Program, method, information processing device, and system

Abstract

To solve the problem that another user group (latent customer) similar to a typical action pattern (representative action data) of a first user group related to a specific theme and event cannot be specified.SOLUTION: In a program to be executed by a computer having a processor and a storage section, the processor executes: an action acquisition step of acquiring action data regarding actions of multiple users; a first extraction step of extracting a first user group comprising one or multiple users related to a predetermined theme from among multiple users on the basis of the action data acquired in the action acquisition step; a representative acquisition step of acquiring representative action data representing the first user group extracted in the first extraction step; and a second extraction step of extracting a second user group comprising one or multiple users from multiple users on the basis of the representative action data of the first user group.SELECTED DRAWING: Figure 13

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

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-197460 Summary of the Invention [Problem to be solved by the invention]

[0004] Due to the declining domestic population and diversifying lifestyles, hobbies, and preferences, appropriate marketing activities that capture customer personas are more important than ever before. However, data-based persona analysis and marketing activities based on the analysis results each require a high level of expertise. In addition, the growing momentum for personal information protection, including the abolition of third-party cookies, has made it difficult to carry out individual tracking-based marketing that relies on the collection of large amounts of personal data across businesses. This has created a need for marketing methods that match big data, statisticalized in various formats that can capture customer personas, with personal data that can be obtained within first-party websites. However, there is a challenge 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 group of users related to a specific theme or event. Therefore, the present disclosure has been made to solve the above problem, and its purpose is to more accurately capture customer profiles by providing technology that identifies 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. [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 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 predetermined theme; a representative acquisition step in which representative behavior data representing 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. [Effects 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 specific theme or event, and to identify user groups from user behavioral data. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 2 is a block diagram showing the functional configuration of the system 1. [Figure 2] FIG. 2 is a block diagram showing the functional configuration of the server 10. [Figure 3] FIG. 2 is a block diagram showing the functional configuration of a user terminal 20. [Figure 4] 3 is a block diagram showing the functional configuration of an administrator terminal 30. FIG. [Figure 5] FIG. 10 is a diagram showing the data structure of a user table 1012. [Figure 6]FIG. 10 is a diagram showing the data structure of a behavior table 1013. [Figure 7] FIG. 10 is a diagram showing the data structure of a theme table 1014. [Figure 8] FIG. 10 is a diagram showing the data structure of a cluster table 1015. [Figure 9] FIG. 10 is a diagram showing the data structure of an advertisement table 1021. [Figure 10] 10 is a flowchart showing the operation of cluster processing. [Figure 11] 10 is a flowchart showing the operation of a user extraction process. [Figure 12] 10 is a flowchart showing the operation of an advertisement distribution process. [Figure 13] 10 is a screen example showing the operation of the 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 it. [Figure 16] This is a diagram showing the relationship between each spot taking into consideration the overall picture. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In all drawings describing the embodiments, common components are designated by the same reference numerals, and repeated description will be omitted. Note that the following embodiments do not unduly limit the content of the present disclosure described in the claims. Furthermore, not all components shown in the embodiments are necessarily essential components of the present disclosure. Furthermore, each drawing is a schematic diagram and is not necessarily a precise illustration.

[0009] <System 1 Configuration> The system 1 in the present disclosure is an information processing system that extracts a predetermined user group based on user behavior data. 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. As shown in FIG. 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 an arithmetic unit 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 will be omitted.

[0011] The system 1 in the present disclosure can be used as a marketing system / advertising distribution 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 distribution systems do not include a processing unit that performs user analysis on the advertiser side, and generally only display advertisements to users who match 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 behaviors. However, this had problems such as a small target population, attributes and distribution being biased toward the user extraction point, and the target users being specific behaviors, 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 their location information, there is no data that indicates a user group called the wealthy, so you can target users who have a check-in history at golf courses or luxury hotels.However, if you set the condition that the user has visited both a golf course and a luxury hotel, the number of target users will be reduced, and if you set the condition that the user has visited either one of them, the targeting accuracy will decrease.In addition, it is not clear from the data whether the wealthy people that the ad distributor is targeting both visit golf courses and luxury hotels. The system 1 in the present disclosure can increase the number of target people, reduce the geographical influence biased towards the sampling point, and refine the user group to be considered wealthy.

[0013] <Server 10 configuration> The server 10 is an information processing device that provides an information processing service that extracts a predetermined user group based on user behavior data. 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 that stores user identification information for identifying a user. The user identification information is an item that is set with a unique value for each user. The user attribute data is an item that stores a character string indicating the user's gender, age, occupation, and other arbitrary user attributes. The user attribute data includes the user's unique data (unique information), such as the user's gender and age.

[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.

[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 about the behavior includes information about spots that indicate the user's location on a specific date and time. For example, the information about 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 separated by a specified latitude and longitude. Location information includes addresses and national local government codes assigned to administrative divisions 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, it includes the names of the facilities visited by the user on a specific date and time and their location information. For example, facility information about facilities may include Tokyo Tower, Tokyo Station, Haneda Airport, Tokyo International Exhibition Center, Ise Shrine, as well as the names and location information of specific stores such as ●● Ramen Funabashi Ekimae Branch. Behavioral data includes information about the type (genre) of facilities visited by a user on a specific date and time. For example, types of facilities include train stations, airports, shrines, art galleries, museums, etc., as well as information indicating the type of store, such as ramen shops or chain store names. Behavioral data includes information about events that users visited on specific dates and times. For example, event information includes the name of the event (e.g., a live performance by a specific artist A, Comic Market), and location information about the event location. · Behavioral data includes the user's facility genre usage score and feature values. The behavioral data includes information about the user's behavior on a specific date and time, as well as information about the user's behavior before and after the specific date and time (hereinafter referred to as "before and after data"). 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 that stores the date and time when new behavioral data is stored or updated in the behavior table. Note that 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), etc. may be stored. The behavioral information may be configured to be stored at any time interval or granularity. Information about user behavior before and after a specific event or the like also includes information about which facility genres the user visited before and after visiting (checking in or checking out) a specific facility genre. Feature values ​​are values ​​compared to the entire user population, and are higher when usage is not high for all users but is high for a specific user group. On the other hand, general advertising fees are set according to overall usage. By using the features of a user group, it is possible to deliver advertisements that select effective keywords, locations, websites, and web pages for that user group at a relatively low cost. The total number of users 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 an area within a radius of Xm.

[0020] The following statistics can be generated based on the behavioral data: 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 facility genre information that belongs to that location information, statistics on the feature values ​​of facility genres and statistics on the feature values ​​of facility genres by time, including data before and after the visit, can be generated. Statistics of relationship values ​​for each spot (values ​​such as facility genre feature value x population occupancy rate x population combination). Statistics of relationship values ​​(such as facility genre feature value x population occupancy rate x population combination) for each spot on the schedule. Demographic statistics based on behavioral data other than data before and after visiting the spot. Statistics on the feature values ​​of facility genres based on behavioral data other than data before and after visiting the spot. Statistics on relationship values ​​for each spot (values ​​such as facility genre feature value x population occupancy rate x population combination) 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 primary behavioral data, representative behavioral data, secondary behavioral data, and clusters.

[0021] It is also possible to create statistics on behavior before and after a visit to a facility category, starting from checking in or checking out of that 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 facilities are not taken into consideration.

[0022] While search and purchase information can only be obtained from search and shopping sites, location information can be obtained from any website. In other words, while there are only a limited number of websites that can match statistical data on search and purchase information, statistical data on location information can be matched with any website. Furthermore, general advertising distribution systems are based on the user's activity history on the website that displays the advertisement and cannot take into account user activity in the real world, making them unsuitable for analyzing and attracting users to tourist spots and stores. In other words, statistical data on location information, which is the user's activity history in the real world, can be used to analyze and attract users to tourist spots and stores in addition to traditional advertising activities.

[0023] <Example of calculating the relationship between clusters and spots> Each value of Bandai Feature value of the facility genre to which the spot belongs: 10 Population occupancy rate of the spot: 30% Population of the spot: 120 When this happens, the relationship value between the band girl and the spot will be 360. Each value of otaku Feature value of the facility genre to which the spot belongs: 1 Population occupancy rate 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 a user's location information 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 obtaining multiple location information and accumulating the spot-related values.

[0024] The theme table 1014 is a table for storing and managing information (theme information) about themes related to a user group made up of a plurality of users. The theme includes information about 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 trains, anime, cosplay, and a specific artist A (anime otaku, cosplay otaku, etc.). In addition, the theme may include character string information indicating the 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.

[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. 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 group of users related to a theme. Specifically, in the present disclosure, information that defines conditions related to the behavioral information stored in the behavior table is stored. Theme conditions are information that defines conditions related to behavioral data and date and time. For example, information that defines behaviors such as demographic data by gender and age, administrative districts such as cities, wards, towns, and villages, weekdays, holidays, time periods, and the Rokuyo calendar, or being in a specific geographical space (Tokyo Big Sight) on a specific date and time (August XX), may be stored. Note that the theme conditions may include conditions related to a combination of multiple pieces of behavioral data. For example, the theme conditions may include multiple pieces of chronologically consecutive behavioral data, such as attending a concert and then going to a ramen restaurant. The first user group is an item that stores user IDs included in a user group related to the 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 from a convenience store to Tokyo Big Sight, a ramen shop, and an izakaya in that order. Note that the first behavioral data does not necessarily need to store a single predetermined behavioral data, and may be configured to store a plurality of representative (typical) behavioral data related to the user group. Furthermore, each of the plurality of behavioral data may be associated with a predetermined score according to the degree of association with 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 about 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.

[0027] The cluster ID is an item that stores second cluster identification information for identifying a second cluster. The second cluster identification information is an item that has a unique value set for each piece of second cluster information. The user group is an item that stores the user IDs included in the 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 relating to advertisements (advertising information). The 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 advertisements. The advertisement identification information is an item for which a unique value is set for each advertisement information. The advertising data is an item that stores information about advertisements. Specifically, the advertising data may include information about the interests of a user or cluster, attribute information of the user or cluster, and unique information. The advertising data also includes information such as the text of the advertisement to be delivered and an avatar image. For tourist spots and brick-and-mortar stores, the advertising 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 users to whom advertisements are to be delivered. Specifically, it is an item for storing theme identification information for specifying a theme stored in the theme table 1014. This item enables targeted advertising that narrows down the users to whom advertisements are 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 information about users who wish to use the service according to the present disclosure in the user table 1012. 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 any information processing terminal, enters 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, completing the user registration. This allows the user stored in the user table 1012 to use the service. Before the user registration control unit 1041 registers the user information in the user table 1012, the service provider may conduct 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 may be automatically set by the user registration control unit 1041. User registration is not required 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 tablet, or may be a stationary personal computer (PC) or laptop PC. It may also be a wearable terminal such as a head mounted display (HMD) or a wristwatch 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 digital signage that 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, this is the case with digital signage.

[0033] To obtain a user's location information, information such as a Global Positioning System (GPS) installed on a user's smartphone, smartwatch, or the like may be used. Alternatively, location information may be obtained by installing a device such as a magnetic reader or IC reader and having the user have the device read the user's device. Furthermore, location information obtained from the base station of a communication device such as a mobile phone or from Wi-Fi (Wireless Fidelity, registered trademark) may also 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 the user's account ID. 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 to identify the user 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 or the like 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 services. The administrator terminal 30 may be, for example, a mobile terminal such as a smartphone or tablet, or may be a stationary personal computer (PC) or laptop PC. It may also be a wearable terminal such as a head mounted display (HMD) or a wristwatch 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 services according to the present disclosure. The user ID 3011 includes information such as a session ID temporarily assigned by the server 10 to identify the user using the administrator terminal 30.

[0043] The application program 3012 may be stored in advance 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 input device 306 of 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 explained 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 an example of a screen showing the operation of the advertisement distribution process. Note that 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 clustering process is a process of dividing (clustering) multiple users into groups according to the similarity of their behavioral information.

[0049] <Cluster processing overview> Cluster processing is a series of processes that store the behavioral information of multiple users, perform clustering based on the similarity of the stored user behavioral information, and assign and store a cluster ID to each user to group them. Alternatively, it may be 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 relating to the time-series behavior of a plurality of users. Specifically, the control unit 104 of the server 10 acquires information (behavior data) about the user's behavior based on the location information etc. periodically received from the user terminal 20. Details of the behavior data are the same as those of the behavior data items in the behavior table 1013. The behavioral data can be identified 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 is stored in association with information such as an address, facility information, information on the type (genre) of the facility, and information on events. The behavioral data may also be identified based on information from 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, or 5G) that is located at a predetermined location where the user terminal 20 is located and that can be wirelessly connected to the user terminal 20, 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 from step S102 onwards in 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 it. 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 the feature amount for each facility genre for all users using the entire behavior data acquired in the behavior acquisition step. Specifically, the control unit 104 of the server 10 calculates the facility genre importance and feature amounts based on the behavioral data of multiple users acquired in step S101. Specifically, the user behavioral data can be regarded as array data spanning a predetermined period, such as every hour, one day, one week, one month, or one year. For example, assuming that information about user behavior (location information, facility name, facility type, numerical values ​​such as event name, text information, and category information) can be acquired for each hour of a 24-hour day (daily behavioral data), the user behavioral data for a specific day can be regarded as an array element [P1, P2, . . . , P24] having 24 elements (24 hours). The control unit 104 of the server 10 may determine, as the daily activity data for all users, a representative value such as the mode, average, median, sum, difference, weighted sum, or weighted difference (the mode, average, median, sum, difference, weighted sum, or weighted difference of the array elements) of the daily activity data for all users over a predetermined period such as one week, one month, or one year, a value obtained by detecting and excluding outliers, or a value obtained by excluding facility genres with a high co-occurrence rate between facility genres. For example, a typical behavior pattern of all users (e.g., frequently moving to facility A, facility B, facility C, etc.) may be used as the daily activity data for all users. The control unit 104 of the server 10 may also vectorize the daily activity data for all users using any machine learning, deep learning, artificial intelligence model, or the like. In each process of this disclosure, we will mainly explain behavioral data related to daily behavior data for 24 hours a day as an example, but this 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 behavioral data of multiple users acquired in step S101. Specifically, the user behavioral data can be regarded as array data spanning a predetermined period, such as every hour, one day, one week, one month, or one year. For example, assuming that information about 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 of a 24-hour day (daily behavioral data), the user's behavioral data for a specific day can be regarded as array elements [P1, P2, . . . , P24] having 24 (24-hour) elements. Alternatively, the period of time spent traveling from home to home can be regarded as the current day. The control unit 104 of the server 10 may determine the user's daily activity data (referred to as "user's daily activity data") as a representative value such as the mode, average, median, sum, difference, weighted sum, or weighted difference (the mode, average, median, sum, difference, weighted sum, or weighted difference of array elements) of the user's daily activity data over a predetermined period such as one week, one month, or one year, a value obtained by detecting and excluding outliers, or a value obtained by excluding facility genres with a high co-occurrence rate between facility genres. For example, the user's typical behavior pattern (e.g., frequently moving to facility A, facility B, facility C, etc.) may be the user's daily activity data. The control unit 104 of the server 10 vectorizes the user's daily activity data using any machine learning, deep learning, artificial intelligence model, or the like. Specifically, based on the behavior data of multiple users acquired in step S101, appropriate processing such as vectorization is performed to enable calculation of the similarity between the behaviors of each user. Additionally, the control unit 104 of the server 10 can apply any normalization method to the obtained vectors to execute a process of extracting the similarity between the vectors of each of multiple users, a vector (representative vector) that characterizes each of the vectors of each user, etc. Also, the importance of facility genre for each user may be calculated and a feature value compared with the overall may be provided. In each process of the present disclosure, behavioral data relating to daily behavioral data for 24 hours per day will be mainly described as an example, but the present disclosure can also be applied to sequence data spanning any predetermined period such as one week, one month, one year, etc. Furthermore, it may be determined whether the user works, lives, or visits a location based on the length of stay, frequency of visits, or usual behavior, and the occupation type may be determined and stored by combining the determination of work status 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, or other similar methods 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 multiple users according to the similarity of their behavioral data based on a similarity matrix. For example, a clustering algorithm such as k-means can be used. By performing the clustering process, cluster labels such as cluster IDs can be obtained for each user and for each similar user. Users with the same cluster ID are considered to have similar behavioral data (high similarity in behavioral data, close distance). Values ​​for facility genres that indicate unreliable values ​​may be removed. For example, if the average number of spots that constitute the feature value of facility genre A is close to 1, this value may indicate that the user was at that location by chance, and is therefore less reliable than a feature value consisting of multiple spots. If the average number of spots that constitute the feature value of facility genre A for a certain user group is close to 1, while the average number of spots that constitute the feature value of facility genre A for another user group with high feature value of facility genre A is farther away from 1, this indicates that the user group is using only a small number of stores in a facility genre that normally involves the use of many stores, and is therefore less reliable. If the average number of spots that served as the source of the feature values ​​for facility genre A is close to 1, and facility genre B, which has a high feature value, is located in the same location and the average number of spots that served as the source of the feature values ​​for facility genre B is far from 1, then the feature values ​​for facility genre A are likely to be influenced by facility genre B, and their reliability is low. Comparing the co-occurrence rates of the locations of spots in facility genre A and facility genre B, if the rate at which facility genre B exists when facility genre A is present is higher than the rate at which facility genre A exists when facility genre B is present, then the feature values ​​for facility genre A are likely to be influenced by facility genre B, and their 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), and the rate at which facility genre B exists when facility genre A is present is 100%, and the rate at which facility genre A exists when facility genre B is present is 33%, then facility genre A is likely to be subordinate to facility genre B, and the feature values ​​for facility genre A have low reliability. If the co-occurrence rate of facility genre A and facility genre B is low when viewed from the perspective of overall usage or usage by other user groups, and the co-occurrence rate is high only for the relevant user group, there is a high possibility that they are being influenced by facility genre B, which is in a subordinate relationship, and their credibility is low.If 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 genres that show unreliable values ​​can be identified using the "average number of spots that served as the source of the features" and the "co-occurrence rate between facility genres," and facility genres 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 calculating the representative behavior data, outliers may be detected and excluded, or values ​​for facility genres with high co-occurrence rates may be excluded. Any other processing may be performed so that the representative behavior data represents typical behavior for a group of users. Furthermore, the representative behavior data may be calculated from a carefully selected cluster of users with many commonalities, prioritizing facility genres with high rarity. For example, the representative behavior data may be calculated based on the behavior data of another cluster with high similarity between the first behavior data and behavior data related to facility genre, or on the behavior data of another cluster determined to have a correlation of a predetermined value or greater with the results of prior clustering (such as anime otaku or train otaku). Alternatively, the representative behavior data can be calculated using a lower value of the facility genre statistics feature compared to the standard. In this case, the cluster will be one where people have not visited that facility genre, so there is a possibility that people who have visited the facility genre will be mistakenly included, but there is no mistakenly included if people have not visited, so the data is highly reliable. Also, the feature can be weighted. The representative behavior data may be calculated using machine learning, deep learning, or any artificial intelligence model. Furthermore, the representative behavior data may be the data with a high total value of each user's feature, or the data with a large number of users may be used as 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 representative behavior data calculated from the behavior data of the user having the cluster ID in the cluster ID, user group, and representative behavior data fields of a new record in the cluster table 1015. This allows multiple users to be divided into multiple groups (user groups) according to the similarity of their behavior data. <User extraction process> The user extraction process is a process of identifying a first user group related to input theme information and a second cluster having behavior information similar to that of the first user group.

[0056] <User extraction process overview> 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 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 open the theme reception processing page to the server 10. 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 that can receive 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, and 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 operates the input device 306 of the administrator terminal 30 to transmit the theme name and theme conditions entered into the input fields to the server 10. 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 have to be executed consecutively. For example, the administrator may execute the process of step S301 in advance and store multiple themes in the theme table 1014. Furthermore, 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 to extract 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 acquires the items of the theme conditions by referring to the theme table 1014. For example, the control unit 104 of the server 10 may be configured to acquire a record of theme information for which the processes from step S302 onward have not been executed and for which no information is stored in at least one of the items of the first user group, the first behavioral 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 during a predetermined period. For example, assume that an event is held at a predetermined facility (facility A) on 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 visited facility A on date and time A, and identifies them as the first user group.

[0063] The first extraction step involves extracting a first user group based on the behavior of multiple users before and after a predetermined period of time, excluding one or more users who are not related to an event being held during the predetermined period of time. Specifically, the theme conditions may include not only conditions that the behavioral data must satisfy, but also conditions that the behavioral data must not satisfy. For example, the theme conditions may include a condition that the user has not visited facility B, facility C, etc., in the time period before or after visiting (checking in or checking 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 from one or more users who visit facility A on date and time A, excludes users who visit facility B, facility C, etc. in the time periods before and after, and identifies them as the first user group.

[0064] For example, if an event is an artist event for minors, facility A, which is the event venue, may include multiple facilities such as a live music venue and a hospital. In this case, it is expected that among multiple users visiting facility A, the interests of users visiting the live music venue and the hospital are completely different. For example, let's say that a user visiting the live music venue is User A and a user visiting the hospital is User B, and there is behavioral data when they visit the hospital. In this case, it is assumed that user A and user B have in common the fact that they visited facility A on a certain date and time A, and that they cannot be distinguished from each other based on their user behavior data. Even in such a case, it is assumed that the behavior patterns of user A and user B visiting facilities, etc., will be significantly different in the time periods before and after their visit to facility A. For example, it is conceivable that user B visited nearby elderly care facilities as facility B and facility C in the time periods before and after their visit. 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, it is assumed that although user B visits facility A on date and time A, the user also visits the welfare facility for the elderly in the time periods before and after, and therefore 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 with a theme related to the artist event, user A is included in the first user group, and user B, who is assumed to have a 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 from abstract behavioral data. It is also possible to use clusters that visit hospitals or the behavior of the target cluster when visiting hospitals as targets for user groups to exclude. By performing the exclusion process, the demographic distribution and schedule of the user group changes, allowing advertisements to be delivered 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 representing the first user group extracted in the first extraction step. The representative acquisition step executes a step of acquiring representative behavior data representing 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 the array elements) as representative behavioral data of the first user group. In calculating the representative behavioral data of the first user group, outliers may be detected and excluded, or facility genres with a high co-occurrence rate among facility genres may be excluded. Any other processing may be performed so that the representative behavioral data represents typical behavior of the user group. Furthermore, the representative behavioral data of the first user group may be calculated from a carefully selected cluster of users with many commonalities, prioritizing facility genres with high rarity. For example, the representative behavioral data may be calculated based on the behavioral data of another cluster with high similarity between the first behavioral data and behavioral data related to facility genres, or on the behavioral data of another cluster determined to have a correlation of a predetermined value or greater with the results of previous clustering (such as anime otaku or train otaku). Specifically, the representative behavior data is a typical behavior pattern based on the behavior data of many users included in the first user group on that day. Alternatively, multiple representative behavior data may be calculated for the first user group. For example, the representative behavior data may be a typical behavior of otaku event participants (first user group) from among otaku event participants, or multiple representative behavior data may be generated from otaku event participants based on typical behavior of anime otaku, train otaku, etc., and the representative behavior data for anime otaku and train otaku may be combined to form the representative behavior data for otaku event participants.

[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 plurality of users based on the representative behavior data of the first user group. Specifically, the control unit 104 of the server 10 searches for 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 obtained from the cluster table 1015. Note that the control unit 104 of the server 10 identifies, as the second cluster ID, cluster IDs of the predetermined number of cluster information in the order of cluster information whose similarity of representative behavior is equal to or greater than a predetermined value, and similarity between representative behavior data. This makes it possible to identify a second user group (one or more users included in the cluster associated with 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 allows for the extraction of a second user group (potential customers) that differs from the first user group but is expected to potentially exhibit similar behavioral patterns to the first user group based on the typical behavioral patterns (representative behavioral data) of the first user group related to a specific theme or event. For example, if the first user group is made up of users who attended a specific artist event, a second user group with similar daily behavioral data outside of the event day is identified. In this case, the second user group is not necessarily users who attended the specific artist event, but because their behavior is similar, it is expected that their interests, concerns, hobbies, and preferences are similar to those of the first user group. For example, it is expected that the purchasing behavior (interests, concerns, hobbies, and preferences in goods and services purchased) of the second user group will be 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 multiple users who have 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 some 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 dealership and have similar tendencies regarding behavioral data as luxury car buyers (first user group), it is possible to identify a more suitable second user group by excluding from the second user group the first user group who have already purchased luxury cars. 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 the next six months. Also, when matching using external context, users who have visited a Lexus store are likely to be potential buyers, so it is possible to choose not to send ads to them.

[0070] 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. Alternatively, 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 (whose similarity is equal to or greater than a predetermined value) may be identified as the second user group. In this case, the second user group, which is part of the first user group and has a behavioral pattern similar to the representative behavioral data of the first user group, can be said to be a user group consisting of some users of the first user group who exhibit typical behavioral data, especially among the first user group (if the first user group is otaku, then it can be said to be genuine otaku who exhibit typical behavior, especially among the otaku). In this way, by performing the user extraction process, it is possible to identify a second user group consisting of a portion of users from the multiple user groups included in the first user group who exhibit typical behavioral data in the first user group that is 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 user groups can be determined by obtaining location information from the website that identifies the spot where the user checked in and adding the relationship value. For example, if a user checks in at spots B and C and has a high relationship value with otaku, the user is determined to belong to 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 whose behavioral data is similar to that of the first user group (the second cluster ID identifies the 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 distribution process is a process of distributing advertisements to the user terminals of users.

[0075] <Outline of ad delivery process> The advertisement distribution 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 distribute the advertisement. Note that, 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 multiple pieces of advertisement information. In this case, in step S505, one or more advertisements that match the behavior of the user are delivered to the user who accessed the predetermined website.

[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 distribution processing page includes input fields for accepting input of theme identification information for identifying a theme related to the user's interests, concerns, hobbies, and preferences, and character strings, images, etc. to be used in advertisements to be distributed to users who are interested in the theme. The theme identification information is the 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 Hakata Ramen Higashi Station Store," and "Customers Eating Ramen for Dinner" on the display 3081 of the administrator terminal 30 in a selectable manner 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 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 fields of a new record in the advertisement table 1021.

[0081] 13 shows an example of the advertisement delivery processing page D1. The advertisement delivery processing page D1 includes avatars D11 visually depicting the personalities of a user group who have interests, concerns, hobbies, and preferences in the selected theme, visited places D12 indicating a breakdown of facilities visited by the user group, facility genres, and the like, attributes D13 indicating a breakdown of the user attribute data of the user group, similarities D14 indicating the similarities between the user group and user groups related to other themes, 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 multiple users identified by referencing the theme table 1014 using one or more theme identification information items stored in the advertisement theme ID field of the advertisement table 1021 will be described as an example, but the present disclosure is not limited thereto. For example, the present disclosure may be applied to presenting information on a user group consisting of multiple 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, or a second user group identified by referencing the user group in the cluster table 1015 using a second cluster ID expected to exhibit a behavioral pattern similar to that of the first user group. Advertisements may also be delivered based on keywords associated with themes. For example, if keywords such as anime, trains, and karaoke are associated with the theme "otaku," an advertisement may be delivered that selects only karaoke, or conversely, an advertisement may be delivered that excludes karaoke. Furthermore, the determination of the theme does not need to be made in real time; the theme may be determined using location information of previously visited locations.

[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 predetermined user group, user attribute data, representative behavioral data of the predetermined user group, and representative values ​​(mode, average, median, maximum, minimum, etc.) of the user attribute data of multiple users. Alternatively, the search may be performed based on values ​​obtained by detecting outliers in the behavioral data and excluding them, values ​​obtained by excluding facility genres with high co-occurrence rates among facility genres, or behavioral data carefully selected to find users with many commonalities, with priority given to facility genres with high rarity.The search may also be performed based on other clusters with high similarity between the first behavioral data and the behavioral data related to the facility genre, behavioral data of clusters with high similarity when facility genres with high co-occurrence rates are excluded, or behavioral 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 human model), such as a human face, eyes, nose, hairstyle, body type, clothing, etc., in association with behavioral data, user attribute data, etc. The control unit 104 of the server 10 refers to the avatar database based on the behavioral data, user attribute data, etc. of a predetermined user group, and identifies the parts (features) 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 components that make up the identified avatar. A configuration in which the person image is created using a learning model such as any machine learning, deep learning, or artificial intelligence model, with behavioral data, user attribute data, etc. of the predetermined user group as input data, can be used. For example, this can be achieved by training a learning model in advance so that a predetermined person image is output as output data for input data such as behavioral data, user attribute data, etc. of the user group. Alternatively, a person image that characterizes a predetermined user group can 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, if the predetermined user group is a group of users with a theme related to railway enthusiasts, an image of a person wearing glasses, a polo shirt with cargo pants, and sneakers may be registered as a railway enthusiast, and the avatar D11 may be output when the similarity to a railway enthusiast is high.

[0083] A person image, i.e., an avatar, that defines the person's image is set for a portion of the second user group. In other words, the number of users to whom avatars are set will be equal to or less than the number of users in the second user group. It is natural that the same avatar will be displayed for a user group with similar representative behavior data, and by using a small number of avatars as person images for a large number of users with similarities, the creation cost can be reduced. The second user group for which the avatar is set is preferably one for which the general public has a common image where the person's interests and appearance correlate, such as participants in an otaku event. User groups that can be extracted using location information are groups of users who gather at specific locations in the real world, such as events or stores, and it is easy to form an image of 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 specific keywords. If we consider events as a facility genre that only appears a few days a year, the rarity of this facility genre is extremely high, and the frequency is concentrated among a select few users. In other words, a specific image that correlates interests and appearance is likely to be recognized by the general public by users of facility genres where the value of rarity x frequency is high and users are extremely concentrated rather than dispersed, and by users who have such facility genres as co-occurring genres. This allows the viewer to implicitly understand the interests and appearances of users other than the user group who set the avatar, 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 portrait photograph taken on the date and time of a specific event or an illustration drawn based on that photograph, and more preferably, if it is statistically more common. The base of the avatar may be changed depending on 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 up a general avatar with each part separated and post information linked to location information, event information names, etc., 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 area or event compared to the whole. Similar to the idea of ​​facility genre features, for example, it is desirable that an Akihabara-style appearance is not the appearance of the majority in Akihabara, but rather an appearance / clothing that is relatively common in Akihabara / only seen in Akihabara. When the avatar of a user group for which an avatar is set is displayed as the avatar of another user group according to the similarity, each part may be changed according to the feature amount of the facility genre of the other user group. For example, each feature of the avatar may be associated with certain facility genres related to appearance, such as tanning salons, nail salons, tattoo shops, contact lenses, etc. In other words, even when displaying an otaku avatar to a group of users who have a high similarity to otaku, changes may be made, such as darkening the skin color if the feature value for tanning salons is high, or removing glasses if the feature value for contact lenses is high. 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 feature may be changed according to the degree of similarity with the other user group for which an avatar is set. For example, if the user group for which avatars are set is set such that otaku tend to wear clothes in dull colors and dull designs, and delinquents tend to wear clothes in bright colors and dull designs, when displaying an otaku avatar to a user group with a high similarity to otaku, if the next most similar user group is delinquents, changes can be made to reflect the characteristics of each group, such as dull colors and bright designs, or bright colors and dull designs. This makes it possible to display images that better match the characteristics of each user group without preparing a large number of images. It is not necessary to have one avatar per user group; multiple avatars can be set. Also, each user group is distributed to various spots, and even if they are all otaku, they can be displayed in clothing and backgrounds that match the location, such as clothing for participating in an event or clothing for 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 predetermined one or more pieces of behavior data tallied in the behavior tallying 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 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 facility genres tallied in the behavior tallying 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 more 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 a breakdown of the behavioral data possessed by 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 from a specific time period, or a pattern of continuous behavioral data spanning 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). Alternatively, the data may be tabulated based on information about any behavior defined in the behavioral data item of the behavior table 1013 (such as location information, facility, facility genre, store, store name, event, etc.). For example, a counting result can be obtained such 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 also 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 associates the counting results with the respective behavioral data and outputs them to the visited place D12.

[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 and Clubs 12 points Family Restaurant 22 points Convenience store 43 points 82 amusement facilities

[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 7-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 relating to the 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 tallied in the attribute aggregating 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 the user attribute data (for example, gender, age, and place of residence) of multiple users included in the user group. For example, the tabulation result may show 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 also be configured to calculate a predetermined index value (score) based on the tabulation result. In this case, the index value indicates 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 breakdown of the collected user attributes to the attribute D13.

[0091] <Attribute D13 output example 1 (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 the similarity between a predetermined user group and multiple user groups. The similarity calculation step executes a step of calculating the similarity between behavioral data of multiple users included in the predetermined user group and behavioral data of multiple users included in each of the multiple user groups. The control unit 104 of the server 10 executes a similarity presentation step of presenting the similarities calculated for each of the plurality of user groups in the similarity calculation step in association with a theme related to the user group. Specifically, the control unit 104 of the server 10 calculates the 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 users related to the theme identified by referring to the theme table 1014 based on the advertising theme ID of another theme stored in the advertisement table 1021. Note that the method for calculating the similarity of the behavior data is similar to the method for calculating the behavior data described in the cluster processing and user extraction processing, and therefore description thereof will be omitted. Alternatively, the control unit 104 of the server 10 may be configured to calculate the similarity of an arbitrary user group consisting of multiple users to a predetermined user group. Furthermore, 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 allows 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 to be calculated, i.e., the similarity between the behavior patterns of multiple users included in each user group. The control unit 104 of the server 10 associates the calculated similarity with the theme name, theme ID, advertisement ID, etc., and outputs it to the similarity D14 of the advertisement delivery processing page D1.

[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 a breakdown of the behavioral data possessed by 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 from a specific time period, or a pattern of continuous behavioral data spanning 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). Alternatively, the data may be tabulated based on information about any behavior defined in the behavioral data item of the behavior table 1013 (such as 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 also 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 user's interests and preferences from the perspective of a predetermined genre type, etc. For example, in 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 interest D15 output> ·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 one or more selected theme IDs. By checking the display contents of the advertisement distribution processing page D1, the information publisher can create advertisement data suitable for a user group. In addition, in accordance with the request of the information publisher's customer (for example, a store such as Hakata Ramen Higashi Station store), the information publisher can check 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 identification 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 theme ID item of the theme table 1014 based on one or more theme identification information stored in the advertising theme ID of the advertising table 1021 in step S501, and obtains information on 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 by 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, in step S501, the control unit 104 of the server 10 creates advertising materials (information and data related to advertisements) using the advertising data stored in the advertisement table 1021. For example, the advertising materials include text advertisements (catchphrases), banner advertisements (images), and advertisements that combine images and text. The advertising materials 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. The data created by the server 10 is not limited to advertising data, but may be any other information such as recommendation information (text data, image data, audio data, video data explaining recommended places, products, services, etc.).

[0099] It is preferable to configure the system so that the processing of steps S501 to S504 is executed in advance to create a plurality of second user groups corresponding to each of 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 allows a suitable advertisement according to the advertisement theme ID set in step S501 to be delivered to the second user group.

[0101] In the present disclosure, an example is described in which, when a second user included in a second user group accesses a specified 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 the predetermined website has a script (JavaScript, etc.) such as a predetermined tag embedded in advance to link with the information processing service according to the present disclosure. When the user terminal 20 accesses a predetermined website, the control unit 204 of the user terminal 20 executes the embedded script and transmits the user ID 2011 of the user terminal 20, the URL of the predetermined 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 predetermined 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 advertisement delivery is not limited to the second user group. For example, advertisements may be delivered to users (target users) other than the second user group whose representative behavior data is similar to that of the second user group. Furthermore, the target users do not necessarily have to be users (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 from a wireless base station or the like that is located in a predetermined location where the user terminal 20 is located and that can be wirelessly connected 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 time-series changes in the user's location information can be acquired as behavioral data. Note that since the acquisition of behavioral data is similar to step S101 of the cluster processing, 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 that is similar to the target behavioral data. Note that the method for calculating the similarity of the behavioral data is the same as the method for 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 distribution of the advertisement data is similar to the advertisement distribution to the second users, and therefore a description thereof will be omitted.

[0104] The information providing step executes a step of providing information to a predetermined user in accordance with the information on interests received in the information input step. The information providing step executes a step of providing information according to a predetermined theme related to an input operation in response to the input operation received from the predetermined user. 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 the 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 attributes 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 identified 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 using the following method. 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 predetermined website, the location information of the target user, the predetermined website accessed by the user terminal 20 in response to the target user's input operation on the user terminal 20, other information on the interests of the target user (for example, identified from information such as the user's facility genre importance determined from the history of accessed websites and the location information of the user terminal), etc. Furthermore, 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 the user attribute data of the target user, the date and time of access to a predetermined website, the target user's location information, the predetermined website accessed by the target user, the 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 taking into account the score obtained 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 (similar interests) user attribute data, location information, date and time of access to a specified website, specified websites accessed, history of websites accessed by the target user, etc. 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. Furthermore, when a city, ward, town, village or company is determined from the IP address, advertisements according to clusters of the city, ward, town, village or company may be delivered. In addition, depending on the time of day, for example, advertisements for restaurants suitable for dinner may be delivered to the user terminal 20 during dinner time.

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

[0107] The specified user does not necessarily have to be one person, and the configuration may be such that the average appearance of multiple users photographed by camera 2061 is regarded as the appearance of the specified user and the processing described below is executed.

[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 similar records from representative appearance vectors of multiple 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, an example has been disclosed in which, 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 used 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 makes up the user's appearance. In this case, the control unit 104 of the server 10 may perform clustering processing in advance for a plurality of users (an unspecified number of users) according to the degree of similarity for each feature. For a user group consisting of a plurality of users, the control unit 104 associates a typical appearance vector (representative appearance vector) with typical user attribute data (representative user attribute data) of the user group for each feature, and stores the associated data in a database (not shown). In this case, typical user attribute data (representative user attribute data) of a similar user group is associated with each feature, such as gender, age, hairstyle, clothing, color scheme, and belongings, 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 of the features that make up 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, for example, a weighted sum of the plurality of pieces of user attribute data. The weight of each part may be set to an arbitrary value (a score or the like may be assigned to each part). Also, a score or the like may be assigned to each combination of parts that make up the appearance.

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

[0111] It is possible to vectorize each part of multiple people, such as their age, gender, possessions, and hairstyle, and calculate a score for that set in the same way as calculating a 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 - Items: sunglasses, bag, hat, etc. - Hairstyle: Short hair, Long hair, Short hair, ... For each part, the TF (frequency of occurrence) is calculated by counting the frequency of occurrence of features (words). - Number of times the 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 whose age is 25 - Number of people whose gender is "male" - Number of people with the item "Sunglasses" - Number of people with "short hair" The inverse document frequency (IDF) is calculated based on the calculated DF. The method for calculating IDF is the same as the procedure for calculating 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 the importance of a particular part (feature) 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 high. By calculating TF-IDF scores for a person's appearance features, similar to document search using TF-IDF scores, it is possible to more appropriately identify groups of users similar to a given person based on the features that are most important within the overall population, while taking into account the frequency of appearance of each feature.

[0112] For information publishers who own 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 degree of similarity to the store's customer persona. In addition, at that time, it is also possible to distribute information taking into account the trade area.

[0113] Generally, a trade area is a set radius that roughly corresponds to the facility type and is the range within which a store, etc. can attract customers. Furthermore, general advertising 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 tourist spots or stores. Therefore, when attracting customers to a tourist spot or store, information is distributed to users within a certain range of the tourist spot or store. In contrast, each user group can have population distribution data for the period before and after check-in (or check-out) at each spot, and this can be considered the actual trade area of ​​each spot. When users are extracted directly from a certain spot, their distribution can be considered the trade area of ​​that spot. These trade areas vary in range depending on the spot and user group, and are trade areas that reflect not only the influence of distance, transportation means, and population, but also the characteristics of the spot and user group. These trade areas do not have a simple radius shape, and can take into account factors such as a particularly large population or a particularly high population ratio compared to other user groups. For example, in the case of tourist destinations, areas close to tourist destinations are generally populated by local residents and tourists are not present in their daily lives, so they are not considered trade areas. Places where tourists are present in large numbers in their daily lives can be treated as actual trade areas. In addition, the period before and after check-in (or check-out) for each spot can be adjusted according to the purpose and server load, and multiple trade areas with different periods can be maintained. In this case, a six-hour period can be interpreted as a range that can attract customers within six hours, and a six-month period can be interpreted as a range that can attract customers within six months. In other words, by considering the trade area, if you want to attract customers within six hours, you can deliver ads to users who can attract customers within six hours. If you want to attract customers within six months, you can deliver ads to users who can attract customers within six months. For example, a store can deliver ads when it has leftover inventory and wants to attract customers who will visit that day. At a regional tourist destination, it can deliver ads when it wants to attract wealthy urban customers who will visit within six months. Furthermore, even if it is determined that a user group is the target based on location information and attribute information obtained from a website, if they are outside the trade area, they can be excluded from ad delivery. 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 checking in (or checking out) at the tourist destination.

[0114] When targeting by matching statistical big data with personal data obtainable within a first-party service, it is expected that only a small amount of information can be obtained from the target user, making it difficult to capture a user profile from such a small amount of information. However, by matching the information sent by the target user with a combination of multiple statistics in different formats (multimodal), a user profile can be captured. Specifically, in the disclosed invention, by combining multiple statistical information in different formats calculated based on behavioral data, a more appropriate user group (cluster) can be identified. This method allows ad distributors and advertising media to achieve optimal targeting with less data, without having to hold large amounts of data that can identify individuals or that could lead to the identification of individuals. For example, if 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 for the mesh or spot that corresponds to that location information, but if further location information cannot be obtained and it is not possible to distinguish whether the user is an otaku or a band girl based on the occupancy rate, by combining this with schedule statistics, it can be inferred that the user is a band girl because 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 to three keywords and one piece of location information can be obtained from website users, and it can be inferred that the user is an otaku or 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 between otaku and band girls based on the feature values, etc., by combining the statistical data of the mesh or spot user group, it can be inferred that the user is an otaku, as 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 (advertising delivery) according 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 time-series representative behavior data for a certain user group in the facility category, such as going to a coffee shop, then a tourist spot, and then returning to a hotel, and if current location information can be obtained that indicates that a user who is presumed to be part of that user group is in a coffee shop, it is possible to provide that user with information about tourist spots that is unrelated to the information that the user is viewing or has viewed on a website. Furthermore, if the facility genre "tourist destination" in the time-series representative behavior data is concentrated in a specific spot, it is presumed that the user group is traveling to a specific tourist destination. Therefore, it is preferable to present information related to the specific tourist destination to the users presumed to be part of the user group without recommending other candidate tourist destinations to the users presumed to be part of the user group. This also applies when the time-series representative behavior data of the user group's spots is concentrated in a specific tourist destination. On the other hand, if the facility genre "tourist destination" in the time-series representative behavior data is distributed across multiple spots, it is presumed that the user group is sightseeing but not planning to visit a specific tourist destination. Therefore, it is preferable to deliver information about candidate tourist destinations to the users presumed to be part of the user group. In this way, the information provided can also be changed depending on the degree of distribution of the spots that make up the time-series representative behavior data of the facility genre for each user group. Note that providing information according to the environment in which the user is placed in the real world (external context information and its changes) is not intended to be unrelated to the information the user is viewing or has viewed on a website. For example, if a user is viewing a website about learning and it is estimated that the user has just visited a sports gym or other facility in the real world based on 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 changes) may be provided. Information about real-world locations is not necessarily provided. For example, if the time-series representative behavior data of the user group's spots and facility genres is not characteristic or important data for the user group, information unrelated to real-world locations according to the user group's interests (features of the facility genre) may be provided.

[0116] For example, if the facility genres before checking in to a ramen restaurant include many izakayas in the time-series representative behavior data for all users, users who check in to izakayas can be grouped together to provide information that captures them as "a group of users taking actions before eating ramen" and "a group of users who will eat ramen as their next action." If the facility genres before checking in to a ramen restaurant differ for each user group, users who check in to the facility genres before checking in to the ramen restaurant for each user group can be grouped together to capture them as "a group of users taking actions before eating ramen" and "a group of users who will eat ramen as their next action." Furthermore, by combining actions before and after the ramen restaurant within six hours of the ramen restaurant, it is possible to create a "group of users who can visit the restaurant within six hours and will eat ramen as their next action." This allows advertisers who want to quickly attract customers to their ramen restaurant to select "a group of users who can visit the restaurant within six hours and will eat ramen as their next action" without having to make complex decisions. 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 suits the tourist destination or store, and by treating the user group as the first user group and identifying representative behavioral data and a second user group, even more appropriate information can be provided. Furthermore, when the advertiser is a tourist destination or store, for example, if a group of users extracted from the tourist destination or store have high characteristics related to prep schools and renovations in their daily behavior data, and high characteristics related to athletics and natural parks in their daily behavior data for the period before and after checking in to the tourist destination or store, then 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 situation more complex, 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 location leading to multiple spots of the same facility genre. 1003 in FIG. 15 indicates that spot X is a location where multiple locations of the same facility genre lead to a random location. 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. 1005 in FIG. 15 indicates that spot X is a location where multiple people from the same facility genre head to the same spot. 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 moving from the same spot to the same spot from forward and backward movements. Reference numeral 1008 in FIG. 15 indicates that spot X is a location from which to travel to a plurality of facilities of the same genre from the same spot. 1009 in FIG. 15 indicates that spot X is a location from the same spot to a location where there is no regularity in the subsequent behavior.

[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 a given 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 a given day starting from check-ins in a certain area. 1101 in Figure 16 indicates that spot X is one of the locations in the lawless world. 1102 in FIG. 16 indicates that spot X is the central location within the lawless area. 1103 in Figure 16 indicates that spot X is the one of them location that is the origin (destination) of the center of the lawless. 1104 in Figure 16 indicates that spot X is one of the locations in the larger flow. 1105 in FIG. 16 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 major flow.

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

[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, and it is not like "If you buy souvenirs, go to the shrine," but rather "If you go to the shrine, buy souvenirs," so it is appropriate to provide information that is in line with this relationship.

[0121] These facility genre features indicate the degree of connection between each user group and the facility genre, and commercial areas and distribution indicate the degree of connection between each user group and spots (areas). Therefore, facility genres / spots (areas) that are highly related to a certain user group can be visualized using a co-occurrence network representation method.

[0122] The information providing step may include providing information according to a predetermined theme related to the combination, depending on 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 input from a predetermined user; one or more pieces of location information indicating the location of the predetermined user; one or more pieces of attribute information related to the attributes of the predetermined 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 specified website, the specified website accessed in response to input operations on the target user's user terminal 20, other information related to the target user's interests, such as the history of websites accessed in response to input operations on the target user's user terminal 20, the IP address of the access source, the user-agent and browser information of the user terminal, etc. This allows 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 memory device 902, an auxiliary memory device 903, and a communication IF 991 (interface), which are electrically connected to one another by a communication bus 921.

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

[0125] The main storage 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 wired or wireless communication standards. 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, and 5G mobile communication systems, LTE (Long Term Evolution), and wireless networks (e.g., Wi-Fi (registered trademark)) that can connect to the Internet via a predetermined access point. In the case of a wireless connection, communication protocols include, for example, Z-Wave (registered trademark), ZigBee (registered trademark), and Bluetooth (registered trademark). In the case of a wired connection, the network also includes a direct connection using a USB (Universal Serial Bus) cable, etc.

[0128] It should be noted that the computer 90 can be virtually realized by distributing all or part of each hardware configuration across multiple computers 90 and interconnecting them 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> The following describes the functional configuration of a computer realized by the basic hardware configuration (FIG. 14) of the computer 90. The computer includes 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 interconnected 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 them in the main storage device 902, and executing processing in accordance with the programs. The control unit can realize functional units that perform various types of 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 described functions. A processor includes transistors and other circuits and is considered to be circuitry or processing circuitry. A processor may also 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 herein 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 allocate a storage area corresponding to the storage unit in the main storage device 902 or the auxiliary storage device 903 in accordance with the programs. Furthermore, the control unit can cause the processor 901 to execute processes for adding, updating, and deleting data stored in the storage unit in accordance with the various programs.

[0134] A database refers to a relational database, which manages data sets called masters and tables in a tabular format structurally defined by rows and columns, by relating them to each other. In a database, a table is called a table, a 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. Typically, each table and each master has a column set as a primary key for uniquely identifying a record, but setting a primary key to a column is not essential. The control unit can cause the processor 901 to add, delete, or update records in specific tables and masters 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 an information processing device and an information processing system according to the present disclosure have been manufactured.

[0135] Note that the databases and masters in this disclosure may include any data structure in which information is structurally defined (such as a list, dictionary, associative array, or object). The data structure also includes data that can be considered as a data structure by combining data with functions, classes, methods, etc. 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, wherein the processor executes the following steps: a behavior acquisition step (S101) of acquiring behavioral data regarding the behavior of multiple users; a first extraction step (S302) of extracting a first user group consisting of one or more users related to a predetermined theme from the multiple users based on the behavioral data acquired in the behavior acquisition step; a representative acquisition step (S303) of acquiring representative behavior data representative of the first user group extracted in the first extraction step; and a second extraction step (S304) 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. This makes it possible to extract a second group of users (potential customers) who are different from the first group of users but are expected to potentially exhibit behavioral patterns similar to those of the first users, based on the typical behavioral patterns (representative behavioral data) of the first group of users related to a specific theme or event.

[0139] (Appendix 2) A program described in Appendix 1, wherein the first extraction step (S302) is a step of extracting a first group of users 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 group of users during a period other than the specified period. This allows for the extraction of a first user group related to an event held at a specific date and time and location, thereby enabling the extraction of 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 group of users based on the behavior of multiple users before and after a specified period, excluding one or more users who are not related to an event being held during the specified period. This makes it possible to exclude from the first user group users who are dissimilar in terms of the similarity of their behavioral patterns from the first user group, thereby extracting 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 multiple users are classified into multiple 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 multiple groups. This allows a group (cluster) having a behavior pattern similar to the behavior pattern of the first user group to be extracted as the second user group from among groups (clusters) obtained by grouping (clustering) a plurality of users according to the similarity of their behavior patterns.

[0142] (Appendix 5) A program described in any 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 predetermined user identified based on a second user group extracted in the second extraction step. This makes it possible to provide predetermined users (potential customers) identified based on a second user group who are expected to potentially exhibit similar behavioral patterns to the first user with information related to a predetermined theme that is expected to interest the second user group, thereby improving the response to the provided information. The predetermined user may be a user included in a second user group.

[0143] (Appendix 6) A program as 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 provision step (S505) of providing information to a specified user in accordance with the information relating to the interests accepted in the information input step. This allows information to be provided to predetermined users who are expected to share the user's interests in response to input of information related to the user's interests received from information publishers, including information media, etc., thereby improving the effectiveness of providing information.

[0144] (Appendix 7) 7. The program according to claim 5, 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 allows a specific user to be provided with information that is more in line with the user's interests, based on the specific operations the user has performed on the Internet. This improves the effectiveness of providing information. For example, it is possible to provide more effective information to the user by 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 appendixes 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 predetermined user with information that is more in line with the interests of each user, according to 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 allows a predetermined user to be provided 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 appendix 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 allows a predetermined user to be provided with information that is more in line with the interests of each user, in accordance with the user's attribute information identified based on the user's appearance, thereby improving the effectiveness of providing information.

[0148] (Appendix 11) 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, depending on the time. This makes it possible to provide a predetermined user with information that is in line with the user's interests according to 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 input from a predetermined user; one or more pieces of location information indicating the location of the predetermined user; one or more pieces of attribute information related to the attributes of the predetermined user; and one or more times, in accordance with the program described in Appendix 5. 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, wherein the processor executes 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 the 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, wherein the similarity calculation step (S501) is a step of calculating the 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 group of users relating to a predetermined theme and a plurality of groups of users relating to other themes. For example, it is possible to present the degree of similarity between representative behavior data of a group of users relating to a certain theme and representative behavior data of a plurality of groups of users relating to other themes. This information can be used as reference when delivering advertisements to a group of users.

[0151] (Appendix 14) A program described in Appendix 13, in which a processor executes a persona creation step (S501) of creating a person image that characterizes a specified user group based on behavioral data of multiple users included in the specified 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 and styles of users included in the user group. This information can be used as reference 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 predetermined one or more behavioral data included in the behavioral data of multiple users included in a predetermined user group, and a behavior presentation step (S501) of presenting an index value based on the number of predetermined one or more behavioral data aggregated in the behavior aggregation step in association with the predetermined one or more 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 distributing 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 included in the behavior data of multiple 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 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 visit, it is possible to use this information as reference information when distributing 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 multiple 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 a user group are visiting. By checking what facilities a plurality of users included in a user group have visited, it is possible to use this information as reference information when distributing 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 predetermined one or more pieces of attribute information included in attribute information relating to the 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 predetermined one or more pieces of attribute information aggregated in the attribute aggregation step in association with the predetermined one or more pieces of attribute information. This makes it possible to present what attributes users included in a user group have. By checking the attributes of multiple 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 computer-implemented method comprising a processor and a memory, wherein the processor performs all of the steps performed in the invention according to any one of appendices 1 to 18. This makes it possible to extract a second group of users (potential customers) who are different from the first group of users but are expected to potentially exhibit behavioral patterns similar to those of the first users, based on the typical behavioral patterns (representative behavioral data) of the first group of users related to a specific 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 group of users (potential customers) who are different from the first group of users but are expected to potentially exhibit behavioral patterns similar to those of the first users, based on the typical behavioral patterns (representative behavioral data) of the first group of users related to a specific 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 steps performed in any of the inventions according to any one of appendixes 1 to 18. This makes it possible to extract a second group of users (potential customers) who are different from the first group of users but are expected to potentially exhibit behavioral patterns similar to those of the first users, based on the typical behavioral patterns (representative behavioral data) of the first group of users related to a specific 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 the behaviors of a plurality of users; a first extraction step of extracting a first user group consisting of 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 from the plurality of users based on representative behavior data of the first user group; A program that executes.

2. the first extraction step is a step of extracting the first user group related to an event held during a predetermined period; The representative acquisition step is a step of acquiring representative behavior data representing the first user group for a period other than the predetermined period. The program according to claim 1.

3. The first extraction step is a step of extracting the first user group based on behaviors of the plurality of users before and after the predetermined period, and excluding one or more users who are not related to an event to be held during the predetermined period. The program according to claim 2.

4. the processor: a grouping step of classifying the plurality of users into a plurality of groups based on the similarity of the behavior data acquired in the behavior acquisition step; Run The second extraction step is a step of extracting, from 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.

5. 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; To execute The program according to claim 1.

6. the processor: an information input step of accepting input of information related to the 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. The program according to claim 5.

7. the information providing step is a step of providing, in response to an input operation received from the predetermined user, information according to the predetermined theme related to the input operation. The program according to claim 5.

8. 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 5.

9. 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 5.

10. the information providing step is a step of providing information according to the predetermined theme related to the attribute information identified based on the appearance of the predetermined user, The program according to claim 9.

11. the information providing step is a step of providing information according to the predetermined theme related to the time in accordance with the time. The program according to claim 5.

12. The information providing step includes: one or more pieces of information about the user's interests; one or more operations received as input from the predetermined user; 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; and providing information according to the predetermined theme related to the combination in accordance with at least two or more of the above. The program according to claim 5.

13. 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 time-series behaviors of a plurality of users; a grouping step of classifying the plurality of users into a plurality of user groups relating to a plurality of different themes based on the similarity of the behavior data acquired in the behavior acquisition step; a similarity calculation step of calculating similarities between a predetermined user group and a plurality of user groups; a similarity presentation step of presenting the similarities for each of the plurality of user groups calculated in the similarity calculation step in association with a theme related to the user group; Run the similarity calculation step is a step of calculating similarities 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; program.

14. the processor: a persona creation step of creating a person image that characterizes the predetermined user group based on behavioral data of a plurality of users included in the predetermined user group; a persona presentation step of presenting the person image created in the persona creation step; To execute The program according to claim 13.

15. the processor: a behavior counting step of counting the number of predetermined one or more behavior data items included in the behavior data of a plurality of users included in the predetermined user group; a behavior presentation step of presenting an index value based on the number of the predetermined one or more pieces of behavior data compiled in the behavior compilation step in association with the predetermined one or more pieces of behavior data; To execute The program according to claim 13.

16. the behavior counting step is a step of counting the number of facility genres related to one or more predetermined types of facilities included in the behavior data of a plurality of users included in the predetermined user group, the behavior presentation step is a step of presenting an index value based on the number of the one or more predetermined facility genres tallied in the behavior aggregation step, in association with the one or more predetermined facility genres. The program according to claim 15.

17. the behavior counting step is a step of counting the number of predetermined facilities included in behavior data of a plurality of users included in the predetermined user group, the behavior presentation step is a step of presenting an index value based on the number of the one or more predetermined facilities tallied in the behavior aggregation step, in association with the one or more predetermined facilities. The program according to claim 15.

18. the processor: an attribute counting step of counting the number of predetermined one or more pieces of attribute information included in attribute information relating to attributes of a plurality of users included in the predetermined user group; an attribute presentation step of presenting an index value based on the number of pieces of the predetermined one or more pieces of attribute information compiled in the attribute compilation step in association with the predetermined one or more pieces of attribute information; To execute The program according to claim 13.

19. A method implemented on a computer having a processor and a memory, wherein the processor performs all of the steps performed in any of the inventions according to claims 1 to 18.

20. 19. An information processing device 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 18.

21. A system comprising means for executing all steps performed in any one of the inventions according to claims 1 to 18.

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