 is a table having a theme ID as a primary key and columns of theme ID, theme name, theme condition, first user group, first behavior data, and second cluster ID. FIG. 7 is a diagram showing the data structure of the theme table 1014. As shown in FIG.

[0025] The theme ID is an item for storing theme identification information for identifying a theme. The theme identification information is an item for which a unique value is set for each piece of theme information. The theme name is an item for storing the name of the theme. Any character string can be set as the theme name. The theme condition is an item for storing a condition for identifying a user group related to a theme. Specifically, in the present disclosure, information that prescribes conditions related to the behavioral information stored in the behavior table is stored. The theme condition is information that prescribes conditions related to behavioral data and date and time. For example, information that prescribes behavior such as being in a specific geographical space (Tokyo Big Sight) on a specific date and time (August XX) is stored, such as demographic data of gender and age, administrative districts such as cities, wards, towns, and villages, and divisions such as weekdays, holidays, time periods, and the Rokuyo. The theme condition may include conditions related to a combination of multiple behavioral data. For example, the theme condition may include multiple chronologically consecutive behavioral data such as going to a ramen shop after attending a concert. The first user group is an item that stores user IDs included in a user group related to a theme. The first behavioral data is an item that stores representative (typical) behavioral data of a user group related to a theme. For example, moving to a convenience store, Tokyo Big Sight, a ramen shop, and an izakaya in that order. Note that the first behavioral data does not necessarily need to store one predetermined behavioral data, and may be configured to store a plurality of representative (typical) behavioral data related to a user group. Also, each of the plurality of behavioral data may be associated with a predetermined score according to the degree of relevance to the first user group. The second cluster ID is an item for storing cluster identification information for identifying a cluster.

[0026] The cluster table 1015 is a table for storing and managing information relating to clusters (cluster information). The cluster table 1015 is a table having a cluster ID as a primary key, and columns of cluster ID, user group, and representative behavior data. FIG. 8 is a diagram showing the data structure of the cluster table 1015. As shown in FIG.

[0027] The cluster ID is an item for storing second cluster identification information for identifying a second cluster. The second cluster identification information is an item in which a unique value is set for each piece of second cluster information. The user group is an item that stores user IDs included in a user group related to the second cluster identification information. The representative behavior data is an item that stores representative (typical) behavior data of a user group associated with the second cluster identification information. Note that the representative (typical) behavior data is similar to the first behavior data, and therefore a description thereof will be omitted.

[0028] The advertisement table 1021 is a table for storing and managing information related to advertisements (advertising information). Advertisement table 1021 is a table having an advertisement ID as a primary key, and columns of advertisement ID, advertisement data, and advertisement theme ID. FIG. 9 is a diagram showing the data structure of the advertisement table 1021. As shown in FIG.

[0029] The advertisement ID is an item for storing advertisement identification information for identifying an advertisement. The advertisement identification information is an item in which a unique value is set for each advertisement information. The advertisement data is an item that stores information about advertisements. Specifically, the advertisement data may include information about the interests of a user or cluster, attribute information of the user or cluster, and unique information. The advertisement data also includes information such as the character string of the advertisement to be delivered and an avatar image. For tourist spots and brick-and-mortar stores, the advertisement data also includes location information such as the latitude and longitude of the spot. The advertisement theme ID is an item for storing theme identification information associated with a user to whom an advertisement is to be delivered. Specifically, the advertisement theme ID is an item for storing theme identification information for specifying a theme stored in the theme table 1014. This item allows targeted advertising to be performed by narrowing down the users to whom the advertisement is to be delivered.

[0030] <Configuration of the control unit 104 of the server 10> The control unit 104 of the server 10 includes a user registration control unit 1041. The control unit 104 executes an application program 1011 stored in the storage unit 101, thereby realizing each functional unit.

[0031] The user registration control unit 1041 performs processing to store, in the user table 1012, information on users who wish to use the service according to the present disclosure. The information stored in the user table 1012 is generated when a user opens a web page or application operated by a service provider from an arbitrary information processing terminal, inputs information into a predetermined input form, and transmits the information to the server 10. The user registration control unit 1041 stores the received information in a new record in the user table 1012, and the user registration is completed. This allows the user stored in the user table 1012 to use the service. Before the user registration control unit 1041 registers user information in the user table 1012, the service provider may carry out a predetermined examination to restrict whether or not the user is permitted to use the service. The user ID may be any character string or number that can identify the user, any character string or number desired by the user, or any character string or number may be automatically set by the user registration control unit 1041. Also, user registration is not mandatory in this system.

[0032] <Configuration of User Terminal 20> The user terminal 20 is an information processing device operated by a user who uses the service. The user terminal 20 may be, for example, a mobile terminal such as a smartphone or a tablet, a stationary type PC (Personal Computer), or a laptop PC. It may also be a wearable terminal such as an HMD (Head Mount Display) or a wristwatch type terminal, or a device capable of acquiring location information such as a car navigation system. The user terminal 20 may also be a terminal consisting only of a display such as a digital signage, which is used exclusively for video distribution. The user terminal 20 includes a storage unit 201 , a control unit 204 , an input device 206 , and an output device 208 . If the user terminal 20 only has the function of displaying received information, it does not need to include the storage unit 201 or the input device 206. For example, digital signage is an example of such a device.

[0033] To obtain the user's location information, information such as a Global Positioning System (GPS) installed on a smartphone, smartwatch, or the like owned by the user may be used. Alternatively, a terminal such as a magnetic reader or IC reader may be installed, and the user may have the terminal read the user's own device, etc., to obtain location information. Furthermore, location information obtained from a base station of a communication device such as a mobile phone, or location information obtained from WiFi (Wireless Fidelity, registered trademark) may be obtained. Location information estimated from an IP address may also be used.

[0034] <Configuration of the storage unit 201 of the user terminal 20> The storage unit 201 of the user terminal 20 includes a user ID 2011 and an application program 2012 .

[0035] The user ID 2011 is an account ID of the user. The user transmits the user ID 2011 from the user terminal 20 to the server 10. The server 10 identifies the user based on the user ID 2011 and provides the user with the service according to the present disclosure. The user ID 2011 includes information such as a session ID temporarily assigned by the server 10 when identifying the user who is using the user terminal 20.

[0036] The application program 2012 may be stored in advance in the storage unit 201, or may be configured to be downloaded from a web server operated by a service provider via a communication IF. The application programs 2012 include applications such as a web browser application. The application program 2012 includes a programming language such as JavaScript (registered trademark) that runs on a web browser application stored in the user terminal 20 .

[0037] <Configuration of the control unit 204 of the user terminal 20> The control unit 204 of the user terminal 20 includes an input control unit 2041 and an output control unit 2042. The control unit 204 executes an application program 2012 stored in the storage unit 201, thereby realizing each functional unit.

[0038] <Configuration of the input device 206 of the user terminal 20> The input device 206 of the user terminal 20 includes a camera 2061 , a microphone 2062 , a position information sensor 2063 , a motion sensor 2064 , and a touch device 2065 .

[0039] <Configuration of the output device 208 of the user terminal 20> The output device 208 of the user terminal 20 includes a display 2081 and a speaker 2082 .

[0040] <Configuration of administrator terminal 30> The administrator terminal 30 is an information processing device operated by an administrator who manages a service. The administrator terminal 30 may be, for example, a mobile terminal such as a smartphone or a tablet, or may be a stationary PC (Personal Computer) or a laptop PC. It may also be a wearable terminal such as an HMD (Head Mount Display) or a wristwatch-type terminal. The administrator terminal 30 includes a storage unit 301 , a control unit 304 , an input device 306 , and an output device 308 .

[0041] <Configuration of the storage unit 301 of the administrator terminal 30> The storage unit 301 of the administrator terminal 30 includes a user ID 3011 and an application program 3012 .

[0042] The user ID 3011 is the user's account ID. The user transmits the user ID 3011 from the administrator terminal 30 to the server 10. The server 10 identifies the user based on the user ID 3011 and provides the user with the service according to the present disclosure. The user ID 3011 includes information such as a session ID temporarily assigned by the server 10 when identifying the user using the administrator terminal 30.

[0043] The application program 3012 may be prestored in the storage unit 301, or may be downloaded from a web server operated by a service provider via a communication IF. The application program 3012 includes a programming language such as JavaScript (registered trademark) that is executed on a web browser application stored in the administrator terminal 30.

[0044] <Configuration of the control unit 304 of the administrator terminal 30> The control unit 304 of the administrator terminal 30 includes an input control unit 3041 and an output control unit 3042. The control unit 304 executes an application program 3012 stored in the storage unit 301, thereby realizing each functional unit.

[0045] <Configuration of the input device 306 of the administrator terminal 30> The input device 306 of the administrator terminal 30 includes a camera 3061 , a microphone 3062 , a position information sensor 3063 , a motion sensor 3064 , and a keyboard 3065 .

[0046] <Configuration of the output device 308 of the administrator terminal 30> The output device 308 of the administrator terminal 30 includes a display 3081 and a speaker 3082 .

[0047] <System 1 Operation> Each process of the system 1 will be described below. FIG. 10 is a flowchart showing the operation of the cluster processing. FIG. 11 is a flowchart showing the operation of the user extraction process. FIG. 12 is a flowchart showing the operation of the advertisement distribution process. FIG. 13 is a screen example showing the operation of the advertisement distribution process. In addition, some or all of the information processing of the cluster processing, user extraction processing, and advertisement delivery processing according to the present disclosure may be realized using an artificial intelligence system such as a generation AI.

[0048] <Cluster processing> The cluster process is a process of dividing a plurality of users into groups (clustering) according to the similarity of their behavioral information.

[0049] <Overview of cluster processing> The cluster process is a series of processes that store the behavioral information of multiple users, execute clustering process based on the similarity of the stored behavioral information of the users, and assign and store a cluster ID to each user to group them. Alternatively, it is also possible to only assign a similarity.

[0050] <Details of cluster processing> The cluster processing will be described in detail below.

[0051] In step S101, the control unit 104 of the server 10 executes a behavior acquisition step of acquiring behavior data related to time-series behaviors of a plurality of users. Specifically, the control unit 104 of the server 10 acquires information (behavior data) regarding the user's behavior based on the location information and the like periodically received from the user terminal 20. Details of the behavior data are the same as those described for the behavior data items in the behavior table 1013. The behavior data can be specified based on the information (date and time and location information) transmitted by the user terminal 20, by referring to a table (not shown) in which location information, address information, facility information, information on the type (genre) of facility, information on event, etc. are stored in association with each other. Also, the behavior data may be specified based on information of a wireless base station or the like (including a mobile base station or the like that provides the user terminal 20 with a mobile wireless communication function such as LTE, 4G, 5G, etc.) that can wirelessly connect to the user terminal 20 and is located at a predetermined position where the user terminal 20 is located, rather than based on the information transmitted by the user terminal 20. For example, location information estimated by a wireless base station or the like may be acquired as the location information of the user terminal 20. The control unit 104 of the server 10 may collect user behavior data in step S101 periodically, constantly, or all at once, regardless of the processing of step S102 and subsequent steps of the cluster processing, and the timing of collection is not important. Also, the control unit 104 of the server 10 may be configured to obtain user behavior data from another information processing service without directly collecting the data. Specifically, steps S101 to S103 of the cluster processing may be configured to be executed independently and asynchronously (for example, at different frequencies).

[0052] In step S102, the control unit 104 of the server 10 calculates a feature amount for each facility genre for all users by using the entire behavior data acquired in the behavior acquisition step. Specifically, the control unit 104 of the server 10 executes the facility genre importance and feature amount based on the behavior data of multiple users acquired in step S101. Specifically, the user behavior data can be regarded as array data over a predetermined period such as every hour, one day, one week, one month, or one year. For example, if information on the user's behavior (location information, facility name, facility type, numerical values ​​such as event name, text information, and category information) can be acquired for each hour for 24 hours in a day (behavior data for that day), the user's behavior data for a certain day can be regarded as array elements [P1, P2, . . . , P24] having 24 elements (24 hours). The control unit 104 of the server 10 may use representative values ​​such as the mode, average, median, sum, difference, weighted sum, weighted difference, etc. (mode, average, median, sum, difference, weighted sum, weighted difference of array elements) of the daily behavior data of all users over a predetermined period such as one week, one month, or one year, values ​​obtained by detecting and removing outliers, or values ​​obtained by removing facility genres with high co-occurrence rates between facility genres as the daily behavior data of all users. For example, a typical behavior pattern of all users (for example, users often move in the order of facility A, facility B, facility C, etc.) becomes the daily behavior data of all users. The control unit 104 of the server 10 may vectorize the daily behavior data of all users using any machine learning, deep learning, artificial intelligence model, etc. In each process of the present disclosure, behavioral data relating to daily behavior data for 24 hours in one day will be mainly described as an example, but the present disclosure can also be applied to sequence data spanning any specified period such as a week, a month, or a year.

[0053] In step S102, the control unit 104 of the server 10 executes a grouping step of classifying a plurality of users into a plurality of groups based on the similarity of the behavior data acquired in the behavior acquisition step. Specifically, the control unit 104 of the server 10 performs clustering based on the similarity of the time-series behavior data of multiple users acquired in step S101. Specifically, the behavior data of the user can be regarded as array data over a predetermined period such as every hour, one day, one week, one month, or one year. For example, if information on the user's behavior (location information, facility name, facility type, numerical values ​​such as event name, text information, and category information) can be acquired for each hour for 24 hours in a day (today's behavior data), the behavior data of the user on a certain day can be regarded as array elements [P1, P2, . . . , P24] having 24 elements (24 hours). Alternatively, the period of going out from home to home may be regarded as the day. The control unit 104 of the server 10 may take representative values ​​such as the mode, average, median, sum, difference, weighted sum, weighted difference, etc. (mode, average, median, sum, difference, weighted sum, weighted difference of array elements) of the user's daily action data over a predetermined period such as one week, one month, or one year, values ​​obtained by detecting and removing outliers, or values ​​obtained by removing facility genres with high co-occurrence rates between facility genres as the user's daily action data (referred to as user's daily action data). For example, a typical user behavior pattern (e.g., often moving to facility A, facility B, facility C, etc. in that order) becomes the user's daily action data. In addition, the control unit 104 of the server 10 vectorizes the user's daily action data using any machine learning, deep learning, artificial intelligence model, etc. Specifically, based on the behavior data of multiple users acquired in step S101, a process such as vectorization is appropriately performed so that the similarity between the actions of each user can be calculated. In addition, the control unit 104 of the server 10 can execute a process of extracting the similarity between the vectors of each of a plurality of users, a vector (representative vector) that characterizes each of the vectors of the users, etc. by applying any normalization method to the obtained vectors. Also, the facility genre importance of each user may be calculated and a feature value compared with the whole may be given. In each process of the present disclosure, behavior data related to daily behavior data for 24 hours per day will be mainly described as an example, but the present disclosure can also be applied to sequence data over any predetermined period such as one week, one month, one year, etc. In addition, it may be determined whether a user works, lives, or visits a place based on the stay time, visit frequency, or usual behavior, and the type of occupation may be determined and stored by combining the determination of work with the facility genre.

[0054] In step S102, the control unit 104 of the server 10 calculates the similarity of vectors related to the behavioral data calculated for each of the multiple users. Specifically, the control unit 104 of the server 10 calculates the value of the similarity between vectors related to the behavioral data using the inverse of Euclidean distance, Manhattan distance, etc., or other similarity such as cosine similarity. In this way, the control unit 104 of the server 10 calculates a similarity matrix (the inverse of the distance matrix) between the multiple users. The control unit 104 of the server 10 clusters a plurality of users according to the similarity of their behavioral data based on the similarity matrix. For example, the clustering algorithm includes k-means. When the clustering process is executed, a cluster label such as a cluster ID can be obtained for each user that is similar to the user. Users with the same cluster ID are users who have similar behavioral data (high similarity of behavioral data, close distance). In addition, values ​​of facility genres that show unreliable values ​​may be removed. For example, if the average number of spots that are the source of the feature amount of facility genre A is close to 1, it may be a value that indicates that the user happened to be at that place, and the reliability is low compared to a feature amount consisting of multiple spots. If the average number of spots that are the source of the feature amount of facility genre A for a certain user group is close to 1 and the average number of spots that are the source of the feature amount of facility genre A for another user group with high feature amount of facility genre A is farther than 1, it means that only the user group uses a small number of stores in a facility genre that usually involves the use of a large number of stores, and the reliability is low. If the average number of spots that are the source of the feature of facility genre A is close to 1, and facility genre B with a high feature is located in the same location and the average number of spots that are the source of the feature of facility genre B is far from 1, then the feature of facility genre A is likely to be influenced by facility genre B and its reliability is low. Comparing the co-occurrence rates of the locations of spots of facility genre A and facility genre B, if the rate at which facility genre B exists when facility genre A exists is higher than the rate at which facility genre A exists when facility genre B exists, then the feature of facility genre A is likely to be influenced by facility genre B and its reliability is low. If facility genre A is a toy store (one store within the aggregation range) and facility genre B is a department store (three stores within the aggregation range), then when the rate at which facility genre B exists when facility genre A exists is 100% and the rate at which facility genre A exists when facility genre B exists is 33%, then facility genre A is likely to be subordinate to facility genre B and the feature of facility genre A has low reliability. If the co-occurrence rate of facility genre A and facility genre B is low when viewed from the overall or other user groups' perspective, and the co-occurrence rate is high only for the relevant user group, then there is a high possibility that the facility genre A is influenced by the subordinate facility genre B, and its credibility is low.When the co-occurrence rate of the locations of spots from both facility genre A and facility genre B is 100%, it is advisable to consider them to be essentially the same facility. In this way, facility categories that show unreliable values ​​can be identified using the "average number of spots that are the source of the features" and the "co-occurrence rate between facility categories," and facility categories that meet certain conditions can be excluded from the representative behavior data. Furthermore, when multiple clusters with high similarity are determined, the user may belong to the cluster with the highest similarity, or may belong to multiple clusters with high similarity. The control unit 104 of the server 10 calculates representative values ​​such as the mode, average, median, sum, difference, weighted sum, weighted difference, etc. (mode, average, median, sum, difference, weighted sum, weighted difference of array elements) of the behavioral data of multiple users (users belonging to the same group) who have the same cluster ID as representative behavioral data of the group. In addition, when calculating the representative behavior data, outliers may be detected and excluded, or values ​​of facility genres with high co-occurrence rates between facility genres may be excluded. In addition, any processing may be performed so that the representative behavior data indicates typical behavior of a group of users. Furthermore, the representative behavior data may be calculated from a cluster that prioritizes facility genres with high rarity and carefully selects users with many commonalities. For example, the representative behavior data may be calculated based on the behavior data of another cluster in which the first behavior data and behavior data related to facility genres are highly similar, or on the behavior data of another cluster that is determined to have a correlation of a predetermined value or more with the results of clustering in advance (such as anime otaku or train otaku). Alternatively, the representative behavior data may be calculated by setting the feature value of the facility genre statistics to a lower value than the standard. In that case, the cluster will be one in which the person has not visited that facility genre, so if the person has visited that facility genre, there is a possibility that there will be errors, but if the person has not visited that facility genre, there will be no errors, so the reliability is high. Also, the feature value may be weighted. The representative behavior data may be calculated using machine learning, deep learning, or any artificial intelligence model. The representative behavior data may be one with a high total value of the features of each user, or one with a large number of members may be the representative value. Even if the facility genre importance similarity is high, if the variance within the cluster is high, it may not be selected as the representative behavior data.

[0055] The control unit 104 of the server 10 stores the cluster ID, the user ID of the user having the cluster ID, and the representative behavior data calculated from the behavior data of the user having the cluster ID in the fields of cluster ID, user group, and representative behavior data of a new record in the cluster table 1015. This makes it possible to divide a plurality of users into a plurality of groups (user groups) according to the similarity of their behavior data. <User extraction process> The user extraction process is a process for identifying a first user group related to the input theme information and a second cluster having behavior information similar to that of the first user group.

[0056] <Overview of user extraction process> The user extraction process is a series of processes that accepts input of theme information, identifies a first user group consisting of multiple users related to the theme information, identifies representative first behavioral data of the first user group, identifies one or more clusters having behavioral data similar to the first behavioral data, and stores the clusters in association with the first user group.

[0057] <User extraction process details> The user extraction process will be described in detail below.

[0058] In step S301, the control unit 104 of the server 10 executes a theme receiving step of receiving theme information related to a theme. An administrator, such as an employee of a business providing an information processing service according to the present disclosure, operates the input device 306 of the administrator terminal 30 to input the URL of a page for executing the theme reception processing (theme reception processing page) into a web browser or the like, and open the theme reception processing page. The control unit 304 of the administrator terminal 30 sends a request to the server 10 to open the theme reception processing page. The control unit 104 of the server 10 generates a theme reception processing page based on the received request and sends it to the administrator terminal 30. The control unit 304 of the administrator terminal 30 displays the received theme reception processing page on the display 3081 of the administrator terminal 30. The theme reception processing page includes an input field capable of receiving input of a theme name that identifies a theme related to the user's interests, concerns, hobbies, and preferences, and theme conditions for identifying the theme. The theme condition is a condition for identifying a group of users related to a theme. The theme condition is the same as the explanation of the theme condition item in the theme table 1014.

[0059] The administrator inputs the theme name and theme conditions into the input fields of the theme reception processing page by operating the input device 306 of the administrator terminal 30. The administrator transmits the theme name and theme conditions entered into the input fields to the server 10 by operating the input device 306 of the administrator terminal 30. The control unit 104 of the server 10 stores the received theme name and theme conditions in the theme name and theme condition items of a new record in the theme table 1014 .

[0060] In the user extraction process, steps S301 to S305 do not necessarily need to be executed consecutively. For example, the administrator may execute the process of step S301 in advance and store a plurality of themes in the theme table 1014. Also, the control unit 104 of the server 10 may be configured to execute steps S302 to S305 asynchronously with the completion of step S301.

[0061] In step S302, the control unit 104 of the server 10 executes a first extraction step of extracting a first user group consisting of one or more users related to a predetermined theme from among multiple users, based on the behavioral data acquired in the behavior acquisition step. Specifically, the control unit 104 of the server 10 refers to the theme table 1014 to acquire the items of the theme conditions. For example, the control unit 104 of the server 10 may be configured to acquire a record of theme information for which the process after step S302 has not been executed and information is not stored in at least one of the items of the first user group, the first behavior data, and the second cluster ID. The control unit 104 of the server 10 searches the behavior data items in the behavior table 1013 based on the theme condition, and identifies a plurality of user IDs (users) whose behavior data meets the theme condition as a first user group.

[0062] The first extraction step executes a step of extracting a first user group related to an event held in a predetermined period. For example, assume that an event is held at a predetermined facility (facility A) at a predetermined date and time (date and time A). In this case, the control unit 104 of the server 10 searches the behavior data items in the behavior table 1013, searches for one or more users who are visiting facility A at date and time A, and identifies them as the first user group.

[0063] The first extraction step includes a step of extracting a first user group based on the behavior of a plurality of users before and after a predetermined period of time, and excluding one or more users who are not related to an event to be held during the predetermined period of time. Specifically, the theme condition may include not only a condition that the behavioral data should satisfy, but also a condition related to a condition that the behavioral data does not satisfy. For example, the theme condition may include a condition related to not visiting facility B, facility C, etc., during a time period before or after visiting (checking in or out of) an event held on date and time A. In this case, the control unit 104 of the server 10 searches the behavior data items in the behavior table 1013, and identifies one or more users visiting facility A on date and time A as a first user group, excluding users who visited facility B, facility C, etc. in the time period before or after the date and time A.

[0064] For example, if the event is an artist event for minors, facility A, which is the venue for the event, may include multiple facilities such as a live music venue and a hospital. In this case, it is expected that the interests of multiple users visiting facility A may be completely different between users visiting the live music venue and users visiting the hospital. For example, assume that a user visiting the live music venue is User A and a user visiting the hospital is User B, and that there is behavioral data when visiting the hospital. In this case, it is assumed that user A and user B have in common that they visited facility A on a certain date and time A, and cannot be distinguished from the user behavior data. Even in such a case, it is assumed that the behavior patterns of the facilities visited by user A and user B in the time period before and after visiting facility A are significantly different. For example, it is conceivable that user B visited nearby elderly welfare facilities as facility B and facility C in the time period before and after. In this case, if there is behavioral data showing a visit to a welfare facility for the elderly before and after the behavioral data when visiting the hospital, although user B visits facility A on date and time A, since the user also visits the welfare facility for the elderly before and after the visit, it is assumed that the user is not attending the live venue but is visiting facility A for other reasons such as visiting the hospital. Therefore, when identifying a first user group for a theme related to the artist event, user A is included in the first user group, and user B, who is assumed to have low relevance, is excluded from the first user group to identify the first user group. This makes it possible to more accurately identify a first user group consisting of multiple users with uniform interests and concerns from abstract behavioral data. Also, clusters that visit hospitals or the behavior of the target cluster when visiting the hospital can be used as targets for a user group to be excluded. By carrying out the exclusion process, the population distribution and schedule of the user group change, making it possible to deliver advertisements to more effective locations.

[0065] In step S303, the control unit 104 of the server 10 executes a representative acquisition step of acquiring representative behavior data representative of the first user group extracted in the first extraction step. The representative acquisition step executes a step of acquiring representative behavior data representative of the first user group for a period other than the predetermined period. Specifically, the control unit 104 of the server 10 calculates representative values ​​such as the mode, average, median, sum, difference, weighted sum, weighted difference, etc. of the behavioral data of multiple users included in the first user group (the mode, average, median, sum, difference, weighted sum, weighted difference of array elements) as representative behavioral data of the first user group. In addition, when calculating the representative behavior data of the first user group, outliers may be detected and excluded, or facility genres with high co-occurrence rates between facility genres may be excluded. In addition, any processing may be performed so that the representative behavior data indicates typical behavior of the user group. Furthermore, the representative behavior data of the first user group may be calculated from a cluster that prioritizes facility genres with high rarity and carefully selects users with many commonalities. For example, the representative behavior data may be calculated based on the behavior data of other clusters in which the first behavior data and behavior data related to facility genres are highly similar, or on the behavior data of other clusters that are determined to have a correlation of a predetermined value or more with the results of clustering in advance (such as anime otaku or train otaku). Specifically, a typical behavior pattern based on the behavior data of many users included in the first user group on the day becomes the representative behavior data. Also, multiple representative behavior data may be calculated for the first user group. For example, a typical behavior of an otaku event participant may be set as the representative behavior data of an otaku event participant from among otaku event participants (first user group), or multiple representative behavior data may be generated from otaku event participants for typical behaviors such as anime otaku and train otaku, and a combination of the multiple representative behavior data such as the representative behavior data of anime otaku and the representative behavior data of train otaku may be set as the representative behavior data of an otaku event participant.

[0066] In step S304, the control unit 104 of the server 10 executes a second extraction step of extracting a second user group consisting of one or more users from the multiple users based on the representative behavior data of the first user group. Specifically, the control unit 104 of the server 10 searches for the items of representative behavior data in the cluster table 1015 based on the representative behavior data of the first user group calculated in step S303. For example, a predetermined number of cluster information having representative behavior data similar to the representative behavior data of the first user group (high similarity of behavior data, close distance) is acquired from the cluster table 1015. Note that the control unit 104 of the server 10 specifies, as the second cluster ID, the cluster IDs of the predetermined number of cluster information in the order of cluster information in which the similarity of the representative behavior is equal to or greater than a predetermined value, and similarity between the representative behavior data. This makes it possible to identify a second user group (one or more users included in the cluster corresponding to the second cluster ID) consisting of one or more users who have behavior data similar to the representative behavior data of the first user group.

[0067] This makes it possible to extract a second user group (potential customers) that is different from the first user group but is expected to potentially exhibit a similar behavior pattern to the first user group based on the typical behavior pattern (representative behavior data) of the first user group related to a specific theme or event. For example, when the first user group is a user who participated in a specific artist event, a second user group is identified that has similar daily behavior data other than the day the event is held. In this case, although the second user group is not necessarily a user who participated in a specific artist event, it is expected that the interests, concerns, hobbies, and preferences of the second user group are similar to those of the first user group because of the similar behavior. For example, it is expected that the purchasing behavior (interests, concerns, hobbies, and preferences of goods and services purchased) of the second user group is similar to that of the first user group.

[0068] In addition, the identification of the second user group does not necessarily require the use of cluster information identified by cluster processing. The second user group that is expected to exhibit behavior patterns similar to those of the first user group may be identified from a plurality of users having behavior data similar to the representative behavior data of the first user group.

[0069] In addition, when the second user group includes the first user group, the second user group may be identified by excluding a part or all of the first user group from the second user group. For example, after identifying potential customers (second user group) who visit a luxury car store and have a tendency related to behavioral data similar to that of luxury car buyers (first user group), a more suitable second user group can be identified by excluding the first user group who have already purchased luxury cars from the second user group. Based on the distribution of the second user group, ads related to attracting customers should be sent to users who are likely to visit within a few hours, and awareness ads should be sent to users who are likely to visit within a period of six months. Also, when matching using external context, it is possible to choose not to deliver ads to users who have visited a Lexus store, since they are likely to be purchasers.

[0070] It should be noted that the number of users (number of people) included in the second user group identified by the first user group may be greater or smaller than the number of users (number of people) included in the first user group. The second user group may be a part of the first user group. For example, the second user group may be identified from one or more user groups included in the first user group. Specifically, a second user group consisting of a part of the users included in the first user group may be identified by applying cluster processing to the users included in the first user group. In addition, the distance (similarity) between the behavioral data of each of the multiple users included in the first user group and the representative behavioral data may be calculated, and a user group whose distance is equal to or less than a predetermined value (similarity is equal to or more than a predetermined value) may be identified as the second user group. In this case, the second user group, which is a part of the first user group and has a behavior pattern similar to the representative behavior data of the first user group, can be said to be a user group composed of a portion of users in the first user group who exhibit behavior data that is typical of the first user group (in the case where the first user group is otaku, it can be said to be genuine otaku who behave in a way that is typical of otaku). In this way, by executing the user extraction process, it is possible to identify a second user group consisting of a portion of users from among the multiple user groups included in the first user group who exhibit typical behavioral data in the first user group, particularly similar to the representative behavioral data of the first user group.

[0071] In cases where a certain facility type or spot is visited by the majority of users, a second user group (potential customers) can be generated by extracting users who have not visited that facility type or spot. The second user group created from the first user group who visited a certain facility type or spot is a user group that has the potential to visit that facility type or spot, but the user group that has not visited a certain facility type or spot may be a customer persona that has factors that prevent them from visiting that facility type or spot, and is effective as marketing data.

[0072] Each spot is assigned a relationship value with each user group, and the user group can be determined by obtaining location information from the website that indicates the spot where the user checked in and adding the relationship value. For example, if a user checks in to spots B and C and has a high relationship value with otaku, the user is determined to be in the otaku cluster.

[0073] In step S305, the control unit 104 of the server 10 executes a second storage step of storing the second user group extracted in the second extraction step. Specifically, the control unit 104 of the server 10 stores the cluster ID of the identified second user group in the second cluster ID item of the theme table 1014. This identifies the first user group and the second cluster ID of the second cluster having behavioral data similar to that of the first user group (the second cluster ID identifies a user group by referring to the cluster IDs in the cluster table 1015, and the multiple user groups become the second user group).

[0074] <Advertisement distribution processing> The advertisement delivery process is a process of delivering advertisements to the user terminals of users.

[0075] <Outline of ad delivery process> The advertisement delivery process is a series of processes that identify a second user group according to the set advertisement, acquire second user information related to the second user group, generate an advertisement according to the second user group, and deliver the advertisement. In addition, although the present disclosure discloses the distribution of advertisements as an example, the information distributed to the user terminal is not limited to advertisements, and any information may be provided to the user, such as recommendation information (text data, image data, audio data, video data, etc. that explain recommended places, products, services, etc.). Also, the user terminal may not have a function for transmitting user information including location information, and may be configured to only receive recommendation information like digital signage.

[0076] In the advertisement delivery process, steps S501 to S505 do not necessarily have to be executed consecutively. For example, it is preferable to execute the advertisement generation process in steps S501 to S504 collectively in advance for a plurality of pieces of advertisement information. In this case, in step S505, one or more advertisements suited to the behavior of a user who has accessed a specific website are delivered to the user.

[0077] <Details of ad delivery process> The advertisement distribution process will be described in detail below.

[0078] In step S501, the control unit 104 of the server 10 executes an information input step of accepting input of information relating to user interests from an information publisher. An administrator, such as an employee of a business providing an information processing service according to the present disclosure, operates the input device 306 of the administrator terminal 30 to input the URL of a page for executing advertisement delivery processing (advertisement delivery processing page) into a web browser or the like, and open the advertisement delivery processing page. The control unit 304 of the administrator terminal 30 transmits a request to open the advertisement delivery processing page to the server 10. The control unit 104 of the server 10 generates an advertisement delivery processing page based on the received request and transmits it to the administrator terminal 30. The control unit 304 of the administrator terminal 30 displays the received advertisement delivery processing page on the display 3081 of the administrator terminal 30. The advertisement delivery processing page includes an input field that can accept input of theme identification information for identifying a theme related to a user's interests, concerns, hobbies, and preferences, and character strings, images, etc. to be used in advertisements to be delivered to users who have an interest in the theme. The theme identification information is a theme ID in the theme table 1014. The control unit 104 of the server 10 may search the theme table 1014 and display the acquired theme ID and theme name in a selectable manner on the display 3081 of the administrator terminal 30. For example, the theme names may be configured to display a list of theme identification information and theme names related to themes such as "Otaku", "Visitors to the Hakata Ramen Higashi Station Store", and "Customers Eating Ramen for Dinner" on the display 3081 of the administrator terminal 30 in a manner selectable by the administrator.

[0079] The administrator can select one or more theme IDs from the list by operating the input device 306 of the administrator terminal 30. This makes it possible to provide information such as advertisements according to themes such as the user's interests, concerns, hobbies, and preferences, as well as user attributes such as gender and age.

[0080] The control unit 304 of the administrator terminal 30 transmits the one or more pieces of theme identification information and advertisement data input by the administrator to the server 10. The control unit 104 of the server 10 stores the received one or more pieces of theme identification information and advertisement data in the advertisement theme ID and advertisement data items of a new record in the advertisement table 1021.

[0081] An example of the advertisement delivery processing page D1 is shown in Fig. 13. The advertisement delivery processing page D1 includes avatars D11 visually depicting the figures of a user group having interests, concerns, hobbies, and preferences in the selected theme, visited places D12 indicating the breakdown of facilities visited by the user group, facility genres, and the like, attributes D13 indicating the breakdown of the user attribute data of the user group, similarity D14 indicating the similarity between the user group and a user group related to another theme, and interests D15 indicating the interests, concerns, hobbies, and preferences of the user group. In the present disclosure, an example of an advertisement delivery processing page D1 for a user group consisting of a plurality of users identified by referring to the theme table 1014 using one or more theme identification information stored in the item of the advertisement theme ID of the advertisement table 1021 will be described as an example, but is not limited thereto. For example, the present invention may be applied to present information on a user group consisting of a plurality of arbitrary users, such as an arbitrary user group in the cluster table 1015 stored in the cluster processing, a first user group in the theme table 1014 stored in the user extraction processing, and a second user group identified by referring to the user group in the cluster table 1015 using a second cluster ID expected to show a behavior pattern similar to that of the first user group. Also, advertisements may be delivered according to keywords linked to the theme. For example, when keywords such as anime, train, and karaoke are linked to the theme of otaku, only karaoke may be selected and an advertisement may be delivered, or conversely, an advertisement may be delivered excluding karaoke. Also, the determination of the theme does not need to be made in real time, and the theme may be determined using location information that has been visited in the past.

[0082] <Avatar D11 Configuration> The control unit 104 of the server 10 executes a persona creation step of creating a person image that characterizes a predetermined user group, based on the behavioral data of a plurality of users included in the predetermined user group. The control unit 104 of the server 10 executes a persona presenting step of presenting the person image created in the persona creating step. Specifically, the control unit 104 of the server 10 searches an avatar database (not shown) based on the behavioral data of multiple users included in a specified user group, user attribute data, representative behavioral data of the specified user group, and representative values ​​(mode, average, median, maximum, minimum, etc.) of the user attribute data of multiple users. In addition, the search may be performed from values ​​obtained by detecting outliers in the behavior data and excluding them, values ​​obtained by excluding facility genres with high co-occurrence rates between facility genres, or from behavior data carefully selected from users with many commonalities, with priority given to facility genres with high rarity.The search may be performed based on other clusters with high similarity between the first behavior data and the behavior data related to the facility genre, behavior data of clusters with high similarity when excluding facility genres with high co-occurrence rates, or behavior data of other clusters determined to have a correlation of a predetermined value or more with the results of prior clustering (such as anime otaku or train otaku). The avatar database stores each part of a character in a digital space (the character does not necessarily have to be a replica of a human), such as a human face, eyes, nose, hairstyle, body type, clothing, etc., in association with behavior data, user attribute data, etc. The control unit 104 of the server 10 refers to the avatar database based on the behavior data, user attribute data, etc. of a predetermined user group, and identifies the parts (parts) that make up an avatar, such as a human face, eyes, nose, hairstyle, body type, clothing, etc. The control unit 104 of the server 10 creates a person image that characterizes a predetermined user group by combining the parts that make up the specified avatar. The person image may be created using a learning model such as any machine learning, deep learning, or artificial intelligence model, with the behavioral data, user attribute data, etc. of the predetermined user group as input data. For example, this can be realized by having a learning model learn in advance so that a predetermined person image is output as output data for input data such as the behavioral data, user attribute data, etc. of the user group. In addition, a person image that characterizes a predetermined user group may be created by applying the behavioral data, user attribute data, etc. of the predetermined user group as input data to a generation AI. The control unit 104 of the server 10 outputs the created person image to the avatar D11. For example, in the case where the predetermined user group is a user group with a theme related to railroad enthusiasts, a person image wearing glasses, a polo shirt combined with cargo pants, and sneakers may be registered as a railroad enthusiast, and the avatar D11 may be output when the degree of similarity to a railroad enthusiast is high.

[0083] A person image, i.e., an avatar, that determines the image of a person is set for a part of the second user group. In other words, the number of users to whom avatars are set is equal to or less than the second user group. It is natural that the same avatar is displayed for a user group with similar representative behavior data, and the creation cost is reduced by using a small number of avatars as person images for a large number of user groups with similarity. The second user group for which avatars are set is preferably one for which the general public has a common image of a person's appearance that correlates with their interests, such as participants in an otaku event. A user group that can be extracted using location information is a group of users who gather at specific locations in the real world, such as events or stores, and it is easy to imagine their appearance by actually seeing them. On the other hand, in the case of search information, for example, it is difficult to evoke a common image of appearance from a group of users who searched for a specific keyword. If we consider an event as a facility genre that appears only a few days a year, the rarity of this facility genre is extremely high, and the frequency is concentrated among a small number of users. In other words, a specific image that correlates interests and appearance is likely to be recognized by the general public by users of a facility genre with a high rarity x frequency value and an extremely concentrated rather than dispersed user group, and by users who have such a facility genre as a co-occurring genre. This allows the viewer to implicitly understand the interests and appearances of users other than the user group for whom the avatar is set, without having to interpret the numerical values ​​of each piece of data. The image of a person that defines the person's image can be an illustration, photograph, or 3D, and each part can be separate. The portrait image that defines the image of a person is preferably, for example, a photograph of the person taken on the date and time of a specific event or an illustration drawn based on the photograph, and it is even more preferable if it is statistically more prevalent. The base image of the avatar may be changed according to gender and age, or different images may be prepared for each gender and age. A person image that determines a person's image may be set by reconstructing a statistical combination from data from a service that allows users to set a general avatar with each part separated and post information linked to location information or event information names, or from data obtained by morphologically analyzing each part of images from a fixed camera, etc. It is desirable that the statistical combination of each part is common in that region or event compared to the whole. Similar to the idea of ​​facility genre features, for example, it is desirable that the appearance of Akihabara is not the appearance of the majority in Akihabara, but an appearance / clothing that is relatively common in Akihabara / is seen only in Akihabara. When the avatar of a user group for which an avatar is set is displayed as an avatar of another user group according to the degree of similarity, each part may be changed according to the feature amount of the facility genre of the other user group. For example, some facility genres related to appearance may be associated with each part of the avatar, such as tanning salons, nail salons, tattoo shops, contact lens shops, etc. In other words, even when displaying an otaku avatar to a user group with a high similarity to otakus, changes may be made, such as making the skin color darker if the feature value of a tanning salon is high, and removing glasses if the feature value of a contact lens shop is high. When the avatar of a user group who sets an avatar is displayed as an avatar of another user group according to the degree of similarity, each feature may be changed according to the degree of similarity with the other user groups who set the avatar. For example, if the user group for which avatars are set is set such that otaku tend to wear clothes in dull colors and with dull designs, and delinquents tend to wear clothes in bright colors and with dull designs, then even if an otaku avatar is displayed to a user group that is highly similar to otaku, if the next most similar user group is delinquents, then the avatar may be changed to reflect the characteristics of each group, such as wearing clothes in dull colors and with dull designs, or clothes in bright colors and with dull designs. This makes it possible to display images that better match the characteristics of each user group without having to prepare a large number of images. It is not necessary to have one avatar for one user group, but multiple avatars may be set. Also, each user group is distributed in various spots, but even if they are the same otaku, they may be displayed in clothes and backgrounds that match the distribution area, such as clothes when participating in an event and clothes when going outdoor camping.

[0084] <Configuration of visiting location D12> The control unit 104 of the server 10 executes a behavior counting step of counting the number of predetermined one or more behavior data included in the behavior data of a plurality of users included in a predetermined user group. The control unit 104 of the server 10 executes a behavior presenting step of presenting an index value based on the number of the predetermined one or more pieces of behavior data tallied in the behavior tabulating step in association with the predetermined one or more pieces of behavior data.

[0085] The behavior counting step executes a step of counting the number of facility genres related to one or more predetermined types of facility included in the behavior data of a plurality of users included in a predetermined user group. The behavior presenting step executes a step of presenting an index value based on the number of predetermined one or more facility genres tallied in the behavior tabulating step in association with the predetermined one or more facility genres.

[0086] The behavior counting step executes a step of counting the number of predetermined one or a plurality of facilities included in the behavior data of a plurality of users included in a predetermined user group. The behavior presenting step executes a step of presenting an index value based on the number of predetermined one or more facilities tallied in the behavior counting step in association with the predetermined one or more facilities.

[0087] Specifically, the control unit 104 of the server 10 tabulates the details of the behavioral data of multiple users included in the user group. In this case, the behavioral data to be tabulated (predetermined behavioral data) may be a single behavioral data in a specific time period, or a pattern of continuous behavioral data over multiple time periods (for example, a case where a user moves to facilities A, B, and C in that order over time periods A, B, and C). In addition, the data may be tabulated based on information about any behavior defined in the behavioral data item of the behavior table 1013 (location information, facility, facility genre, store, store name, event, etc.). For example, a counting result can be obtained that A% of users visited facility A, B% of users visited facility B, C% of users visited facility genre C, and D% of users visited facility genre D. The control unit 104 of the server 10 may be configured to calculate a predetermined index value (score) based on the counting result. In this case, the index value indicates the degree of association between a specific user group and predetermined behavioral data, the importance of the behavioral data, etc. The control unit 104 of the server 10 outputs the counted result to the visited place D12 in association with each of the behavior data.

[0088] <Output example 1 of visited place D12 (facility genre)> Karaoke Box 42 points · Computer sales and repair shop 32 points ·CD·DVD Shop 48 items Anime Shop 64 points Card Shop 42 points · Bars & Clubs 12 points Family Restaurant 22 points Convenience store: 43 points Amusement facilities: 82 points

[0089] The output of the visited places D12 may be configured to be hierarchically subdivided by facility genre and facility, and output hierarchically. <Output example 2 of visited location D12> Convenience stores Seven-Eleven 44 points FamilyMart 28 points Lawson 32 points Amusement facilities Aquarium 42 points ·Museum 22 items Cinema 12 points

[0090] The control unit 104 of the server 10 executes an attribute counting step of counting the number of predetermined one or more pieces of attribute information included in attribute information related to attributes of a plurality of users included in a predetermined user group. The control unit 104 of the server 10 executes an attribute presenting step of presenting an index value based on the number of predetermined one or more pieces of attribute information collected in the attribute collecting step in association with the predetermined one or more pieces of attribute information. Specifically, the control unit 104 of the server 10 compiles a breakdown of what user attribute data (for example, gender, age, place of residence) each of the multiple users included in the user group has. For example, a counting result can be obtained that A% of users are male, B% are female, C% are in their 20s, and D% are in their 30s. The control unit 104 of the server 10 may be configured to calculate a predetermined index value (score) based on the counting result. In this case, the index value is an index value indicating the degree of association between a specific user group and predetermined user attribute data, the importance of the user attribute data, etc. The control unit 104 of the server 10 outputs the details of the collected user attributes to the attribute D13.

[0091] <Example 1 of attribute D13 output (gender)> ·Male 72 points ·Female 28 points <Attribute D13 Output Example 2 (Era)> 10-19 years old: 14 points 20-29 years old: 18 points 30-39 years old: 22 points 40-49 years old: 21 points <Attribute D13 Output Example 3 (Place of Residence)> Kanto 42 points Tohoku 18 points ·Chubu 10 points Kansai 5 points

[0092] <Configuration of similarity D14> The control unit 104 of the server 10 executes a similarity calculation step of calculating a similarity between a predetermined user group and a plurality of user groups. The similarity calculation step executes a step of calculating a similarity between behavioral data of a plurality of users included in the predetermined user group and behavioral data of a plurality of users included in each of the plurality of user groups. The control unit 104 of the server 10 executes a similarity presenting step of presenting the similarities for each of the multiple user groups calculated in the similarity calculating step in association with a theme related to the user group. Specifically, the control unit 104 of the server 10 calculates a similarity between the representative behavior data of a predetermined user group and the first behavior data of the theme information stored in the theme table 1014, the representative behavior data stored in the cluster table 1015, or the representative behavior data of a user related to the theme specified by referring to the theme table 1014 based on the advertisement theme ID of another theme stored in the advertisement table 1021. Note that the calculation method of the similarity of the behavior data is the same as the calculation method of the behavior data described in the cluster processing and the user extraction processing, so the description is omitted. Alternatively, a configuration may be adopted in which the similarity of a predetermined user group to an arbitrary user group consisting of multiple users is calculated. Also, the control unit 104 of the server 10 may be configured to calculate a predetermined index value (score) based on the similarity. In this case, the index value is an index value indicating the similarity between the predetermined user group and another user group. This makes it possible to calculate the similarity between the representative behavior data of a specific user group and the representative behavior data of user groups included in other theme information and advertisement information, that is, the similarity between the behavior patterns of multiple users included in each user group. The control unit 104 of the server 10 outputs the calculated similarity to the similarity D14 of the advertisement delivery processing page D1 in association with the theme name, theme ID, advertisement ID, etc.

[0093] <Example 1 of similarity D14 output> ·Overseas travel 64 points Outdoor Festival 28 items Leisure 12 points ·Car 82 points Otaku 24 points

[0094] <Configuration of Interest D15> The control unit 104 of the server 10 executes an interest presentation step of presenting interest information, which is an index value indicating the interests of multiple users included in a specified user group, based on behavioral data of the multiple users included in the specified user group. Specifically, the control unit 104 of the server 10 tabulates the details of the behavioral data of multiple users included in the user group. In this case, the behavioral data to be tabulated (predetermined behavioral data) may be a single behavioral data in a specific time period, or a pattern of continuous behavioral data over multiple time periods (for example, a case where a user moves to facilities A, B, and C in that order over time periods A, B, and C). In addition, the data may be tabulated based on information about any behavior defined in the behavioral data item of the behavior table 1013 (location information, facility, facility genre, store, store name, event, etc.). For example, in facility genre A, a counting result can be obtained that A1% of users visit facility genre A1, A2% of users visit facility genre A2, and A3% of users visit facility genre A3. The control unit 104 of the server 10 may be configured to calculate a predetermined index value (score) based on the counting result. In this case, the index value indicates the degree of association between a specific user group and predetermined behavioral data, the importance of the behavioral data, etc. In the present disclosure, unlike the visited places D12, the interests D15 present information indicating the tendency of the user's interests and preferences in terms of a predetermined genre type, etc. For example, in the facility genre A, it is possible to visually confirm the proportion of A1, A2, and A3 groups in the interests and preferences of users included in a predetermined user group. <Example 2 of output for interest D15> ·drink Coffee 44 points ·Tea 12 points Barley tea 22 points ·Water 32 points

[0095] By checking the display contents of the advertisement distribution processing page D1, the information publisher can learn in detail about the themes such as the interests, concerns, hobbies and preferences of a user group consisting of multiple users identified by the selected theme ID or IDs. The information publisher can create advertisement data suitable for a user group by checking the display contents of the advertisement distribution processing page D1. Also, according to the request of the information publisher's customer (for example, a store such as Hakata Ramen Higashi Station store), it can be confirmed whether the user group is likely to visit the store as a potential customer. It is preferable to group the themes under a uniform theme, such as for each baseball team, as this makes it easier to understand when delivering advertisements.

[0096] In step S502, the control unit 104 of the server 10 executes a second user group identifying step of identifying one or more second user groups related to the user's interests input in step S501. Specifically, the control unit 104 of the server 10 searches the item of theme ID in the theme table 1014 based on one or more theme identification information stored in the advertisement theme ID of the advertisement table 1021 in step S501, and acquires information of the second cluster ID. This makes it possible to identify a second user group (potential customers) that is expected to exhibit a behavior pattern similar to that of the first user group linked to one or more pieces of theme identification information input from the administrator in step S501.

[0097] In step S503, the control unit 104 of the server 10 executes a second user information acquisition step of acquiring second user information of users included in one or more second user groups identified in the second user group identification step. Specifically, the control unit 104 of the server 10 searches the cluster ID item in the cluster table 1015 based on the second cluster ID acquired in step S502, and acquires the user group item. The control unit 104 of the server 10 searches the user ID item in the user table 1012 based on one or more pieces of user identification information included in the acquired user group, and acquires user information (second user information of the second user) including one or more pieces of user attribute data. Note that the control unit 104 of the server 10 may acquire information such as representative behavior data of the second user group in addition to the user attribute data.

[0098] In step S504, the control unit 104 of the server 10 executes a data creation step for generating information. Specifically, the control unit 104 of the server 10 creates advertisement material (information, data related to advertisements) using the advertisement data stored in the advertisement table 1021 in step S501. For example, the advertisement material is a text advertisement (catch copy), a banner advertisement (image), an advertisement combining an image and text, etc. The advertisement material may be text data, or may be image data, audio data, or video data. The control unit 104 of the server 10 may create an advertisement suited to the purchasing behavior of the second user, taking into consideration the user attribute data and representative behavior data of the user acquired in step S503. In addition, the data created by server 10 is not limited to advertising data, but may be configured to create any information, such as recommendation information (text data, image data, audio data, video data explaining recommended places, products, services, etc.).

[0099] It is preferable to perform the processes in steps S501 to S504 in advance to create a plurality of second user groups corresponding to the plurality of pieces of advertising information, and advertising data corresponding to the plurality of second user groups.

[0100] In step S505, the control unit 104 of the server 10 executes an information providing step of providing information according to a predetermined theme to the second user group extracted in the second extraction step. Specifically, the control unit 104 of the server 10 delivers the advertisement created in step S504 to the second user group identified in step S502. This makes it possible to deliver to the second user group an advertisement suitable for the advertisement theme ID set in step S501.

[0101] In the present disclosure, an example is described in which, when a second user included in a second user group accesses a specific website using a user terminal 20, advertising material provided by the server 10 according to the present disclosure is posted on the website. The second user can access a predetermined website by executing a browser application or the like by operating the input device 206 of the user terminal 20. It is assumed that a script (JavaScript, etc.) such as a predetermined tag for linking with the information processing service according to the present disclosure is embedded in the predetermined website. The control unit 204 of the user terminal 20 executes the embedded script in response to access to the specific website, and transmits the user ID 2011 of the user terminal 20, the URL of the specific website, and other user information to the server 10 according to the present disclosure. As a result, the control unit 104 of the server 10 obtains information indicating that the second user identified by the user ID 2011 (second user ID) has accessed the specific website.

[0102] If the control unit 104 of the server 10 determines that the received second user ID is included in the user IDs of the second user group identified in step S502, it transmits the advertising data generated in step S504 to the user terminal 20 of the second user. The control unit 204 of the user terminal 20 of the second user displays the received advertisement data on the display 2081 of the user terminal 20, and presents the advertisement data to the second user.

[0103] In step S505, the information providing step executes a step of providing information to a predetermined user according to the information on the interests received in the information input step. The control unit 104 of the server 10 may execute an information providing step of providing information according to a predetermined theme to a predetermined user identified based on the second user group extracted in the second extraction step. Specifically, the target of the advertisement is not limited to the second user group. For example, the advertisement may be delivered to a user (target user) other than the second user group whose representative behavior data is similar to that of the second user group. In addition, the target user does not necessarily have to be a user (including the first user and the second user) of the information processing service according to the present disclosure. For example, the user terminal 20 may be configured not to transmit user identification information such as the user ID 2011. In this case, the user can be identified based on information of a wireless base station or the like that is arranged at a predetermined position where the user terminal 20 is located and that can wirelessly connect to the user terminal 20. For example, location information estimated by a wireless base station or the like may be used as the location information of the user terminal 20. In this case, as the user moves, the wireless base station or the like used by the user terminal 20 changes, and the time-series change in the user's location information can be acquired as behavioral data. Note that the acquisition of behavioral data is similar to step S101 of the cluster processing, and therefore a description thereof will be omitted. This allows the control unit 104 of the server 10 to acquire behavioral data of users (target users) other than the second user group. The control unit 104 of the server 10 compares the acquired behavioral data of the target user (target behavioral data) with the behavioral data of the second user group acquired in step S503, and identifies the second user group similar to the target behavioral data. Note that the method of calculating the similarity of the behavioral data is the same as the method of calculating the similarity of the behavioral data described in the cluster processing and user extraction processing, and therefore the description thereof will be omitted. The control unit 104 of the server 10 distributes the advertisement data created by the processing of steps S501 to S504 to the specified second user group to the target users. Note that the advertisement data distribution is similar to the advertisement distribution to the second users, and therefore the description will be omitted.

[0104] The information providing step executes a step of providing information to a predetermined user in accordance with the information on the interests received in the information input step. The information providing step includes a step of providing, in response to an input operation received from a predetermined user, information according to a predetermined theme related to the input operation. The information providing step includes a step of providing information according to a predetermined theme related to the location information in accordance with the location information of a predetermined user. The information providing step is a step of providing information according to a predetermined theme related to the attribute information in accordance with attribute information on the attribute of a predetermined user. The information providing step is a step of providing information according to a predetermined theme related to the attribute information in accordance with the attribute information specified based on the appearance of the predetermined user. The information providing step executes a step of providing information according to a predetermined theme related to the time in accordance with the time.

[0105] Although the target user of this device and the user ID of the media are not the same, they can be matched in the following way. For example, the control unit 104 of the server 10 may identify a second user group related to the target user based on the user attribute data of the target user, the date and time of access to a specific website, the location information of the target user, a specific website accessed by the user terminal 20 in response to an input operation of the target user on the user terminal 20, a history of websites accessed in response to an input operation of the target user on the user terminal 20, an IP address of the access source when accessing, User-Agent of the user terminal, browser information, and other information related to the interests of the target user (for example, identified from information such as the facility genre importance of the user determined from the history of websites accessed and the location information of the user terminal). In addition, the number of second user groups identified does not need to be one, and multiple second user groups may be identified. The control unit 104 of the server 10 may deliver advertisements to the target user by combining known targeting advertising methods using user attribute data of the target user, the date and time of access to a specific website, location information of the target user, specific websites accessed by the target user, history of websites accessed by the target user, etc. For example, when comparing the acquired behavioral data of the target user (target behavioral data) with the behavioral data of a second user group and calculating the similarity (score) with the target behavioral data, one or more second user groups may be identified in consideration of the score by a known targeting advertising method. For example, just as the control unit 104 of the server 10 identifies a second user group having representative behavior data similar to the target behavior data of the target user based on the target behavior data of the target user, the control unit 104 may also identify a second user group having similar user attribute data, location information, date and time of access to a specific website, specific websites accessed, history of websites accessed by the target user, etc. (similar interests). This allows the control unit 104 of the server 10 to deliver to the target user advertisements that are expected to interest the target user, based not only on the target behavior data of the target user, but also on user attribute data, location information, date and time, accessed websites, IP addresses of access sources, User-Agents of user terminals, etc. Also, when a city, ward, town, village, or company is determined from an IP address, advertisements according to clusters of the city, ward, town, village, or company may be delivered. In addition, for example, depending on the time of day, advertisements for restaurants suitable for dinner may be delivered to the user terminal 20 during dinner time.

[0106] Furthermore, the user attribute data of a user does not necessarily need to be acquired from the user's behavior data or the like. For example, assume that the user terminal 20 is a terminal consisting only of a display such as a digital signage. In this case, a camera 2061 provided in front of the digital signage or the like captures an image of the appearance of a user (a specified user) standing in front of the display such as the digital signage. The control unit 204 of the user terminal 20 transmits the captured appearance image data of the specified user to the server 10. The control unit 104 of the server 10 may be configured to identify (infer) the user attributes of the specified user by analyzing the appearance image data. Specifically, the user attributes of the user are identified in the following procedure: The control unit 104 of the server 10 analyzes (morphological analysis) the acquired appearance image data, and thereby can grasp the specific user as an expression vector (appearance vector) consisting of components such as gender, age, hairstyle, clothing, color scheme, belongings, etc. In this case, user attribute data of a group of users having appearances similar to that of a specific user based on the features that make up the specific user's appearance may be used as the user attribute data of that user.

[0107] The specified user does not necessarily have to be one person, and the process described below may be performed by regarding the average appearance of multiple users captured by the camera 2061 as the specified user's appearance.

[0108] The control unit 104 of the server 10 performs clustering processing in advance for a plurality of users (an unspecified number of users) according to the similarity of the expression vectors. The control unit 104 of the server 10 associates a typical appearance vector (representative appearance vector) with typical user attribute data (representative user attribute data) of a user group consisting of a plurality of users, and stores the associated data in a database (not shown). Note that the user attribute data of the users to be processed in the clustering processing is known in advance. The database stores the user group, the representative appearance vector, and the representative user attribute data in association with each other. The control unit 104 of the server 10 searches for a similar record from the representative appearance vectors of a plurality of user groups stored in a database or the like based on the acquired expression vector of the predetermined user, and acquires representative user attribute data stored in association with the representative appearance vector. The control unit 104 of the server 10 adopts the representative user attribute data as the predetermined user attribute data.

[0109] In this disclosure, as an example, based on one appearance vector of a specific user, representative user attribute data of a group of users having a representative appearance vector similar to the appearance vector is set as the user attribute data of the specific user, but this is not limited to this. For example, a given user may have multiple independent appearance vectors, each of which is a combination of at least one of gender, age, hairstyle, clothing, color scheme, belongings, etc. Specifically, a given user may have an independent appearance vector for each part (gender, age, hairstyle, clothing, color scheme, belongings, etc.) that constitutes the user's appearance. In this case, the control unit 104 of the server 10 may perform clustering processing for a plurality of users (an unspecified number of users) in advance according to the similarity of each part. For a user group consisting of a plurality of users, the control unit 104 of the server 10 associates a typical appearance vector (representative appearance vector) with typical user attribute data (representative user attribute data) of the user group for each part in a database (not shown) or the like. In this case, typical user attribute data (representative user attribute data) of a similar user group is associated with each part such as gender, age, hairstyle, clothing, color scheme, belongings, etc. and stored in a database (not shown). The control unit 104 of the server 10 identifies a plurality of pieces of user attribute data for each part constituting the appearance of a predetermined user by referring to a database (not shown). The control unit 104 of the server 10 identifies the user attribute data of the predetermined user as a weighted sum of the plurality of pieces of user attribute data or the like. The weight for each part may be an arbitrary value (a score or the like may be assigned) for each part. Also, a score or the like may be assigned to each combination of parts that make up the appearance.

[0110] In addition, the control unit 104 of the server 10 may identify a group of users having a similar appearance based on the features that make up the appearance of a specific user, using the TF-IDF scores for each feature shown below, and treat the user attribute data of the group of users as the user attribute data of the specific user.

[0111] It is possible to vectorize the age, gender, possessions, hairstyle, and other parts of multiple people, and calculate a score for that set in the same way as calculating the TF-IDF score for text data. Specifically, person data is structured by parts, and each part is treated as an independent "word." - Age: 25, 30, 35, ... - Gender: Male, Female - What to bring: sunglasses, bag, hat, ... - Hairstyle: Short hair, Long hair, Short hair, ... For each part, TF (frequency of occurrence) is calculated by counting the frequency of occurrence of features (words). - Number of times age "25" appears - Number of times the gender "male" appears - Number of times the item "Sunglasses" appears - Number of times the hairstyle "short hair" appears DF (document frequency) is calculated by counting the number of people in which each feature appears. - Number of people with age "25" - Number of characters with the gender "male" - Number of people with the item "Sunglasses" - Number of people with "short hair" hairstyle Based on the calculated DF, the IDF (inverse document frequency) is calculated. The method for calculating the IDF is the same as the procedure for calculating the TF-IDF for normal text documents. The TF-IDF score is calculated by calculating the product of TF and IDF. The TF-IDF score calculated in this way can be used to evaluate how important a particular part (feature) is in a set. For example, if the possession "sunglasses" is common to many people, its IDF will be low, and the importance of possessions unique to a particular person will be evaluated as high. By calculating TF-IDF scores for a person's external features, it is possible to more appropriately identify a group of users similar to a given person based on the features that are most important within the entire population, while taking into account the frequency of occurrence of each feature, just as in document search using TF-IDF scores.

[0112] For information publishers who have stores, etc., it is possible to analyze the area where the store is located and understand the store's customer persona, and then distribute information to a group of users who have a high similarity to the store's customer persona. In addition, at that time, information may be distributed taking into account the trade area.

[0113] Generally, a trade area is a fixed radius that is roughly determined for each facility type as the range that can attract customers to a store, etc. Also, general advertisement distribution systems do not obtain location information with a resolution that can determine whether a visitor is visiting a tourist spot or store from a website, and therefore do not collect visitors to the tourist spot or store, so when attracting customers to a tourist spot or store, they distribute information to users within a certain range of the tourist spot or store. On the other hand, each user group can have population distribution data for the period before and after check-in (or check-out) for each spot, and these can be regarded as the actual commercial area of ​​each spot. When users are extracted directly from a certain spot, the distribution can be regarded as the commercial area of ​​that spot. These commercial areas vary in range depending on the spot and user group, and are commercial areas that reflect not only the influence of distance, transportation means, and population, but also the characteristics of the spot and user group. These commercial areas do not have a simple radius shape, and can take into account the degree to which the population is particularly large or the population ratio is particularly high compared to other user groups. For example, in the case of tourist spots, places close to tourist spots are basically populated by local residents and tourists are not distributed in daily life, so they are not considered to be commercial areas, and places where tourists are distributed in large numbers in daily life can be treated as actual commercial areas. In addition, the period before and after check-in (or check-out) for each spot can be changed arbitrarily depending on the purpose and server load, and multiple trade areas with different periods can be held. In this case, if the period before and after is 6 hours, it can be considered as the range that can attract customers within 6 hours, and if it is 6 months, it can be considered as the range that can attract customers within 6 months. In other words, by considering the trade area, it is possible to deliver advertisements to users who are within a range that can attract customers within 6 hours when you want to attract customers within 6 hours, and to deliver advertisements to users who are within a range that can attract customers within 6 months when you want to attract customers within 6 months. For example, if a store has leftover inventory, it is possible to deliver advertisements when it wants to attract customers who will come on the same day, and if it is a regional tourist destination, it is possible to deliver advertisements when it wants to attract wealthy urban people who will come within 6 months. Furthermore, even if it is determined that the user group is the one to be attracted from the location information and attribute information obtained from the website, if they are outside the trade area, they can be excluded from the ad delivery target. In addition, for example, when a second user group (potential customers) for a particular tourist destination is generated, its distribution may differ from the actual commercial area, but this can be corrected by using the distribution of the original user group or the population distribution of another user group in the period before and after check-in (or check-out) at the tourist destination.

[0114] When targeting is performed by matching statistical big data with personal data that can be obtained within a first party, it is expected that only a small amount of information can be obtained from the target user, and it is difficult to grasp the user image from this small amount of information. However, by matching the information sent from the target user with a combination of multiple statistics of different formats (multimodal), it is possible to grasp the user image. Specifically, in the invention of the present disclosure, a more appropriate user group (cluster) can be identified by combining multiple statistical information of different formats calculated based on behavioral data. This method allows ad distributors and advertising media to achieve optimal targeting using smaller amounts of data, without having to hold large amounts of data that can identify individuals or lead to the identification of individuals. For example, two or three pieces of location information can be obtained from a website user, and the user can be inferred to be an otaku or a band girl based on the occupancy rate of the statistical data of the user group of the mesh or spot that the location information corresponds to. However, even if further location information is obtained, it is not possible to distinguish whether the user is an otaku or a band girl based on the occupancy rate, etc., and by combining this with schedule statistics, it is possible to infer that the user is a band girl, since there are fewer otaku and more band girls using the facility genre that corresponds to the above location information at 10 p.m. In addition, two or three keywords and one location can be obtained from website users, and it can be inferred that the user is an otaku or a band girl based on the statistical data of the user group corresponding to the keywords (feature values ​​of the facility genre).However, even if further keywords are obtained, if it is not possible to distinguish whether the user is an otaku or a band girl based on the feature values, etc., it is possible to infer that the user is an otaku by combining the statistical data of the user group of the mesh or spot, since the occupancy rate of that spot is low for band girls and high for otaku.

[0115] A typical advertising delivery system delivers information according to the content of the website the user is viewing or has viewed, but by matching with location information statistically compiled in various formats, it is possible to provide more appropriate information (ad delivery) that is appropriate to the environment the user is in in the real world (external context information and its changes), which is unrelated to the information the user is viewing or has viewed on the website. For example, if there is representative time-series behavior data for a facility category of a certain user group, such as going to a coffee shop, going to a tourist spot, and returning to a hotel, and if current location information can be acquired indicating that a user who is presumed to be part of the user group is in a coffee shop, it is possible to provide the user with information about tourist spots that is unrelated to the information the user is viewing or has viewed on a website. In addition, when the facility genre "tourist destination" of the above-mentioned time-series representative behavior data is concentrated in a specific spot, it is presumed that the user group is moving to a specific tourist destination, so it is preferable to present information related to the specific tourist destination to the users presumed to be the user group, without recommending other candidate tourist destinations to the users presumed to be the user group. This is also true when the time-series representative behavior data of the spots of the user group is concentrated in a specific tourist destination. On the other hand, when the facility genre "tourist destination" of the above-mentioned time-series representative behavior data is distributed to multiple spots, it is presumed that the user group is sightseeing but not a specific tourist destination, so it is preferable to deliver information about candidate tourist destinations to the users presumed to be the user group. In this way, the information provided can also be changed depending on the degree of dispersion of the spots constituting the time-series representative behavior data of the facility genre for each user group. In addition, the provision of information according to the environment in which the user is placed in the real world (external context information and its transition) is not performed in order to avoid association with information that the user is viewing or has viewed on a website. For example, if a user is viewing a website related to learning and it is estimated that the user has just visited a sports gym in the real world based on the time-series representative behavior data of a user group to which the user is estimated to belong, information combining the content of the website the user is viewing and the environment in which the user is placed in the real world (external context information and its transition) may be provided, such as presenting "the best study method after exercise." Information on real-world locations is not necessarily provided. For example, if the time-series representative behavior data of the spots and facility genres of the user group is not characteristic or important data for the user group, information not related to real-world locations according to the interests of the user group (feature amount of the facility genre) may be provided.

[0116] In addition, for example, in the time-series representative behavior data of the facility genres of all users, if there are many izakayas in the facility genre before checking in to a ramen shop, the users who check in to izakayas may be grouped together to provide information that is regarded as "a user group taking an action before eating ramen" and "a user group who eats ramen as their next action." If the facility genre before checking in to a ramen shop is different for each user group, the users who check in to the facility genre before checking in to the ramen shop for each user group can be grouped together to be regarded as "a user group taking an action before eating ramen" and "a user group who eats ramen as their next action." In addition, by combining actions before and after the ramen shop within 6 hours, it is possible to create a "user group who can visit the shop within 6 hours and eat ramen as their next action." This allows an advertiser who wants to quickly attract customers to the ramen shop to select a "user group who can visit the shop within 6 hours and eat ramen as their next action" without complex judgment. When the advertiser is a tourist destination or a store, the user group extracted from the tourist destination or store is the user group that is suited to the tourist destination or store, and by treating the user group as a first user group and identifying representative behavioral data and a second user group, even more suitable information can be provided. In addition, when the advertiser is a tourist destination or a store, for example, if a group of users extracted from the tourist destination or store have high characteristics of prep schools and renovations in their daily behavior data, and high characteristics of athletic facilities and natural parks in their daily behavior data for the period before and after checking in to the tourist destination or store, by using both keywords and matching them with keywords obtained from website users, more appropriate targeting can be achieved that reflects the users' interests in their daily lives and their interests when visiting the tourist destination or store.

[0117] When the context of the actions on that day is added to make the picture more complicated, there are roughly nine trends. 1001 in FIG. 15 indicates that spot X is a place where there is no regularity in forward and backward movements. Reference numeral 1002 in FIG. 15 indicates that spot X is a destination point for multiple spots of the same facility category. Reference numeral 1003 in FIG. 15 indicates that spot X is a location where multiple spots of the same facility genre head towards a random location. Reference numeral 1004 in FIG. 15 indicates that spot X is a location where multiple facilities of the same facility genre head towards the same facility genre. Reference numeral 1005 in FIG. 15 indicates that spot X is a location from which a plurality of users of the same facility genre head to the same spot. Reference numeral 1006 in FIG. 15 indicates that spot X is a location for heading to the same spot from a location where there is no regularity in forward and backward movements. 1007 in FIG. 15 indicates that spot X is a location for going from the same spot to the same spot from forward and backward actions. Reference numeral 1008 in FIG. 15 indicates that spot X is a location from which one spot can head to a plurality of spots of the same facility genre. 1009 in FIG. 15 indicates that spot X is heading from the same spot to a location with no regularity in subsequent movements.

[0118] The significance of Spot X itself as a spot or facility genre for a cluster can be understood by combining it with the behavior of that day at spots where large numbers of people gather regardless of user group, such as train stations or airports, or with the overall behavior of that day starting from check-ins in a certain area. 1101 in FIG. 16 represents spot X being one of the places in the lawless world. 1102 in FIG. 16 represents spot X as the central location within the no-law. 1103 in Figure 16 indicates that spot X is the location of One of Them, the origin (destination) of the Lawless Center. 1104 in Figure 16 indicates that spot X is one of the locations in the larger stream. In FIG. 16, 1105 indicates that spot X is the center of a large flow. 1106 in Figure 16 indicates that spot X is one of the locations that is complementary to the center of the larger flow.

[0119] Spots or facility genres where clusters (including the whole) share the same behavior can be further categorized by the length of stay and frequency of visits. The more variance there is before and after, and the longer the stay time compared to the time before and after, the more likely it is a destination. It can be seen that places where people spend the longest time and visit frequently are places of interest and facilities such as schools and workplaces. It can be seen that places where the stay time is long and the frequency is low are spots and facility genres such as events and tourist spots. It can be seen that places where the stay time is short and the frequency is high are transportation hubs such as train stations and airports, as well as facility types. It can be inferred that places where people spend a short time and visit infrequently are places or facility genres that are sacred places for specialized shopping or specific groups of users.

[0120] There is a master-slave relationship between spots, where the destination or intended action is the master, and the spots or actions that complement it can be seen as the slave. For example, shrines and souvenir shops have a master-servant relationship, so it is not like "If you buy a souvenir, go to the shrine," but rather, "If you go to the shrine, buy a souvenir," and it is appropriate to provide information that is in line with this relationship.

[0121] These facility genre features indicate the degree of association with facility genres for each user group, and trade areas and distribution indicate the degree of association with spots (areas) for each user group, so facility genres / spots (areas) that are highly associated with a certain user group may be visualized using a co-occurrence network representation method.

[0122] The information providing step may include a step of providing information according to a predetermined theme related to the combination in accordance with a combination of at least two or more of: one or more pieces of information related to the user's interests; one or more operations inputted from a specified user; one or more pieces of location information indicating the location of the specified user; one or more pieces of attribute information related to the attributes of the specified user; and one or more times. Specifically, the second user group may be identified based on a combination of at least two or more of the target user's behavioral data (including location information), the target user's user attribute data, the date and time of access to a specific website, the specific website accessed in response to the target user's input operation on the user terminal 20, other information related to the interests of the target user, such as the history of websites accessed in response to the target user's input operation on the user terminal 20, the IP address from which the website was accessed, the user terminal's User-Agent and browser information, etc. This enables the control unit 104 of the server 10 to deliver to the target user more suitable advertisements that are expected to interest the target user, based not only on the target behavior data of the target user, but also on a combination of user attribute data, date and time, accessed websites, etc.

[0123] <Basic computer hardware configuration> 14 is a block diagram showing the basic hardware configuration of a computer 90. The computer 90 includes at least a processor 901, a main storage device 902, an auxiliary storage device 903, and a communication IF 991 (interface). These are electrically connected to each other by a communication bus 921.

[0124] The processor 901 is hardware for executing an instruction set described in a program, and is composed of an arithmetic unit, a register, a peripheral circuit, and the like.

[0125] The main memory device 902 is for temporarily storing programs, data to be processed by the programs, etc. For example, it is a volatile memory such as a DRAM (Dynamic Random Access Memory).

[0126] The auxiliary storage device 903 is a storage device for saving data and programs, such as a flash memory, a hard disk drive (HDD), a magneto-optical disk, a CD-ROM, a DVD-ROM, or a semiconductor memory.

[0127] The communication IF 991 is an interface for inputting and outputting signals for communicating with other computers via a network using a wired or wireless communication standard. The network is composed of the Internet, a LAN, various mobile communication systems constructed by wireless base stations, etc. For example, the network includes 3G, 4G, 5G mobile communication systems, LTE (Long Term Evolution), wireless networks that can connect to the Internet via a specified access point (e.g., Wi-Fi (registered trademark)), etc. In the case of wireless connection, communication protocols include, for example, Z-Wave (registered trademark), ZigBee (registered trademark), Bluetooth (registered trademark), etc. In the case of wired connection, the network also includes a network that is directly connected by a USB (Universal Serial Bus) cable or the like.

[0128] It should be noted that the computer 90 can be virtually realized by distributing all or part of each hardware configuration among multiple computers 90 and connecting them together via a network. In this way, the computer 90 is a concept that includes not only a computer 90 housed in a single housing or case, but also a virtualized computer system.

[0129] <Basic functional configuration of computer 90> A description will now be given of the functional configuration of a computer realized by the basic hardware configuration (FIG. 14) of a computer 90. The computer comprises at least the functional units of a control unit, a storage unit, and a communication unit.

[0130] The functional units of the computer 90 can also be realized by distributing all or part of the functional units among multiple computers 90 connected to each other via a network. The computer 90 is a concept that includes not only a single computer 90 but also a virtualized computer system.

[0131] The control unit is realized by the processor 901 reading out various programs stored in the auxiliary storage device 903, expanding the programs in the main storage device 902, and executing processes according to the programs. The control unit can realize functional units that perform various information processing depending on the type of program. In this way, the computer is realized as an information processing device that performs information processing.

[0132] The functions performed by the components described herein may be implemented in circuitry or processing circuitry, including general purpose processors, application specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), a CPU (a Central Processing Unit), conventional circuits, and / or combinations thereof, programmed to perform the functions described. Processors include transistors and other circuits and are considered to be circuitry or processing circuitry. A processor may be a programmed processor that executes a program stored in a memory. In this specification, a circuitry, unit, or means is hardware that is programmed to realize or performs the described functions, which may be any hardware disclosed in this specification or any hardware known to be programmed to realize or perform the described functions. If the hardware is a processor considered to be a type of circuitry, the circuitry, means, or unit is a combination of the hardware and software used to configure the hardware and / or processor.

[0133] The storage unit is realized by a main storage device 902 and an auxiliary storage device 903. The storage unit stores data, various programs, and various databases. Furthermore, the processor 901 can secure a storage area corresponding to the storage unit in the main storage device 902 or the auxiliary storage device 903 in accordance with a program. Furthermore, the control unit can cause the processor 901 to execute processes of adding, updating, and deleting data stored in the storage unit in accordance with the various programs.

[0134] The term database refers to a relational database, which is used to manage sets of data called masters and tables in a tabular format structurally defined by rows and columns, by associating them with each other. In a database, a table is called a table or master, a column in a table is called a column, and a row in a table is called a record. In a relational database, relationships between tables and masters can be set and associated. Usually, a column that serves as a primary key for uniquely identifying a record is set in each table and each master, but setting a primary key in a column is not essential. The control unit can cause the processor 901 to add, delete, and update records in a specific table or master stored in the storage unit according to various programs. By storing data, various programs, and various databases in the storage unit, it can be considered that the information processing device and information processing system according to the present disclosure are manufactured.

[0135] In addition, the database and master in this disclosure may include any data structure (such as a list, a dictionary, an associative array, or an object) in which information is structurally defined. The data structure also includes data that can be considered as a data structure by combining data with a function, class, method, or the like written in any programming language.

[0136] The communication unit is realized by the communication IF 991. The communication unit realizes a function of communicating with other computers 90 via a network. The communication unit can receive information transmitted from other computers 90 and input the information to the control unit. The control unit can cause the processor 901 to execute information processing on the received information in accordance with various programs. In addition, the communication unit can transmit information output from the control unit to other computers 90.

[0137] <Additional Notes> The matters described in the above embodiments will be supplemented below.

[0138] (Appendix 1) A program to be executed by a computer having a processor and a memory unit, the program executing a behavior acquisition step (S101) in which the processor acquires behavioral data relating to the behavior of a plurality of users, a first extraction step (S302) in which a first user group consisting of one or more users related to a predetermined theme from the plurality of users based on the behavioral data acquired in the behavior acquisition step, a representative acquisition step (S303) in which representative behavior data representative of the first user group extracted in the first extraction step, and a second extraction step (S304) in which a second user group consisting of one or more users from the plurality of users based on the representative behavior data of the first user group. This makes it possible to extract a second user group (potential customers) that is different from the first user group but is expected to potentially exhibit similar behavior patterns to the first users based on the typical behavior patterns (representative behavior data) of the first user group related to a particular theme or event.

[0139] (Appendix 2) The program described in Appendix 1, wherein the first extraction step (S302) is a step of extracting a first user group related to an event held during a specified period, and the representative acquisition step (S303) is a step of acquiring representative behavior data representing the first user group during a period other than the specified period. In this way, by extracting a first user group related to an event held at a specific date and time and place, it is possible to extract a first user group that exhibits more homogeneous, high-quality, and similar behavioral patterns. Based on the similarity of the behavioral patterns of the first user group during periods other than the event, it is possible to extract a second user group (potential customers) that is expected to potentially exhibit behavioral patterns similar to those of the first user.

[0140] (Appendix 3) The program described in Appendix 2, wherein the first extraction step (S302) is a step of extracting a first user group based on the behavior of multiple users before and after a specified period, and excluding one or more users who are not related to an event being held during the specified period. This makes it possible to exclude users who are not similar to the first user group in terms of the similarity of their behavioral patterns from the first user group, and to extract a first user group that exhibits more homogeneous, high-quality, and similar behavioral patterns.

[0141] (Appendix 4) A program described in any of Appendices 1 to 3, wherein a processor executes a grouping step (S102) in which a plurality of users are classified into a plurality of groups based on the similarity of the behavioral data acquired in the behavior acquisition step, and a second extraction step (S304) in which one or more groups having representative behavioral data similar to the representative behavioral data of the first user group are extracted as a second user group from the plurality of groups. This makes it possible to extract, from among groups (clusters) obtained by grouping (clustering) a plurality of users according to the similarity of their behavior patterns, groups having behavior patterns similar to those of the first user group as the second user group.

[0142] (Appendix 5) A program described in any one of Appendices 1 to 4, in which a processor executes an information providing step (S505) of providing information according to a predetermined theme to a specific user identified based on a second user group extracted in the second extraction step. This makes it possible to provide information related to a predetermined theme that is expected to interest a predetermined user (potential customer) identified based on a second user group that is expected to potentially exhibit a behavior pattern similar to that of the first user, thereby improving the response to the provided information. The predetermined user may be a user included in a second user group.

[0143] (Appendix 6) The program described in Appendix 5, wherein a processor executes an information input step (S501) of accepting input of information relating to a user's interests, and an information providing step (S505) of providing information to a specific user in accordance with the information relating to the interests accepted in the information input step. This makes it possible to provide information to predetermined users who are expected to have an interest in response to input of information related to the user's interests received from an information publisher, including an information medium, etc., thereby improving the effectiveness of providing information.

[0144] (Appendix 7) The program according to claim 5 or 6, wherein the information providing step (S505) is a step of providing, in response to an input operation received from a predetermined user, information according to a predetermined theme related to the input operation. This makes it possible to provide a specific user with information that is more in line with the interests of that specific user, in accordance with a specific operation that the specific user has performed on the Internet. This improves the effectiveness of providing information. For example, it is possible to provide information that is more effective for the user, taking into account the genre and article content of the website visited by the user.

[0145] (Appendix 8) The program according to any one of appendix 5 to 7, wherein the information providing step (S505) is a step of providing information according to a predetermined theme related to the location information of a predetermined user in accordance with the location information of the predetermined user. This makes it possible to provide a specific user with information that is more in line with the interests of each user in accordance with the user's location information, thereby improving the effectiveness of providing information.

[0146] (Appendix 9) The program according to any one of appendixes 5 to 8, wherein the information providing step (S505) is a step of providing information according to a predetermined theme related to attribute information on a predetermined user attribute, in accordance with the attribute information. This makes it possible to provide a specific user with information that is more in line with the interests of each user, depending on the user's attribute information such as the user's gender, age, occupation, etc., thereby improving the effectiveness of providing information.

[0147] (Appendix 10) The program according to claim 9, wherein the information providing step (S505) is a step of providing information according to a predetermined theme related to attribute information identified based on the appearance of a predetermined user. This makes it possible to provide a specific user with information that is more in line with the interests of each user in accordance with the user's attribute information that is specified based on the user's appearance, thereby improving the effectiveness of providing information.

[0148] (Appendix 11) The program according to any one of appendixes 5 to 10, wherein the information providing step (S505) is a step of providing information according to a predetermined theme related to the time in accordance with the time. This makes it possible to provide a specific user with information that matches the user's interests and concerns depending on the time period, thereby improving the effectiveness of providing information.

[0149] (Appendix 12) The information providing step (S505) is a step of providing information according to a predetermined theme related to a combination of at least two or more of the following: one or more pieces of information related to the user's interests; one or more operations inputted from a specified user; one or more pieces of location information indicating the location of the specified user; one or more pieces of attribute information related to the attributes of the specified user; and one or more times, in accordance with the combination. This makes it possible to provide a predetermined user with information that matches the user's interests in accordance with a plurality of conditions, thereby improving the effectiveness of providing information.

[0150] (Appendix 13) A program to be executed by a computer having a processor and a memory unit, the processor executing a behavior acquisition step (S101) of acquiring behavioral data related to the time-series behavior of a plurality of users, a grouping step (S102, S304, S502) of classifying the plurality of users into a plurality of user groups related to each of a plurality of different themes based on the similarity of the behavioral data acquired in the behavior acquisition step, a similarity calculation step (S501) of calculating a similarity between a predetermined user group and the plurality of user groups, and a similarity presentation step (S501) of presenting the similarity for each of the plurality of user groups calculated in the similarity calculation step in association with the theme related to the user group, the similarity calculation step (S501) being a step of calculating a similarity between the behavioral data of a plurality of users included in the predetermined user group and the behavioral data of a plurality of users included in each of the plurality of user groups. This makes it possible to present the similarity of behavioral patterns between a user group related to a given theme and a plurality of user groups related to other themes. For example, it is possible to present the similarity between representative behavior data of a user group regarding a certain theme and representative behavior data of a plurality of user groups regarding another theme. This can be used as reference information when delivering advertisements to a group of users.

[0151] (Appendix 14) The program described in Appendix 13, in which a processor executes a persona creation step (S501) of creating a person image characterizing a specific user group based on behavioral data of a plurality of users included in the specific user group, and a persona presentation step (S501) of presenting the person image created in the persona creation step. This makes it possible to present typical appearances, styles, etc. of users included in a user group. This can be used as reference information when delivering advertisements to a group of users.

[0152] (Appendix 15) The program described in Appendix 13, in which a processor executes a behavior aggregation step (S501) of aggregating the number of one or more predetermined behavioral data included in the behavioral data of a plurality of users included in a predetermined user group, and a behavior presentation step (S501) of presenting an index value based on the number of the one or more predetermined behavioral data aggregated in the behavior aggregation step in association with the one or more predetermined behavioral data. This makes it possible to present what behavioral patterns the users included in the user group have. By referring to the behavioral patterns of a plurality of users included in a user group, it is possible to use the patterns as reference information when delivering advertisements to the user group.

[0153] (Appendix 16) The program described in Appendix 15, wherein the behavior aggregation step (S501) is a step of aggregating the number of facility genres related to one or more predetermined facility types contained in the behavior data of a plurality of users included in a predetermined user group, and the behavior presentation step (S501) is a step of presenting an index value based on the number of the one or more predetermined facility genres aggregated in the behavior aggregation step in association with the one or more predetermined facility genres. This makes it possible to present what facility genres the users included in the user group are visiting. By checking what facility genres a plurality of users included in a user group are visiting, it is possible to use this information as reference information when delivering advertisements to the user group.

[0154] (Appendix 17) The program described in Appendix 15, wherein the behavior aggregation step (S501) is a step of aggregating the number of one or more predetermined facilities included in the behavior data of a plurality of users included in a predetermined user group, and the behavior presentation step (S501) is a step of presenting an index value based on the number of one or more predetermined facilities aggregated in the behavior aggregation step in association with the one or more predetermined facilities. This makes it possible to present what facilities users included in the user group are visiting. By checking what facilities a plurality of users included in a user group are visiting, it is possible to use this information as reference information when delivering advertisements to the user group.

[0155] (Appendix 18) The program described in Appendix 13, wherein the processor executes an attribute aggregation step (S501) of aggregating the number of one or more predetermined attribute information items included in attribute information relating to attributes of a plurality of users included in a predetermined user group, and an attribute presentation step (S501) of presenting an index value based on the number of the predetermined attribute information items aggregated in the attribute aggregation step, in association with the predetermined attribute information items. This makes it possible to present what attributes the users included in the user group have. By checking the attributes of a plurality of users included in a user group, it is possible to use the information as reference when delivering advertisements to the user group.

[0156] (Appendix 19) A method implemented on a computer having a processor and a memory, the processor performing all of the steps performed in the invention according to any one of claims 1 to 18. This makes it possible to extract a second user group (potential customers) that is different from the first user group but is expected to potentially exhibit similar behavior patterns to the first users based on the typical behavior patterns (representative behavior data) of the first user group related to a particular theme or event. This also includes methods implemented using generative AI, large-scale language models, etc.

[0157] (Appendix 20) An information processing device comprising a control unit and a memory unit, wherein the control unit executes all of the steps executed in the invention according to any one of Supplementary Note 1 to Supplementary Note 18. This makes it possible to extract a second user group (potential customers) that is different from the first user group but is expected to potentially exhibit similar behavior patterns to the first users based on the typical behavior patterns (representative behavior data) of the first user group related to a particular theme or event. This also includes information processing devices consisting of generative AI, large-scale language models, etc.

[0158] (Appendix 21) A system comprising means for performing all the steps performed in any of the inventions according to appendix 1 to appendix 18. This makes it possible to extract a second user group (potential customers) that is different from the first user group but is expected to potentially exhibit similar behavior patterns to the first users based on the typical behavior patterns (representative behavior data) of the first user group related to a particular theme or event. This also includes systems consisting of generative AI, large-scale language models, etc. [Explanation of symbols]

[0159] 1 system, 10 server, 101 memory unit, 104 control unit, 106 input device, 108 output device, 20 user terminal, 201 memory unit, 204 control unit, 206 input device, 208 output device, 30 administrator terminal, 301 memory unit, 304 control unit, 306 input device, 308 output device

Claims

1. A program to be executed by a computer having a processor and a storage unit, The processor, a behavior acquisition step of acquiring behavior data relating to a plurality of pieces of location information, facility information, or facility genre information visited by a plurality of users; a first extraction step of extracting a first user group including one or more users related to a predetermined theme from among the plurality of users based on the behavior data acquired in the behavior acquisition step; a representative acquisition step of acquiring representative behavior data representative of the first user group extracted in the first extraction step; A second extraction step of extracting a second user group consisting of one or more users having behavior data similar to the representative behavior data of the first user group from among the plurality of users; Run the first extraction step is a step of extracting the first user group by excluding a plurality of users who have first behavioral data related to an event held in a predetermined period and who have second behavioral data before and after the predetermined period and who have a low relevance to the event, the representative acquisition step is a step of acquiring representative behavior data which is representative behavior data of a part or all of the first user group, the plurality of users having similar daily behavior data in a period other than the predetermined period; The second extraction step is a step of extracting a second user group consisting of one or more users whose daily behavior data in a period other than the predetermined period has behavior data similar to the representative behavior data of the first user group. program.

2. The representative acquisition step is a step of acquiring representative behavior data which is representative behavior data of a part or all of the first user group, the plurality of users having similar daily behavior data in a period other than the day on which the event is held. The program according to claim 1.

3. The processor, a statistics creation step of creating statistics of a population distribution based on the behavior data of the second user group extracted in the second extraction step; Execute The program according to claim 1.

4. The statistics creating step is a step of creating statistics of a population distribution of location information, facility information, or facility genre based on the behavior data of the second user group. The program according to claim 3.

5. The processor, a grouping step of classifying the plurality of users into a plurality of groups based on a similarity of daily behavior data during a period other than the predetermined period acquired in the behavior acquisition step; Run The second extraction step is a step of extracting, from among the plurality of groups, one or more groups having representative behavior data similar to representative behavior data of the first user group as the second user group. The program according to claim 1.

6. the grouping step is a step of classifying the plurality of users into a plurality of groups based on a population distribution of spots identified based on a similarity of location information, facility information, or facility genre information visited by the users during a period other than the predetermined period acquired in the behavior acquisition step; The second extraction step includes a step of correcting the second user group in consideration of a population distribution of spots identified based on location information, facility information, or facility genre information visited by the user during a period other than the predetermined period acquired in the behavior acquisition step. The program according to claim 5.

7. The processor, an information providing step of providing information according to the predetermined theme to a predetermined user identified based on the second user group extracted in the second extraction step; Execute The program according to claim 1.

8. The processor, an information providing step of providing information according to the predetermined theme to a predetermined user identified based on the statistics of the second user group created in the statistics creating step; Execute The program according to claim 3.

9. The processor, An information input step of accepting input of information related to a user's interests; Run The information providing step is a step of providing information to the predetermined user in accordance with the information on the interests received in the information input step.

9. The program according to claim 7 or 8.

10. the information providing step is a step of providing information according to the predetermined theme related to an input operation received from the predetermined user in response to the input operation, 9. The program according to claim 7 or 8.

11. The information providing step is a step of providing information according to the predetermined theme related to the location information of the predetermined user in accordance with the location information of the predetermined user. The program according to claim 7.

12. The information providing step is a step of providing information according to the predetermined theme related to attribute information in accordance with attribute information on the attribute of the predetermined user. The program according to claim 7.

13. The information providing step is a step of providing information according to the predetermined theme related to the attribute information, in accordance with the attribute information identified based on the appearance of the predetermined user. The program according to claim 12.

14. The information providing step is a step of providing information according to the predetermined theme related to the time according to the time. The program according to claim 7.

15. The information providing step includes: one or more pieces of information regarding a user's interests; One or more operations received as input from the predetermined user; and One or more pieces of location information indicating the location of the predetermined user; One or more pieces of attribute information relating to attributes of the predetermined user; One or more times; providing information according to the predetermined theme related to the combination in accordance with at least any two or more combinations of the above; The program according to claim 7.

16. The information providing step is a step of providing information according to the predetermined theme related to the location information in accordance with two or more pieces of location information indicating the location of the predetermined user. The program according to claim 7.

17. A method implemented on a computer having a processor and a memory, the processor performing all of the steps performed in any of the inventions according to claims 1 to 8, 11 to 16.

18. 17. An information processing apparatus comprising a control unit and a storage unit, wherein the control unit executes all of the steps executed in the invention according to any one of claims 1 to 8 and 11 to 16.

19. A system comprising means for executing all the steps performed in any one of claims 1 to 8 and 11 to 16.

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