Information processing device, method, and program

Abstract

Provided are an information processing device, method, and program capable of facilitating sales of image data. The information processing apparatus includes a processor. The processor acquires first image data and first information including information on evaluation of an image represented by the first image data. The processor analyzes the first image data and classifies the first image data into a plurality of categories. The processor calculates an evaluation value for each category on the basis of the first information. The processor generates second information of the second image data on the basis of the evaluation value.

Description

Technical Field

[0001] This invention relates to an information processing apparatus, method, and program, and more particularly to an information processing apparatus, method, and program for processing information related to the sale of image data. Background Technology

[0002] In Patent Document 1, a camera system that provides information to aid in photography is described as having the following components: a setting unit that sets a request for a photograph desired by a user; a sending unit that sends the request set by the setting unit to a learning device; a learning device that extracts an image from an image database that matches the request and has been evaluated by a third party, and uses the extracted image to perform machine learning and output a reasoning model; a receiving unit that receives the reasoning model output from the learning device; and a display unit that displays the result obtained by reasoning using the reasoning model.

[0003] Patent Document 2 describes a system that provides suggestions for the composition of a photograph in relation to a community that evaluates the subject. The system includes: a learning unit that learns evaluations related to the composition of multiple images posted on a social networking service (SNS); an extraction unit that analyzes images taken on a user-operated terminal and extracts feature information; and a notification unit that, if a composition is temporarily specified by the user, notifies the user of information based on the extracted feature information that it is inferred to be an evaluation given to an image taken according to the composition on the SNS.

[0004] Previous technical documents

[0005] Patent documents

[0006] Patent Document 1: Japanese Patent Application Publication No. 2019-114222

[0007] Patent Document 2: Japanese Patent Application Publication No. 2021-77131 Summary of the Invention

[0008] One embodiment of the present invention provides an information processing apparatus, method, and program that can help promote the sale of image data.

[0009] means for solving technical problems

[0010] (1) An information processing apparatus comprising a processor, the processor performing the following processing: acquiring first image data and first information containing information on an evaluation of an image represented by the first image data; analyzing the first image data and classifying it into multiple categories; calculating an evaluation value for each category based on the first information; and generating second information of second image data based on the evaluation values.

[0011] (2) The information processing apparatus according to (1), wherein the second information is information that helps to promote sales.

[0012] (3) The information processing apparatus according to (1) or (2), wherein the first information includes the situation in which the image represented by the first image data is viewed, selected, or added to a shopping cart.

[0013] (4) The information processing apparatus according to any one of (1) to (3), wherein, in the evaluation, the shorter the period from the start of sale to purchase of the image represented by the first image data, the higher the evaluation is given.

[0014] (5) The information processing apparatus according to any one of (1) to (4), wherein the first information includes sales information as evaluation information.

[0015] (6) The information processing apparatus according to (5), wherein the evaluation value is the average value of sales.

[0016] (7) The information processing apparatus according to any one of (1) to (6), wherein the first information includes information about the subject and / or the photographer, and the processor calculates an evaluation value by weighting the evaluation information based on the information about the subject and / or the photographer.

[0017] (8) The information processing apparatus according to any one of (1) to (7), wherein the second information is information on the priority order disclosed when selling a plurality of second image data.

[0018] (9) According to the information processing apparatus of (8), wherein the first information includes information on the sales date and time, the processor performs the following processing: generating statistical information on the evaluation based on the sales date and time for each category; and generating the second information based on the statistical information on the evaluation.

[0019] (10) The information processing apparatus according to any one of (1) to (7), wherein the category is classified according to composition and / or pose, and the second information is information on recommended composition and / or pose.

[0020] (11) The information processing apparatus according to (10), wherein the processor performs the following processing: extracting compositions and / or poses with an evaluation value of above a threshold or an evaluation value ranking above a threshold; and generating information of the extracted compositions and / or poses as second information.

[0021] (12) The information processing apparatus according to (10) or (11), wherein the processor performs the following processing: acquiring information on sold compositions and / or poses; and generating information on unsold compositions and / or poses from the extracted compositions and / or poses as second information.

[0022] (13) The information processing apparatus according to any one of (10) to (12), wherein the processor performs the following processing: extracting compositions and poses with an evaluation value of or above a threshold or with an evaluation value ranking of or above a threshold; and generating information composed of the extracted compositions and poses as second information.

[0023] (14) The information processing apparatus according to any one of (10) to (13), wherein the processor performs the following processing: acquiring information on the number of people in a single shot; extracting compositions and / or poses with an evaluation value of or above a threshold or an evaluation value ranking of or above a threshold; and generating, based on the extracted composition and / or pose information, composition and / or pose information corresponding to the number of people as second information.

[0024] (15) The information processing apparatus according to any one of (1) to (14), wherein the category is classified according to composition and / or pose, and the second information is information on the combination of composition and / or pose when generating the collage image.

[0025] (16) An information processing method, wherein a first image data and a first information containing information on the evaluation of the image represented by the first image data are acquired, the first image data is analyzed and classified into multiple categories, an evaluation value for each category is calculated based on the first information, and a second information of the second image data is generated based on the evaluation value.

[0026] (17) An information processing program that enables a computer to perform the following functions: acquiring first image data and first information containing information on the evaluation of the image represented by the first image data; analyzing the first image data and classifying it into multiple categories; calculating the evaluation value of each category based on the first information; and generating second information of the second image data based on the evaluation value. Attached Figure Description

[0027] Figure 1 This is a conceptual diagram of an image data sales service.

[0028] Figure 2 This is a block diagram illustrating an example of the system architecture of an image data sales system.

[0029] Figure 3 This is a block diagram illustrating an example of the hardware structure of a sales server.

[0030] Figure 4 This is a block diagram illustrating an example of the hardware structure of a database server.

[0031] Figure 5 This is a block diagram illustrating an example of the hardware structure of an information processing server.

[0032] Figure 6 This is a block diagram illustrating an example of the hardware structure of the first user terminal.

[0033] Figure 7 This is a block diagram illustrating an example of the hardware structure of a second user terminal.

[0034] Figure 8 This is a block diagram illustrating an example of the hardware structure of a printer.

[0035] Figure 9 This is a block diagram of the main functions of the first user terminal.

[0036] Figure 10 This is an example of a screenshot showing the process of uploading image data for sale.

[0037] Figure 11 This is an example of a screenshot showing the process of uploading image data for sale.

[0038] Figure 12 This is an example of a screenshot showing the process of uploading image data for sale.

[0039] Figure 13 This is a block diagram of the main functions of the second user terminal.

[0040] Figure 14 This is an example of a screen display when browsing image data that is currently being sold.

[0041] Figure 15 This is an example of a screen display when browsing image data that is currently being sold.

[0042] Figure 16 This is an example of a screen display when browsing image data that is currently being sold.

[0043] Figure 17 This is an example of a display screen that is being printed.

[0044] Figure 18 This is an example of a display screen after printing is complete.

[0045] Figure 19 This is a block diagram of the main functions of the sales server.

[0046] Figure 20 This is a block diagram of the main functions of an information processing server.

[0047] Figure 21 It is a conceptual diagram for generating publicly available decision information.

[0048] Figure 22This is a block diagram of the main functions of the sales processing department regarding public decision processing.

[0049] Figure 23 This is a conceptual diagram of a method for determining the display order of publicly available information.

[0050] Figure 24 It is a flowchart of the publicly determined action.

[0051] Figure 25 This is a main block diagram of the image data sales system's functions related to the generation and provision of recommendation information.

[0052] Figure 26 This is a diagram illustrating an example of displaying recommendation information on the first user terminal.

[0053] Figure 27 This is an example of a collage image. Detailed Implementation

[0054] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0055] [First Implementation]

[0056] Here, we will take as an example the case where the present invention is applied to a system that provides the following services.

[0057] Figure 1 This is a concept diagram of the service.

[0058] This service is an online sales service for image data for printing. In this service, image data is received from users who wish to sell it (User 1), and the received image data is made publicly available online and sold online to users who wish to purchase it (User 2). Users who purchase the image data (User 2) download the image data and print it. Through printing, a photograph (a photograph obtained by recording the image represented by the image data on photographic paper, film, or other recording media) is ultimately obtained.

[0059] like Figure 1 As shown, the first user U1 who wants to sell image data uses a first user terminal 100 such as a smartphone to upload the image data to be sold to the sales server 10.

[0060] The sales server 10 records the image data uploaded by the first user U1 in the database server 20. Furthermore, the sales server 10 publicly displays the image data recorded in the database server 20 online and sells it online.

[0061] A second user U2, who wishes to purchase image data, browses publicly available image data using a second user terminal 200, such as a smartphone. Furthermore, the second user U2 purchases the publicly available image data using the second user terminal 200.

[0062] Image data can be downloaded by purchase. Second user U2 uses second user terminal 200 to download the image data from sales server 10 and prints it using printer 300. The printed image 400 is obtained as a photograph.

[0063] This service is primarily used by singers, actors, and entertainers (User 1 U1) in the entertainment and music fields when they sell printable image data to fans (User 2 U2). For convenience, singers, actors, and entertainers will be referred to as "entertainers" below. This includes so-called idols. Idols typically refer to young entertainers supported by teenagers.

[0064] In such services, even image data sold by the same person can sometimes have different sales volumes depending on its content, how it is published, and other factors.

[0065] However, it is difficult to determine or identify the factors that cause the sales discrepancies. This is especially true for users who are less familiar with sales.

[0066] The image data sales system of this embodiment is a system that receives and sells image data for printing from users, and is a system that can promote the sales of image data.

[0067] [System Structure of the Image Data Sales System]

[0068] Figure 2 This is a block diagram illustrating an example of the system architecture of an image data sales system.

[0069] The image data sales system 1 of this embodiment is configured as follows: it receives image data from a user (first user) who wishes to sell image data, discloses the received image data, and sells it to a user (second user) who wishes to purchase it. The user (second user) who purchases the image data prints the image data himself / herself.

[0070] For example, when an artist sells image data for printing, the artist becomes the first user, and the artist's fans become the second user.

[0071] like Figure 2 As shown, the image data sales system 1 mainly consists of a sales server 10, a database server 20, an information processing server 30, a first user terminal 100 used by the first user U1, a second user terminal 200 used by the second user U2, and a printer 300.

[0072] Sales server 10 and first user terminal 100 are communicatively connected via network 2. Similarly, sales server 10 and second user terminal 200 are communicatively connected via network 2. Network 2 is constructed, for example, by wireless communication networks such as 4G (4th Generation), 5G (5th Generation), WiMAX (Worldwide Interoperability for Microwave Access), LTE (Long Term Evolution), and various base stations, as well as the Internet.

[0073] Furthermore, the sales server 10, database server 20, and information processing server 30 are communicatively connected via wired or wireless means. As an example, in this embodiment, the sales server 10, database server 20, and information processing server 30 are connected via a LAN (Local Area Network).

[0074] Furthermore, the second user terminal 200 and the printer 300 can be communicatively connected via wired or wireless means. As an example, in this embodiment, the second user terminal 200 and the printer 300 are wirelessly connected via Bluetooth (registered trademark).

[0075] [Hardware structure of the sales server]

[0076] Figure 3 This is a block diagram illustrating an example of the hardware structure of a sales server.

[0077] The sales server 10 has the structure of a typical server computer. That is, it has a processor 11, a main storage unit 12, an auxiliary storage unit 13, an operation unit 14, a display unit 15, and an interface unit 16.

[0078] The processor 11 executes programs and functions as various processing units. As an example, in this embodiment, the processor 11 is composed of a CPU (Central Processing Unit). The various programs executed by the processor 11 and the data stored are stored in the main memory 12 and / or the auxiliary memory 13. The terms "program" and "software" have the same meaning.

[0079] The main storage unit 12 includes RAM (Random Access Memory) and ROM (Read Only Memory). The RAM is used as the working area of ​​the processor 11. The ROM stores basic input / output programs, etc.

[0080] The auxiliary storage unit 13 may be composed of, for example, an HDD (Hard Disk Drive) or an SSD (Solid State Drive).

[0081] The operation unit 14 may consist of, for example, a keyboard, a mouse, etc.

[0082] The display unit 15 may be composed of, for example, a liquid crystal display (LCD) or an organic EL display (OLED).

[0083] The interface unit 16 includes a communication interface for connecting the sales server 10 to the network 2, a communication interface for connecting the sales server 10 to the database server 20, and a communication interface for connecting the sales server 10 to the information processing server 30.

[0084] [Database server hardware architecture]

[0085] Figure 4 This is a block diagram illustrating an example of the hardware structure of a database server.

[0086] Similar to the sales server 10, the database server 20 has the structure of a typical server computer. That is, it has a processor 21, a main storage unit 22, a secondary storage unit 23, an operation unit 24, a display unit 25, and an interface unit 26. The structure of each unit is essentially the same as that of the sales server 10, so its description is omitted.

[0087] [Hardware structure of the information processing server]

[0088] Figure 5 This is a block diagram illustrating an example of the hardware structure of an information processing server.

[0089] Similar to the sales server 10, the information processing server 30 has the structure of a typical server computer. That is, it has a processor 31, a main storage unit 32, an auxiliary storage unit 33, an operation unit 34, a display unit 35, and an interface unit 36. The structure of each unit is essentially the same as that of the sales server 10, so its description is omitted.

[0090] [Hardware structure of the first user terminal]

[0091] The first user terminal 100 is composed of a computer with communication capabilities. Preferably, it is a mobile computer such as a smartphone or tablet. More preferably, it is a mobile computer with camera capabilities (photography capabilities). As an example, in this embodiment, the first user terminal 100 is composed of a smartphone with camera capabilities.

[0092] Figure 6 This is a block diagram illustrating an example of the hardware structure of the first user terminal. Figure 6 This illustrates an example where the first user terminal 100 is composed of a smartphone.

[0093] like Figure 6 As shown, the first user terminal 100 includes a processor 101, a main storage unit 102, an auxiliary storage unit 103, a display unit 104, an operation unit 105, a GPS (Global Positioning Systems) receiver 106, a camera unit 107, a voice input unit 108, a voice output unit 109, a communication unit 110, a short-range wireless communication unit 111, and a sensor unit 112.

[0094] The processor 101 executes programs and functions as various processing units. As an example, in this embodiment, the processor 101 is a CPU. The various programs executed by the processor 101 and the data stored are stored in the main memory 102 and / or the auxiliary memory 103.

[0095] The main storage unit 102 includes RAM and ROM. The RAM is used as the working area of ​​the processor 101. The ROM stores basic input / output programs, etc.

[0096] The auxiliary storage unit 103 is composed of flash memory such as EEPROM (Electrically Erasable and Programmable ROM).

[0097] The display unit 104 may be composed of, for example, a liquid crystal display or an organic EL display.

[0098] The operation unit 105 includes a touch panel and various operation buttons. The touch panel detects touch operations on the screen of the display unit 104.

[0099] GPS receiver 106 receives a GPS signal containing the location information of the first user terminal 100.

[0100] The camera unit 107 includes a photographic lens and an image sensor, which electronically captures images.

[0101] The sound input unit 108 includes a microphone for inputting sound.

[0102] The sound output unit 109 includes a speaker that outputs sound.

[0103] The Communications Department 110 uses an antenna to communicate wirelessly with the nearest base station, etc.

[0104] The short-range wireless communication unit 111 uses an antenna to communicate with external devices in a short-range wireless manner.

[0105] The sensor unit 112 includes various sensors such as geomagnetic sensors, gyroscopes, and accelerometers.

[0106] [Hardware structure of the second user terminal]

[0107] The second user terminal 200 is composed of a computer with communication capabilities. Preferably, it is a mobile computer such as a smartphone or tablet. As an example, in this embodiment, the second user terminal 200 is composed of a smartphone.

[0108] Figure 7 This is a block diagram illustrating an example of the hardware structure of a second user terminal. Figure 7 This illustrates an example where the second user terminal 200 is composed of a smartphone.

[0109] Similar to the first user terminal 100, the second user terminal 200 includes a processor 201, a main storage unit 202, an auxiliary storage unit 203, a display unit 204, an operation unit 205, a GPS receiver 206, a camera unit 207, a voice input unit 208, a voice output unit 209, a communication unit 210, a short-range wireless communication unit 211, and a sensor unit 212. The functions of each unit are essentially the same as those of the first user terminal 100, therefore detailed descriptions are omitted.

[0110] In this embodiment, the second user terminal 200 is connected to the printer 300 via a near-field communication unit 211. The near-field communication unit 211 is connected to the printer 300, for example, via Bluetooth (registered trademark).

[0111] [Printer hardware structure]

[0112] Printer 300 receives a print request from the connected destination, namely the second user terminal 200, and prints an image on a recording medium. There are no particular limitations regarding the printing method or the type of recording medium used. As an example, in this embodiment, printer 300 is described as a so-called instant film printer. An instant film printer is a printer that uses instant film as the recording medium. Furthermore, in this embodiment, printer 300 is described as a so-called portable printer. A portable printer generally refers to a small, portable printer that can be carried around.

[0113] Figure 8 This is a block diagram illustrating an example of the hardware structure of a printer.

[0114] like Figure 8As shown, the printer 300 includes a control unit 301, a printing unit 302, a short-range wireless communication unit 303, and an operation unit 304.

[0115] The control unit 301 centrally controls the operation of the printer 300. The control unit 301 may be, for example, a microcomputer equipped with a processor and memory. The processor may be, for example, a CPU. The memory includes RAM, ROM, and EEPROM. The ROM and / or EEPROM store the programs executed by the processor and various data required for control.

[0116] The printing unit 302 prints images on instant film under the control of the control unit 301. The printing unit 302 includes a film loading chamber 302a, a film feeding mechanism 302b, a film transport mechanism 302c, and a print head 302d.

[0117] The film loading chamber 302a is the loading section for instant film. Instant film is loaded into the film loading chamber 302a, for example, in the form of a film pouch 310. The film pouch 310 is formed by storing the instant film in a predetermined box. The instant film is stored in the box in multiple sheets (e.g., 10 sheets). The instant film is, for example, a single-sheet type (sheet film method). Furthermore, this structure of instant film and film pouch 310 is known, for example, as the instant film and film pouch 310 used in instant cameras (Instax (registered trademark), trade name "Cheki") manufactured by Fujifilm Corporation.

[0118] The film delivery mechanism 302b delivers film one sheet at a time from the film package 310 filled in the film loading chamber 302a.

[0119] The film transport mechanism 302c transports the display film that is delivered from the film package 310 by the film delivery mechanism 302b.

[0120] The printhead 302d is, for example, a linear exposure head. The printhead 302d records an image by irradiating the exposure surface of the developing film, which is transported by the film transport mechanism 302c, line by line with printing light.

[0121] The developing film that records the image is developed during its transport and discharged from a designated outlet.

[0122] The short-range wireless communication unit 303 communicates with an external device (in this embodiment, the second user terminal 200) via an antenna in a short-range wireless manner. In this embodiment, the printer 300 and the second user terminal 200 are communicatively connected using Bluetooth (registered trademark).

[0123] In addition to the power button, the operation unit 304 also includes various operation buttons required for operating the printer 300.

[0124] In addition, printer 300 can have functions other than printing. For example, it can have a photography function (camera function) (so-called a camera-equipped printer). In addition to functioning as a so-called external printer, the camera-equipped printer can also function as a digital camera, capable of printing images taken on-site.

[0125] [Functions of the First User Terminal]

[0126] As described above, the first user terminal 100 is the terminal used by the first user U1. The first user U1 is a person who sells image data using the image data sales system 1. The first user terminal 100 is mainly used to provide the image data being sold.

[0127] Figure 9 This is a block diagram of the main functions of the first user terminal.

[0128] like Figure 9 As shown, regarding the provision of sales image data, the first user terminal 100 has functions such as an image data acquisition unit 100a, a sales setting information receiving unit 100b, and an upload processing unit 100c.

[0129] The image data acquisition unit 100a processes the acquisition of image data to be sold. The image data for sale is acquired, for example, using the camera unit 107 included in the first user terminal 100. That is, the image data for sale is acquired by taking pictures using the camera unit 107. Alternatively, image data for sale can also be acquired in the form of data read from an external device. External devices include external storage devices (e.g., memory cards) and photographic devices (digital cameras, etc.). Therefore, for example, image data taken with an external digital camera can also be used as image data for sale. The method of reading image data from an external device is not particularly limited.

[0130] The sales setting information receiving unit 100b processes the input of various information required when receiving sales image data. This sales setting information includes, for example, information about the sales period set for the image data being sold, and information about the sales price. The sales setting information receiving unit 100b displays a pre-defined input screen on the display unit 104 and receives sales setting information input from the user.

[0131] The upload processing unit 100c processes the image data to be sold and its sales settings information to the sales server 10. The upload processing unit 100c associates the image data to be sold and its sales settings information with the user's identification information before uploading. Identification information may consist of, for example, a user account. The user account is set up, for example, when the user applies for service. When setting up a user account, the user is required to enter various information, which is then associated with the user account and recorded in the database server 20. For example, the user may be required to enter an ID (Identification Code), password, username (name, Romanized spelling, katakana), self-introduction, profile picture, birthday, place of birth, website URL (Uniform Resource Locator), SNS URL, etc. The profile picture refers to the image set in the user's profile. It typically consists of an image of the user (facial image, etc.).

[0132] The upload is performed according to the execution instruction from the first user U1. The upload processing unit 100c displays the prescribed operation screen on the display unit 104 and receives the upload execution instruction from the first user U1.

[0133] Figures 10 to 12 This is an example of a screenshot showing the process of uploading image data for sale.

[0134] Figure 10 This shows an example of a screen displaying a method for acquiring image data.

[0135] Figure 10 Examples of three methods for acquiring image data are shown. The first method is acquisition by taking a picture using the first user terminal 100. The second method is acquisition from an external camera. The third method is acquisition from previously captured image data (image data stored in the first user terminal 100). When acquiring using the first method, click button IB21. When acquiring using the second method, click button IB22. When acquiring using the third method, click button IB23.

[0136] When the first method is selected, the camera unit 107 of the first user terminal 100 is activated and can take pictures.

[0137] If method 2 is selected, the connection to an external camera is set up, and image data is read from the connected camera.

[0138] When the third method is selected, the captured image data (image data stored in the first user terminal 100) is displayed on the screen of the display unit 104, thereby enabling image selection.

[0139] If the image data of the products being sold is obtained, then the transition to... Figure 11 The scene.

[0140] Figure 11 This shows an example of a screen for inputting sales settings information.

[0141] Figure 11 This shows an example of a situation where information such as color, comments, sales period, and sales price are entered as sales setting information.

[0142] Here, "color information" refers to the information conveyed by color to express the user's mood when uploading (posting). Figure 11 In the example shown, the structure is set to be selected from six predefined colors (white, red, orange, yellow, turquoise, and purple). The default is white. Therefore, if no color is selected, the color information is automatically set to white.

[0143] "Reviews" are user comments on the images being sold.

[0144] "Sales Period" refers to the period during which image data is sold. Specify the start date and time, as well as the end date and time. The sales period can also be set to an automatically defined structure. For example, it can be set to automatically set within a certain period (e.g., 10 minutes) based on the upload time. Furthermore, it can be set to automatically set the sales period by specifying only the start date and time. For example, it can also be set to automatically set within 10 minutes of the specified start date and time.

[0145] "Selling price" refers to the price required to print image data.

[0146] like Figure 11 As shown, the screen displays an image representing the image data for sale (IC11), an input field for color information (IC12), an input field for comments (IC13), an input field for the sales period (IC14), and an input field for the sales price (IC15).

[0147] A defined frame IC11f is displayed in the image display bar IC11, within which the image IC11i representing the image data being sold is displayed. The frame IC11f is composed of an image simulating the frame of a film. Therefore, the image is displayed in the display bar IC11 in a manner that simulates the printing result.

[0148] In addition, image editing is also possible in this example. Clicking the "Edit Image" button IB31 displayed on the screen will transition to the image editing screen, where you can edit (process) the image. Although detailed explanations of image editing are omitted, in addition to the usual image retouching functions (adjusting brightness, saturation, sharpness, cropping, etc.), it also has drawing (also known as drawing or painting) functions. Therefore, by editing the image, you can add handwritten characters such as signatures and messages to the image.

[0149] The available colors are displayed in the color information input field IC12. Figure 11 This example shows a display where circles filled with selectable colors are arranged horizontally in a row. The user clicks on the circle with the desired color to select it.

[0150] The specified box is displayed in the comment input field IC13. The user enters a comment in the box using the character input function provided in the first user terminal 100.

[0151] The sales period input field IC14 includes input fields for the start date and time of the sales period, as well as the end date and time. The user enters the start and end dates and times of the sales period in these fields. For example, the user can use the character input function provided in the first user terminal 100 to input the date and time information. Furthermore, a calendar and clock can be displayed to allow selection of the date and time. A drop-down menu for selectable dates and times can also be displayed for further selection.

[0152] The price input field IC15 displays a box for entering the price. The user enters the selling price (the price required for printing) in the box. The user enters the value in the box using the character input function provided in the first user terminal 100. Alternatively, a drop-down menu can be displayed for selecting a value.

[0153] The input screen for sales settings also displays a confirmation button IB32. The confirmation button IB32 is displayed, for example, at the end of the screen (the final position when scrolling). The confirmation button IB32 is activated when all input fields are filled. Therefore, clicking it will have no effect if there are unfilled input fields. Furthermore, the color of the confirmation button IB32 changes when all input fields are filled, for example, from gray to black. This allows for visual confirmation that all input fields have been filled.

[0154] If you complete entering all the information in the input fields and click the confirmation button IB32, you will be redirected to the confirmation screen for the entered content.

[0155] Figure 12An example of a confirmation screen is shown.

[0156] like Figure 12 As shown, the confirmation screen for input content displays the image represented by the image data to be sold, as well as the information entered as sales settings (color information, reviews, sales period, and sales amount).

[0157] And, as Figure 12 As shown, the upload button IB41 is also displayed on the confirmation screen for input. The upload button IB41 is displayed, for example, on the terminal screen.

[0158] When the input is correct, the user clicks the upload button IB41. Clicking the upload button IB41 instructs the upload to be performed. As a result, the image data to be sold and its sales settings information are uploaded to the sales server 10.

[0159] If the entered sales settings information is incorrect, the user will be returned to the sales settings input screen to correct the input information in the corresponding section.

[0160] In this embodiment, the image data of the first user's sale or intended sale is an example of the second image data.

[0161] [Functions of the second user terminal]

[0162] As described above, the second user terminal 200 is the terminal used by the second user U2. The second user U2 is the person who purchases the image data. The second user terminal 200 is mainly used for browsing and purchasing image data. Furthermore, in the system of this embodiment, the purchaser prints the image data. Therefore, the second user terminal 200 is used to download and print the purchased image data.

[0163] Figure 13 This is a block diagram of the main functions of the second user terminal.

[0164] The second user terminal 200 performs processes such as browsing and purchasing image data that is currently on sale, downloading purchased image data, and printing the downloaded image data by the printer 300. The second user terminal 200 has functions such as a browsing processing unit 200a, a purchasing processing unit 200b, a downloading processing unit 200c, and a printing processing unit 200d for these processes.

[0165] The browsing processing unit 200a processes image data that is displayed on the sales server 10 as image data that is currently being sold. The image data is displayed in the same way as a so-called webpage. Therefore, the browsing processing unit 200a has the same functions as a web browser, and displays a page with images that are currently being sold in a prescribed format on the display unit 204.

[0166] Figures 14 to 16 This is an example of a screen display when browsing image data that is currently being sold.

[0167] For example, images can be browsed on a per-seller basis. That is, images are displayed per user who sells an image (user 1).

[0168] Figure 14 This shows an example of what is called the homepage (home page).

[0169] The homepage is the initial screen displayed after the required user authentication has been completed. User authentication refers to the system's act of identifying a pre-registered user. User authentication itself is a well-known technology, so a detailed explanation of its process is omitted. As an example, user authentication is performed by entering a pre-registered ID and password.

[0170] like Figure 14 As shown, the homepage displays the user's display bar FC11, the display bar of followed users FC12, the search button FB11, etc.

[0171] The user's information is displayed in the user's display bar FC11. In this example, the user's name is displayed. Here, "user" refers to the person who uses the second user terminal 200 to browse, purchase, etc., image data that is being sold. Therefore, it is the second user.

[0172] The user's followed users are displayed in the Followed Users display bar FC12. In this example, the user's followed users' profile pictures are displayed in the display bar.

[0173] Here, "following" means registering in order to see posts from users you like. Following and "liking" are essentially the same thing.

[0174] The search button FB11 is for users searching for sales image data. Clicking the search button FB11 leads to the search screen. Searching itself is a well-known technology, so a detailed explanation is omitted here. The search screen allows users to perform searches for specific customers (sellers).

[0175] exist Figure 14 On the homepage shown, if you click and select one of the user's profile images in the display bar FC12 of the users you are following, you will be redirected to a screen displaying the selected user's information (individual user information screen). The same screen (individual user information screen) will appear when a specific user is selected from search results, etc.

[0176] Figure 15 This shows an example of how individual user information is displayed.

[0177] like Figure 15 As shown, the individual user information screen displays the following information: a display bar FC21 for the seller's information, a display bar FC22 for the personal profile image, a display bar FC23 for the sales quantity and number of followers, a personal profile button FB21, a message display bar FC24, and a display bar FC25 for the image data of the product being sold.

[0178] The seller information display panel FC21 is used to display the seller's information. In this example, it displays the seller's name. The seller is the user who sold image data in this system and is user number 1.

[0179] The profile picture display bar FC22 is the bar for displaying the profile picture of the seller who has registered.

[0180] The FC23 display panel shows the total number of images the seller has sold (posted) to date (sales volume) and the total number of users who follow the seller (followers).

[0181] The Profile button FB21 is a button that indicates the display of the seller's profile. Clicking the Profile button FB21 will display the seller's profile. For example, the profile can be displayed via a dropdown or pop-up.

[0182] The message posted by the seller is displayed in the message display bar FC24.

[0183] The FC25i displays a list of information about image data currently on sale in the image data display bar.

[0184] The information FC25i of the image data currently for sale includes the image FC25i1 represented by the image data currently for sale, the sales period information FC25i2 of the image data currently for sale, the sales price information FC25i3 of the image data currently for sale, and the comment information FC25i4 appended to the image data currently for sale. The sales period information FC25i2, the sales price information FC25i3, and the comment information FC25i4 are displayed adjacent to the image FC25i1 represented by the image data currently for sale. Figure 15 In the example shown, information about the sales period (FC25i2), the sales price (FC25i3), and reviews (FC25i4) are displayed next to the image FC25i1, which represents the image data being sold. Furthermore, the image FC25i1, representing the image data being sold, is displayed within a defined frame (FC25iF). The frame (FC25iF) is composed of an image that simulates the frame of a film.

[0185] When multiple images are currently being sold, the information FC25i of the images currently being sold is displayed in a column along the scrolling direction of the screen. Therefore, in this embodiment, the information FC25i of the images currently being sold is displayed sequentially by scrolling the screen.

[0186] In addition, the number of FC25i information that can display image data that is being sold at one time is determined based on the screen size of the display unit 204, the display size of the image FC25i1, etc.

[0187] If you click on an image in the information display bar FC25i that shows the image data being sold, you will be transitioned to a screen that displays the details of the clicked image data (details display screen). The transition to the details display screen is an example of selecting image data.

[0188] Figure 16 This is an example of a detailed display screen.

[0189] A detailed display screen is a screen that shows detailed information about the image data selected on an individual user information screen. Displaying a detailed display screen is an example of viewing the displayed image data.

[0190] like Figure 16 As shown, the detailed display screen includes a display bar FC31 for information on the sales period, a display bar FC32 for images represented by image data, a display bar FC33 for seller information, a display bar FC34 for comments, a display bar FC35 for sales price information, and a print button FB31.

[0191] The sales period information is displayed in the display bar FC31. In this example, it displays the start date and time of the sales, as well as the end date and time. Alternatively, it can be configured to display the end date and time of the sales, along with the remaining time until the end of the sales period.

[0192] A specified frame FC32f is displayed in the image display bar FC32, and the image FC32i represented by the image data for printing is displayed within the frame FC32f. The frame FC32f is composed of an image that simulates the frame of the film.

[0193] The seller information display bar FC33 is used to display the seller's information. In this example, it displays the seller's name and a seller icon image. The icon image is, for example, an image obtained by shrinking down the seller's profile picture.

[0194] The comments attached by the seller when uploading image data that is being displayed on the detail screen are shown in the comment display bar FC34.

[0195] The price information is displayed in the FC35 display bar, showing the price of the image data being printed on the detail display screen.

[0196] The print button FB31 is for requesting a printout of the image data currently displayed on the detail screen. A print request is made by clicking the print button FB31. Requesting a printout has the same meaning as indicating a purchase intention. Furthermore, requesting a printout is essentially the same as adding something to the shopping cart. Requesting a printout of image data is another example of viewing and / or selecting that image data.

[0197] The purchase processing unit 200b performs settlement processing with the sales server 10 based on the user's indication of purchase intention (click of the print button FB31 on the detailed display screen). Online settlement processing is a well-known technology, so details about it are omitted here.

[0198] By completing the settlement process, the image data currently displayed on the detailed display screen can be downloaded and printed using printer 300.

[0199] In the detailed display screen, such as Figure 16 As shown, at least a portion of the background is displayed in a specified color. The displayed color is the color selected by the seller when uploading the image data, which is being displayed on the detail screen.

[0200] The download processing unit 200c works in conjunction with the sales server 10 to process the image data that has been downloaded from the sales server 10 and has completed the settlement process.

[0201] The print processing unit 200d works in conjunction with the printer 300 to process the downloaded image data for printing. Furthermore, as a prerequisite for print processing, a second user terminal 200 is required to be connected to the printer 300.

[0202] Figure 17 This is an example of a display screen that is being printed.

[0203] If printing starts via printer 300, then Figure 17 The screen shown (screen in print) is displayed on display unit 204.

[0204] The printing screen displays a message PM1 indicating that printing is in progress. It also displays an animation LA1 (the so-called loading animation) indicating that printing is in progress. Figure 17 The example shown depicts an animation of circles (dots) arranged at constant intervals on the same circumference rotating along the circumference.

[0205] Furthermore, in the image being printed, a portion of the background is also displayed in a specified color. The displayed color is the color selected by the seller of the image data when uploading the image data.

[0206] Figure 18 This is an example of a display screen after printing is complete.

[0207] If printing in printer 300 is completed, it will display: Figure 18 The screen shown (after printing).

[0208] If printing is completed, the printer 300 outputs this information (print completion notification) to the second user terminal 200. If the printer 300 receives the print completion notification, the second user terminal 200 displays the print completion screen on the display unit 204.

[0209] like Figure 18 As shown, the printing completion screen displays a message PM2 indicating printing completion, a sales period information display bar FC41, a printed image display bar FC42, a response button display bar FC43, a seller information display bar FC44, a comment display bar FC45, and an album button FB41, etc.

[0210] The sales period information for the printed image is displayed in the sales period information display bar FC41.

[0211] The specified frame FC42f is displayed in the display bar FC42 of the printed image, and the printed image FC42i is displayed within the frame FC42f. The frame FC42f is composed of an image that simulates the frame of the developing film.

[0212] Selectable reaction buttons are displayed in the reaction button display bar FC43. These reaction buttons allow the buyer (second user) to express their mood in response to the printed image. The buyer clicks the reaction button corresponding to the image they wish to express.

[0213] The seller information display bar FC33 is used to display the seller's information. In this example, it displays the seller's name and icon image.

[0214] The comments attached to the printed image (the comments entered by the buyer at the time of upload) are displayed in the comments display bar FC45.

[0215] The album button FB41 is the button that indicates the start of the album. Images previously printed by the second user are stored in the album. The second user can browse these previously printed images in the album.

[0216] On the printed screen, a portion of the background is also displayed in a specified color. This color is the one selected by the seller of the image data when uploading it.

[0217] [Functions of the Sales Server]

[0218] Figure 19 This is a block diagram of the main functions of the sales server.

[0219] Sales server 10 primarily handles the reception, publication, and sale of image data. For example... Figure 19 As shown, the sales server 10 has functions such as a data receiving unit 10a, a data recording and processing unit 10b, a sales processing unit 10c, a sales management unit 10d, and a recommendation information acquisition unit 10e. The functions of each unit are implemented by the processor 11 executing a prescribed program.

[0220] The data receiving unit 10a performs processing to receive image data from the first user terminal 100. Specifically, it performs processing to receive image data and its sales setting information uploaded from the first user terminal 100.

[0221] The data recording and processing unit 10b processes the image data and its sales setting information uploaded from the first user terminal 100 and records them in the database server 20. The uploaded image data and its sales setting information are associated with the uploaded user's identification information (user account) and then recorded in the database server 20.

[0222] The sales processing unit 10c processes image data publicly recorded in the database server 20 and sells it as image data for printing. As described above, the publicly recorded image data is viewed and purchased through the second user terminal 200.

[0223] Furthermore, the image data for printing is not necessarily the image data uploaded by the first user (seller). For example, it could be an image obtained by adjusting the size, etc., of the image data uploaded by the first user (an image with changed resolution and / or size, etc.). Also, it could be an image converted to an image format that can be printed by printer 300.

[0224] The Sales Management Department 10d collects information related to the image data being sold as sales information at specific points in time and records it in the database server 20. This is known as point-of-sale (POS) information management. The sales information includes information about the image data being sold, the sales price, the sales date and time, etc. Furthermore, the sales information may also include information about the buyer (second user). This buyer information may include, for example, the buyer's age, gender, address, and number of purchases. Buyer information may be derived from information entered by the buyer when applying to use the system (when an account is issued). This information is recorded in the database server 20 for each user (second user).

[0225] The recommendation information acquisition unit 10e processes the acquisition of recommendation information from the information processing server 30. Here, "recommendation information" refers to information such as recommended actions and settings to promote the sale of image data. Recommendation information is an example of second information and is an example of information that helps promote sales. In this embodiment, disclosure determination information is acquired as recommendation information. "Disclosure determination information" refers to the priority order information when displaying image data that is currently being sold. That is, the information on the recommended display order for promoting the sale of image data is used as disclosure determination information. The recommendation information acquisition unit 10e acquires the disclosure determination information as recommendation information. The sales processing unit 10c discloses the image data based on the disclosure determination information acquired as recommendation information when disclosing the image data. This will be described in detail later.

[0226] [Functions of an information processing server]

[0227] The information processing server 30 generates recommendation information based on various information recorded in the database server 20. In this embodiment, the information processing server is an example of an information processing device.

[0228] Figure 20 This is a block diagram of the main functions of an information processing server.

[0229] like Figure 20 As shown, the information processing server 30 has functions such as a data acquisition unit 30a, an image analysis unit 30b, an evaluation extraction unit 30c, an evaluation value calculation unit 30d, and a recommendation information generation unit 30e. The functions of each unit are implemented by the processor 31 executing a predetermined program. In this embodiment, the program executed by the processor 31 is an example of an information processing program.

[0230] The data acquisition unit 30a acquires image data and sales information related to that image data from the database server 20. In this embodiment, the data acquisition unit 30a performs data acquisition processing (image data and sales information related to that image data) on all image data recorded in the database server 20. In this embodiment, the image data recorded in the database server 20 is an example of the first image data.

[0231] The image analysis unit 30b analyzes the image data acquired by the data acquisition unit 30a and classifies it into multiple categories. As an example, in this embodiment, the image data is classified into multiple categories based on the subject's pose. That is, the pose of the subject reflected in the image data is identified, and the image data is classified according to its type. More specifically, it is classified into which category (type of pose). For example, poses such as hands on the face, hands on the head, hands on the waist, and back to the camera are identified and classified according to each type of pose.

[0232] The image analysis unit 30b can employ so-called artificial intelligence (AI) in its processing (image classification). Specifically, it can use a model (learned model) that has undergone machine learning (including deep learning) using prescribed learning data. In this embodiment, a learned model that has undergone machine learning to classify objects based on the type of pose of the subject appearing in the image data can be used. The image analysis unit 30b uses the learned model to identify the pose of the subject appearing in the image data and outputs its classification result (a determination of which category it belongs to). Furthermore, AI-based image recognition or image classification is a known technology. Therefore, a detailed description of it is omitted.

[0233] The evaluation extraction unit 30c extracts information (evaluation information) evaluating the image represented by the image data from the sales information of the image data. In this embodiment, the sales revenue of the image data is extracted as the evaluation information. The sales revenue is calculated based on the sales quantity and sales price of the image data. That is, it is calculated by "sales revenue = sales quantity × sales price". In this embodiment, the evaluation information is an example of the evaluation information for the image represented by the first image data. Furthermore, the sales information of the image data is an example of the first information.

[0234] The evaluation value calculation unit 30d calculates the evaluation value according to each category classified by the image analysis unit 30b based on the evaluation information extracted by the evaluation extraction unit 30c. As described above, in this embodiment, the image data is classified according to the type of pose of the subject. Therefore, an evaluation value is calculated for each type of pose. Furthermore, as described above, in this embodiment, sales revenue is used as the evaluation information for the image data. Therefore, the evaluation value is calculated based on the sales revenue of the image data. In this embodiment, the evaluation value for each pose is calculated by averaging the sales revenue for each type of pose. That is, the average sales revenue of the image data belonging to each category (type of pose) is used as the evaluation value for each category. For example, if there are three image data belonging to a certain category (type of pose), and the sales revenue of each image data is 10,000 yen, 20,000 yen, and 30,000 yen respectively, the evaluation value becomes 20,000.

[0235] The recommendation information generation unit 30e generates recommendation information based on the evaluation value of each category calculated by the evaluation value calculation unit 30d. As described above, in this embodiment, public determination information is generated as recommendation information. Furthermore, in this embodiment, the evaluation value of each category is calculated based on sales revenue, and the average sales revenue of image data belonging to each category is used as the evaluation value of each category. The public determination information is the priority order information when displaying image data that is currently being sold. This information is the recommended display order information in the sales promotion of image data. The recommendation information generation unit 30e generates the recommended display priority order information for the sales promotion of image data based on the information of the average sales revenue of image data belonging to each category. As an example, in this embodiment, the category display priority order information is generated by sorting the categories in descending order of evaluation value.

[0236] Figure 21 It is a conceptual diagram for generating publicly available decision information.

[0237] Figure 21 This example illustrates how image data can be categorized into 10 classes (Class A to Class J) based on the pose of the subject being photographed.

[0238] Assume that category A has a rating of 10,000, category B has a rating of 15,000, category C has a rating of 1,000, category D has a rating of 0, category E has a rating of 20,000, category F has a rating of 16,000, category G has a rating of 8,000, category H has a rating of 13,000, category I has a rating of 18,000, and category J has a rating of 3,000.

[0239] As described above, in this embodiment, the categories are sorted in descending order of evaluation value to generate public disclosure information. Therefore, in this example, the categories are set as follows: Category E is the first, Category I is the second, Category F is the third, Category B is the fourth, Category H is the fifth, Category A is the sixth, Category G is the seventh, Category J is the eighth, Category C is the ninth, and Category D is the tenth to generate public disclosure information.

[0240] In this way, multiple categories are sorted in descending order of evaluation value to generate public decision information indicating the priority ranking to be displayed.

[0241] [Public Judgment and Handling]

[0242] The publicly available decision information generated as described above is used when selling image data for each seller (user 1) on the sales server 10. That is, it is used when displaying the image data sold by each seller on the display bar FC25 on each seller's page (individual user information screen).

[0243] like Figure 15 As shown, the image data sold by each seller is displayed in a column along the scrolling direction of the screen on the individual user information screen.

[0244] When the sales processing unit 10c displays image data for each seller on an individual user information screen, it determines the display order of the image data based on public determination information and displays it in the display bar FC25 for the image data currently being sold. The process of determining the display order of the image data is called public determination processing.

[0245] Figure 22 This is a block diagram of the main functions of the sales processing department regarding public decision processing.

[0246] like Figure 22 As shown, regarding the disclosure determination process, the sales processing unit 10c mainly has the functions of the image analysis unit 10c1 and the disclosure determination processing unit 10c2, etc. The functions of each unit are implemented by the processor 11 executing a prescribed program.

[0247] Similar to the image analysis unit 30b of the information processing server 30, the image analysis unit 10c1 analyzes image data and classifies it into multiple categories. The categories are the same as those of the image analysis unit 30b of the information processing server 30. Therefore, in this embodiment, the image data is classified into multiple categories according to the pose of the subject.

[0248] The public determination processing unit 10c2 performs processing to determine the display order of the image data to be sold based on the public determination information and the classification results of the categories of each image data.

[0249] Figure 23 This is a conceptual diagram of a method for determining the display order of publicly available information.

[0250] Figure 23 This example illustrates a seller's sale of five image data sets, ID1 through ID5. Assume that image data ID1 is classified as category E, image data ID2 as category A, image data ID3 as category C, image data ID4 as category J, and image data ID5 as category B.

[0251] The public determination processing unit 10c2 determines the display order of image data ID1 to ID5 according to the display priority information of each category represented by the public determination information. Figure 23 In the example shown, the display order of the five image data IDs ID1 to ID5 is set in descending order of priority as: Image Data ID1 (1st), Image Data ID5 (5th), Image Data ID2 (2nd), Image Data ID4 (4th), and Image Data ID3 (3rd). Therefore, when these image data IDs ID1 to ID5 are displayed on individual user information screens, they are arranged in the order of Image Data ID1 (1st), Image Data ID5 (5th), Image Data ID2 (2nd), Image Data ID4 (4th), and Image Data ID3 (3rd).

[0252] As described above, in this embodiment, the disclosed determination information is generated based on the sales revenue of image data belonging to each category, and is set to be displayed higher if the sales revenue is higher. Therefore, by displaying image data based on the disclosed determination information, it is expected to promote sales.

[0253] The role of an image data sales system

[0254] Here, the operation of the public determination of the image data sales system 1 of this embodiment will be explained.

[0255] Figure 24 It is a flowchart of the publicly determined action.

[0256] The public determination process is performed in the sales server 10, and as a preprocessing step, recommendation information is generated in the information processing server 30. In this embodiment, the recommendation information is the public determination information.

[0257] The information processing server 30 obtains image data and sales information of the image data from the database server 20 (step S11), and generates recommendation information based on the obtained image data and sales information (step S12).

[0258] In this embodiment, the information processing server 30 analyzes the acquired image data and classifies it into multiple categories based on the subject's pose. Then, it calculates an evaluation value for each category. The evaluation value is calculated based on the average sales revenue of the image data belonging to each category. The information processing server 30 sorts the multiple categories in descending order of evaluation value to determine the display priority and generates public judgment information (recommendation information). The priority of the generated public judgment information is set so that categories with higher sales revenue are displayed first.

[0259] Sales server 10 obtains recommendation information (public disclosure determination information) from information processing server 30 (step S21). By obtaining the recommendation information, public disclosure determination processing can be performed in information processing server 30. Furthermore, Figure 24 The public decision-making process shown is a process for a specific first-user (seller). That is, public decision-making is performed for each first-user.

[0260] First, determine whether image data has been acquired (step S22). That is, determine whether the first user, as the seller, has uploaded image data.

[0261] If image data is obtained, determine whether the first user who uploaded the image data has sold multiple image data, including the uploaded image data (step S23).

[0262] When selling multiple image data, including uploaded image data, a public determination process is performed (step S24). That is, the display order of the image data is determined based on the public determination information, which serves as recommendation information.

[0263] When the sales server 10 displays information about image data currently being sold on the second user terminal 200, it displays the data according to a predetermined display order. Specifically, when displaying information about image data currently being sold in the display bar FC25 of the individual user information screen, the data is displayed according to a predetermined display order (see reference). Figure 15 ).

[0264] In addition, when the image data sold by the first user is only one (when the determination in step S23 is "no"), there is no need to prioritize the display settings, so no public determination is performed.

[0265] The sales server 10 determines whether there is image data that has ended its sale (step S25). If there is image data that has ended its sale, the sales server 10 determines whether there is image data that is currently being sold (step S26). That is, it determines whether there is any remaining image data that is currently being sold. If there is image data that is currently being sold, it returns to step S23 to determine whether multiple image data are being sold. On the other hand, if there is no image data that is currently being sold, the process ends.

[0266] Thus, according to the image data sales system 1 of this embodiment, public decision information (recommendation information) is generated based on information recorded in the database server 20, and the display order of image data sold by each first user is determined based on the generated public decision information. The public decision information is set such that categories with higher sales are displayed more prominently. Therefore, by displaying image data based on the public decision information, it is expected to promote sales.

[0267] [Variation Example]

[0268] [Category Setting]

[0269] In the above embodiments, the structure is set to classify image data into multiple categories based on the subject's pose, but the classification categories are not limited to this. Furthermore, for example, the structure can also classify image data into multiple categories based on composition, background, scene, etc. That is, the composition, background, scene, etc., of the image represented by the image data are identified, and the image data is classified into multiple categories (types of composition, types of background, types of scene, etc.) based on the identified composition, background, scene, etc. Furthermore, the image data can also be classified into multiple categories based on the subject's expression, clothing, etc. That is, the expression, clothing, etc., of the subject are identified, and the image data is classified into multiple categories (types of expression, types of clothing, etc.) based on the identified expression, clothing, etc. Additionally, multiple combinations of these can be used to classify the image data into multiple categories.

[0270] Furthermore, an image dataset can be classified into multiple categories. For example, an image dataset can be categorized into pose, clothing, composition, background, etc., for identification, and then classified into categories based on the identification results. In this case, from the perspectives of pose, clothing, composition, background, etc., an image dataset is classified into multiple categories.

[0271] [Evaluation Value]

[0272] In the above implementation, the average sales revenue of image data belonging to each category is used as the evaluation value for each category, but the value used as the evaluation value is not limited to this. Any value that can evaluate each category from a sales perspective is acceptable. For example, the total sales revenue of image data belonging to each category, the average sales quantity or total sales volume of image data belonging to each category, or the average purchase amount per person for image data belonging to each category (the amount purchased by a second user) can also be used as the evaluation value.

[0273] Furthermore, the evaluation values ​​can be calculated using weighted averages. For example, the evaluation information can be weighted using information about the subject and / or photographer. In this case, for example, the sales figures (evaluation information) for each image data point are weighted by the subject and / or photographer, and evaluation values ​​for each category are calculated. The information about the subject and / or photographer (e.g., including sales setting information for the image data) is obtained by the first user upon upload.

[0274] Furthermore, the rating can be calculated from the perspective of the period from the start of the sale of image data to the purchase. That is, the time required from the start of the sale to the purchase is calculated as the rating of the image, and its average is calculated as the rating value for each category. In this case, the shorter the time, the higher the rating. That is, it is assumed that the shorter the time from the start of the sale to the purchase, the higher the demand, and therefore a higher rating can be obtained.

[0275] In addition, the time required to sell a pre-determined quantity of image data can be used as an evaluation of the images.

[0276] [Public Judgment Information]

[0277] In the above embodiment, the structure for generating public decision information is set to all image data recorded in the database server 20, but it can also be set to generate public decision information on a user-by-user basis. That is, it can be set to generate public decision information for each first user. In this case, the first user generates public decision information based on the image data sold and the sales information of that image data. The image data and its sales data include both image data previously sold by the first user and image data currently being sold. By generating public decision information for each first user, it is possible to generate public decision information suitable for each first user. That is, it is possible to generate public decision information based on the sales trends of each first user.

[0278] Furthermore, image data can be grouped from multiple perspectives, and public decision information can be generated for each group. For example, image data can be divided into multiple groups based on the attributes (age, gender, occupation, etc.) of the first user who sold the image data, and public decision information can be generated for each group. Similarly, image data can be divided into multiple groups based on the attributes of the second user who purchased the image data, and public decision information can be generated for each group. When multiple public decision information is generated based on the attributes of the second user who purchased the image data, it is preferable that the first user can select the public decision information to use. This allows for a display order more suitable for the primary purchase layer. Moreover, image data can also be divided into multiple groups based on the date and time (month, date, time) of the purchase of the image data, and public decision information can be generated for each group.

[0279] Furthermore, artificial intelligence can be used to generate publicly available judgment information. For example, when classifying image data across multiple perspectives (pose, clothing, composition, background, etc.), artificial intelligence can be used to generate publicly available judgment information that combines these perspectives. Specifically, image data is classified based on perspectives such as pose, clothing, composition, and background, and recommended combinations (combinations of pose, clothing, composition, background, etc.) are generated using artificial intelligence based on the classification results and evaluation values. Multiple combinations are generated, ranked, and used as publicly available judgment information.

[0280] [How to utilize publicly available judgment information]

[0281] In the above embodiments, as a method for utilizing the disclosed determination information, the display order of image data was determined by displaying image data in order of priority from high to low according to the priority order shown in the disclosed determination information. However, the method for utilizing the disclosed determination information is not limited to this.

[0282] Alternatively, the display order of image data can be determined by prioritizing the categories from low to high. In this way, by deliberately displaying image data in a priority order from low to high (the order of sales from low to high), sales can be balanced.

[0283] [Methods for selling image data]

[0284] In the above embodiment, the example described is the sale of image data within a limited period, but the structure can also be configured to sell image data indefinitely. Furthermore, when the sales period is limited, the structure can also be configured to limit sales to a very short period of time. For example, it can be configured to limit sales to 10 minutes.

[0285] When selling image data within a limited period, public decision information can be generated as recommendation information from the perspective of date and time. For example, public decision information can be generated from the perspectives of the sales period, sales month, sales date, and sales weekday (weekday or weekend).

[0286] When generating public decision information for different time periods, the average sales revenue of image data belonging to each category is calculated for each time period. Then, the display priority of each category is determined based on the obtained average sales revenue. Thus, public decision information can be generated for different time periods. The method of dividing the time periods is not particularly limited. Furthermore, in this example, calculating the average sales revenue of image data belonging to each category for different time periods is an example of generating statistical information based on sales date and time for each category. Public decision information for different sales months, different sales dates, and different sales weeks can also be generated in the same way.

[0287] In this way, by generating publicly available decision information from the perspective of date and time, the optimal display can be set based on the date and time of the sales image data. For example, the optimal display can be set based on the time of sale (morning, afternoon, evening, etc.), the month (season) of sale, the date of sale (Christmas, Valentine's Day, etc.).

[0288] Furthermore, in generating publicly available judgment information from the perspective of date and time, the displayed order can be dynamically changed based on the date and time of the sale.

[0289] Furthermore, when image data is sold for a limited period, publicly available judgment information can be generated from the perspective of the period from the start of sales to the purchase of the image data. That is, the time required from the start of sales to the purchase is used as an evaluation of the image data, and the average of that time is used as the evaluation value for each category to generate publicly available judgment information. As a result, it is possible to display information corresponding to the characteristics of image data sales (such as selling well immediately after sales begin, selling well as sales near the end of the period, gradually selling well, or selling well instantly).

[0290] [Display of image data currently for sale]

[0291] In the above embodiment, an example was described where, in the case that the second user terminal 200 is a smartphone, information about image data currently on sale is displayed in a single column along the scrolling direction of the screen. The method for displaying this information is not limited to this. It is preferable to set it appropriately according to the screen size, etc. For example, it can be configured to display image data information in multiple columns along the scrolling direction or vertically. In this case, for example, it can be displayed sequentially from the upper left to the right of the screen. If the end of the screen is reached, it moves to the next line, and similarly, it is displayed sequentially from the left to the right of the screen.

[0292] [Second Implementation]

[0293] In the above embodiment, the example described is the case where the priority ranking information (public determination information) when generating and displaying image data that is being sold is used as the recommendation information.

[0294] In this embodiment, the information of generating recommended sales images is used as recommendation information and provided to the first user.

[0295] The following example illustrates how information about the image used for recommended sales can be used to generate information about recommended poses (recommended pose information).

[0296] Figure 25 This is a main block diagram of the image data sales system's functions related to the generation and provision of recommendation information.

[0297] like Figure 25 As shown, in the image data sales system 1 of this embodiment, the information processing server 30 generates recommendation information (recommended posing information). The generated recommendation information is sent to the first user terminal 100 via the sales server 10 and displayed to the first user. That is, it is displayed on the display unit 104 of the first user terminal 100.

[0298] [Generation of Recommendation Information]

[0299] Similar to the first embodiment described above, the information processing server 30 has functions such as a data acquisition unit 30a, an image analysis unit 30b, an evaluation extraction unit 30c, an evaluation value calculation unit 30d, and a recommendation information generation unit 30e.

[0300] The data acquisition unit 30a acquires image data and sales information of the image data from the database server 20.

[0301] The image analysis unit 30b analyzes the image data acquired by the data acquisition unit 30a and classifies it into multiple categories. In this embodiment, the image data is classified into multiple categories based on the pose of the subject.

[0302] The evaluation extraction unit 30c extracts information (evaluation information) from the sales information of the image data to evaluate the image represented by the image data. In this embodiment, the sales amount of the image data is extracted as the evaluation information.

[0303] The evaluation value calculation unit 30d calculates the evaluation value according to each category classified by the image analysis unit 30b based on the evaluation information extracted by the evaluation extraction unit 30c. In this embodiment, the evaluation value is calculated for each pose.

[0304] The recommendation information generation unit 30e generates recommendation information based on the evaluation value of each category calculated by the evaluation value calculation unit 30d. In this embodiment, recommended posture information is generated as recommendation information based on the evaluation value of each posture.

[0305] The recommended pose information consists of poses categorized into multiple types, sorted in descending order of evaluation scores. In this embodiment, the poses are sorted in descending order of average sales revenue.

[0306] [Providing Recommendations]

[0307] like Figure 25 As shown, regarding the provision of recommendation information, the sales server 10 has functions such as a recommendation information acquisition unit 10f and a recommendation information provision unit 10g.

[0308] The recommendation information acquisition unit 10f processes the recommendation information (recommended posture information in this embodiment) generated in the information processing server 30 by acquiring the recommendation information (recommended posture information) from the information processing server 30.

[0309] The recommendation information providing unit 10g processes the recommendation information obtained from the information processing server 30 and provides it to the first user terminal 100. In this embodiment, the recommendation information providing unit 10g provides (sends) recommendation information to the first user terminal 100 according to an information providing request from the first user terminal 100.

[0310] Recommendation information provided by the sales server 10 is displayed on the display unit 104 of the first user terminal 100 in a prescribed display format.

[0311] Figure 26 This is a diagram illustrating an example of displaying recommendation information on the first user terminal.

[0312] As recommended poses, information on recommended poses is displayed in the order shown in the recommendation information. Figure 26The example shown illustrates a situation where an illustration is displayed along with the type of pose. In this case, recommendation information, including the information in the illustration, is provided to the first user terminal 100. Alternatively, an actual photograph may be displayed instead of an illustration.

[0313] Recommended poses are displayed sequentially along the scrolling direction of the screen. Figure 26 In the example shown, the recommended poses are displayed sequentially, starting from the first position, by scrolling the screen from bottom to top.

[0314] Thus, in the image data sales system of this embodiment, information about recommended sales images (in the above example, information about recommended poses) is generated as recommendation information and provided to the first user. The first user takes images and makes sales based on the provided recommendation information, thereby enabling the sale of image data that is easily purchased (image data with high sales expectations).

[0315] [Variation Example]

[0316] [Recommended Information]

[0317] In the above embodiments, the example described uses information about generating recommended poses as recommendation information, but the information generated as recommendation information is not limited to this. Furthermore, recommendation information can be generated from perspectives such as composition, background, scene, expression, and clothing. Moreover, recommendation information combining multiple perspectives can also be generated. For example, information combining recommended poses and compositions can be generated as recommendation information. In this case, for example, poses and compositions with evaluation values ​​above a threshold or ranked above a threshold are extracted, and the extracted poses and compositions are combined to generate recommendation information.

[0318] [Generation of Recommendation Information Using Artificial Intelligence]

[0319] Recommendations can also be generated using artificial intelligence. In particular, when image data is categorized from multiple perspectives, AI can be used to generate recommended combinations of information as recommendations (the so-called utilization of generative AI). For example, when image data is categorized by pose and composition, AI can generate recommended combinations of poses and compositions based on the evaluation values ​​of the poses and compositions. Elements such as background, facial expressions, and clothing can also be added to generate more detailed information. Furthermore, date and time elements can be added to generate information corresponding to specific periods.

[0320] Multiple recommendations can also be generated using artificial intelligence. In this case, it is preferable to generate recommendations after sorting them.

[0321] Furthermore, the information output as a result is preferably output as image information. For example, when outputting recommended poses and compositions, an image illustrating the poses and compositions is generated and output. Alternatively, an image with similar content can be output.

[0322] Furthermore, recommendation information can also be generated as information when selling or releasing animations. In this case, information such as generating recommended pose changes can be used as recommendation information.

[0323] [Recommended information when there are multiple subjects]

[0324] For example, when shooting group photos or similar subjects that include multiple people within a single frame, it is preferable to generate recommendation information corresponding to the number of people. That is, recommendation information is generated that includes suggested compositions and poses for each person, corresponding to the number of people being photographed. For instance, when photographing a group of three, information on the arrangement (composition) of each person and their poses is generated as recommendation information. In this case, for example, the number of people to be photographed is obtained from the first user via the first user terminal 100.

[0325] [Providing Recommendations]

[0326] When generating multiple recommended poses, compositions, and other information as recommendations, the amount of information provided to the first user can be limited. For example, if the information is sorted, the first n pieces of information can be shown to the first user. Alternatively, the first user can be allowed to arbitrarily set the number n (threshold) to be displayed. Furthermore, the structure can be configured to only provide the first user with information whose evaluation value is above the threshold.

[0327] Furthermore, the structure can be configured to retrieve information on sold (including currently being sold) image data for each first user and provide information on unsold (unphotographed) image data. For example, when generating recommended poses or compositions as recommendation information, information on unsold poses or compositions for the first user is extracted from the information on multiple generated poses or compositions, and provided to the first user. Similarly, when generating information on combinations of recommended poses and compositions as recommendation information, information on combinations of unsold poses or compositions for the first user is extracted from the information on multiple generated combinations of poses and compositions, and provided to the first user. For example, information on sold image data can be retrieved from database server 20. Alternatively, when extracting unsold image data, the structure can be configured to extract it from image data with a rating value above a threshold or whose rating value is ranked above a threshold.

[0328] Alternatively, the following approach can be used: The first user obtains information about the image data that has been sold, and provides the first user with the poses and / or compositions that have not been sold by the first user as recommendation information, regardless of the rating value.

[0329] [other]

[0330] The structures and variations shown in the first embodiment above can also be appropriately applied to this embodiment.

[0331] [Third Implementation]

[0332] There is a known technique called collage, which combines multiple images to create a single image. The resulting image is called a collage image.

[0333] Figure 27 This is an example of a collage image. In particular, Figure 27 This shows an example of printing a collage image on an instant film (collage printing).

[0334] Figure 27 The example shown illustrates how three images, Im1, Im2, and Im3, are combined to generate a collage image ImC. Each image, Im1, Im2, and Im3, is displayed within a pre-divided frame to create the collage image ImC.

[0335] In this embodiment, when the first user sells a collage image, information about the recommended collage image is generated as recommendation information.

[0336] [Generation of Recommendation Information]

[0337] Here, we will take the case of generating recommended collage images (recommendation information) from the perspective of posing as an example for illustration.

[0338] Similar to the second embodiment described above, the information processing server 30 has functions such as a data acquisition unit 30a, an image analysis unit 30b, an evaluation extraction unit 30c, an evaluation value calculation unit 30d, and a recommendation information generation unit 30e (see reference). Figure 25 ).

[0339] The data acquisition unit 30a acquires image data and sales information of the image data from the database server 20.

[0340] The image analysis unit 30b analyzes the image data acquired by the data acquisition unit 30a and classifies it into multiple categories. In this embodiment, the image data is classified into multiple categories based on the pose of the subject.

[0341] Furthermore, the image analysis unit 30b determines whether the image data is image data of a collage image. If the image data is image data of a collage image, the image analysis unit 30b further classifies the image data into multiple categories from the viewpoint of the collage method (e.g., the way the boxes are divided). In this embodiment, the image data of the collage image is classified into multiple categories from the viewpoint of the way the boxes are divided (vertically divided into two equal parts, horizontally divided into two equal parts, vertically divided into three equal parts, etc.).

[0342] The evaluation extraction unit 30c extracts information (evaluation information) from the sales information of the image data to evaluate the image represented by the image data. In this embodiment, the sales amount of the image data is extracted as the evaluation information.

[0343] The evaluation value calculation unit 30d calculates the evaluation value according to each category classified by the image analysis unit 30b, based on the evaluation information extracted by the evaluation extraction unit 30c. In this embodiment, the evaluation value is calculated for each pose and each segmentation method of the frame.

[0344] The recommendation information generation unit 30e generates recommendation information based on the evaluation value information calculated by the evaluation value calculation unit 30d (information on the evaluation value of each pose and information on the evaluation value of each segmentation method of the frame). Specifically, the recommendation information includes information on the segmentation method of the frame to be recommended and information on the combined poses. For example, the recommendation information includes information on the segmentation method of the frame with the highest evaluation value and the number of poses corresponding to the number of segments of that frame. The pose information is selected in descending order of evaluation value. Multiple recommendation information can also be generated.

[0345] [Providing Recommendations]

[0346] Regarding the provision of recommendation information, the sales server 10 has functions such as a recommendation information acquisition unit 10f and a recommendation information provision unit 10g (see reference). Figure 25 ).

[0347] The recommendation information acquisition unit 10f processes the recommendation information (information on the recommended collage images) generated in the information processing server 30 by acquiring it from the information processing server 30.

[0348] The recommendation information providing unit 10g processes the recommendation information obtained from the information processing server 30 and provides it to the first user terminal 100. The recommendation information providing unit 10g provides (sends) recommendation information to the first user terminal 100 based on the information providing request from the first user terminal 100.

[0349] Recommendation information provided by the sales server 10 is displayed on the display unit 104 of the first user terminal 100 in a prescribed display format.

[0350] Thus, according to the image data sales system of this embodiment, when a first user sells a collage image, information about recommended collage images can be provided to the first user. The first user creates a collage image based on the provided recommendation information, thereby enabling the sale of image data of easily purchasable collage images.

[0351] [Variation Example]

[0352] [Recommended Information]

[0353] In the above embodiments, the example described is the generation of recommended collage image information from the perspective of posing; however, the information generated as recommended collage image information (recommendation information) is not limited to this. Furthermore, recommended collage image information can be generated from perspectives such as composition, background, scene, expression, and clothing. Moreover, it is also possible to combine multiple perspectives to generate recommended collage image information.

[0354] Furthermore, the combined posture information is based on the combination of postures with high evaluation values, but it can also be set to generate a structure by deliberately combining postures with low evaluation values.

[0355] Furthermore, in the above embodiment, the recommended information is structured to generate information on the segmentation method of the recommended boxes and the combined pose information. However, it can also be structured to generate information on only one of these. For example, it can be structured to generate information on the segmentation method of the recommended boxes only, and it can also be structured to generate information on only the combined pose information.

[0356] Furthermore, for example, information about the segmentation method of the box used can be received from the first user, and information such as recommended poses can be generated as recommendation information based on the received segmentation method of the box.

[0357] Similarly, image data for tiling images can be received from a first user, and information on recommended box segmentation methods can be generated based on the received image data as recommendation information. In this case, the information on recommended box segmentation methods is generated by analyzing the image data received from the first user.

[0358] Furthermore, it can be configured to automatically generate a recommended collage image by receiving multiple image data from a first user. In this case, the recommended collage image can be generated using all the received image data, or it can be generated using only a portion of the image data.

[0359] [Generation of Recommendation Information Using Artificial Intelligence]

[0360] Artificial intelligence can also be used to generate information for recommended collage images. For example, a model that automatically generates collage images with high sales expectations when multiple image data are input can be used to generate information for recommended collage images. In this case, for example, machine learning can be used to generate the model using image data of collage images recorded in database server 20 and their sales information (especially sales figures).

[0361] Furthermore, it can be configured to receive multiple image data sources and use the learned model to automatically generate recommended collage image structures.

[0362] [other]

[0363] The structures and variations shown in the first or second embodiments described above can also be appropriately applied to this embodiment.

[0364] [Other Implementation Methods]

[0365] [System Architecture]

[0366] The first user terminal 100 and the second user terminal 200 can be any computer capable of communicating with the sales server 10 via the network 2. Therefore, they do not necessarily have to be mobile computers.

[0367] Furthermore, in the above embodiment, an example was described using a printer that prints images on instant film as printer 300, but the type of printer 300 is not limited to this. For example, printers that print images on recording media using inkjet or thermal methods can also be used. Moreover, printer 300 does not necessarily have to be a portable printer; a stationary printer can also be used.

[0368] Furthermore, the connection between the printer 300 and the second user terminal 200 can be either wired or wireless.

[0369] [Hardware structure of each server]

[0370] The functions of each server are implemented through various processors. These processors include general-purpose processors that execute programs and function as various processing units, such as CPUs and / or GPUs (Graphics Processing Units); FPGAs (Field Programmable Gate Arrays); programmable logic devices (PLDs) whose circuit structures can be modified after manufacturing; and application-specific integrated circuits (ASICs) with circuit structures specifically designed to perform specific processes, i.e., dedicated circuits. The terms "program" and "software" have the same meaning.

[0371] A processing unit can be composed of one of these various processors, or it can be composed of two or more processors of the same or different types. For example, a processing unit can be composed of multiple FPGAs or a combination of a CPU and an FPGA. Furthermore, multiple processing units can be composed of a single processor. As examples of multiple processing units composed of a single processor, firstly, there is the following: as exemplified by computers used for clients or servers, a processor is composed of a combination of one or more CPUs and software, and this processor functions as multiple processing units. Secondly, there is the following: as exemplified by Systems on Chips (SoCs), a processor that implements the overall system functionality including multiple processing units is used, implemented by a single IC (Integrated Circuit) chip. Thus, various processing units are constructed using one or more of the aforementioned processors as hardware structures.

[0372] Furthermore, in the above embodiment, the structure is configured such that three servers (sales server 10, database server 20, and information processing server 30) perform each processing separately, but it can also be configured such that one server performs all processing. Additionally, it can also be configured such that the first user terminal 100 or the second user terminal 200 performs a portion of the processing.

[0373] Furthermore, the functions of each server can also be realized through so-called cloud computers.

[0374] [other]

[0375] The structures and functions described in the above embodiments can be appropriately combined.

[0376] Furthermore, the above embodiments were described using an example of applying the present invention to a system for online sales of image data for printing, but the application of the present invention is not limited thereto. The present invention can also be applied to systems that sell image data. Furthermore, the present invention can also be applied to systems that provide image data (including image data for printing) free of charge (including partially free of charge).

[0377] Furthermore, the buying and selling of image data is not limited to the use of conventional currency, but also includes the use of virtual currency (including so-called points). For example, a structure could be set up that issues points equivalent to currency and uses these points to purchase image data.

[0378] Symbol Explanation

[0379] 10-Sales Server, 10a-Data Receiving Department, 10b-Data Recording and Processing Department, 10c-Sales Processing Department, 10c1-Image Analysis Department, 10c2-Public Judgment Processing Department, 10d-Sales Management Department, 10e-Recommendation Information Acquisition Department, 10f-Recommendation Information Acquisition Department, 10g-Recommendation Information Provision Department, 11-Processor, 12-Main Storage Department, 13-Auxiliary Storage Department, 14-Operation Department, 15-Display Department, 16-Interface Department, 20-Database Server, 21-Processor, 22-Main Storage Department, 23-Auxiliary Storage Department, 24-Operation Department, 25-Display Department, 26-Interface Department, 30-Information Processing Server, 30a-Data Acquisition Department, 30b-Image Analysis Department, 30c-Evaluation Department Price Extraction Unit, 30d-Evaluation Value Calculation Unit, 30e-Recommendation Information Generation Unit, 31-Processor, 32-Main Storage Unit, 33-Auxiliary Storage Unit, 34-Operation Unit, 35-Display Unit, 36-Interface Unit, 100-First User Terminal, 100a-Image Data Acquisition Unit, 100b-Sales Setting Information Receiving Unit, 100c-Upload Processing Unit, 101-Processor, 102-Main Storage Unit, 103-Auxiliary Storage Unit, 104-Display Unit, 105-Operation Unit, 106-GPS Receiver Unit, 107-Camera Unit, 108-Sound Input Unit, 109-Sound Output Unit, 110-Communication Unit, 111-Short Range Wireless Communication Unit, 112-Sensor Unit, 200-Second User Terminal, 2 00a-Browsing Processing Unit, 200b-Purchase Processing Unit, 200c-Download Processing Unit, 200d-Printing Processing Unit, 201-Processor, 202-Main Storage Unit, 203-Auxiliary Storage Unit, 204-Display Unit, 205-Operation Unit, 206-GPS Receiver Unit, 207-Camera Unit, 208-Sound Input Unit, 209-Sound Output Unit, 210-Communication Unit, 211-Short Range Wireless Communication Unit, 212-Sensor Unit, 300-Printer, 301-Control Unit, 302-Printing Unit, 302a-Film Loading Chamber, 302b-Film Feeding Mechanism, 302c-Film Transport Mechanism, 302d-Print Head, 303-Short Range Wireless Communication Unit, 304-Operation Unit, 310 - Film pack, 400-photos (prints), FB11-Search button, FB21-Profile button, FB31-Print button, FB41-Album button, FC11-User display bar, FC12-Display bar for followed users, FC21-Seller information display bar, FC22-Profile image display bar, FC23-Sales volume and follower count information display bar, FC24-Message display bar, FC25-Showing image data information display bar, FC25i-Showing image data information, FC25i1-Image represented by the image data being sold, FC25i2-Sales period information for the image data being sold.FC25i3 - Information on the selling price of the image data currently being sold; FC25i4 - Information on comments attached to the image data currently being sold; FC25iF - Box; FC31 - Display bar for information during the sales period; FC32 - Display bar for the image represented by the image data; FC32f - Box; FC32i - Image represented by the image data; FC33 - Display bar for seller information; FC34 - Display bar for comments; FC35 - Display bar for selling price information; FC41 - Display bar for information during the sales period; FC42 - Display bar for the printed image; FC42f - Box; FC42i - Printed image; FC43 - Display bar for the response button; FC44 - Display bar for seller information; FC45 - Display bar for comments; IB21 - Button; IB22 - Button; I B23 - Button, IB31 - Button, IB32 - Button, IB41 - Button, IC11 - Display bar for the image represented by the image data for sale, IC11f - Box, IC11i - Image represented by the image data for sale, IC12 - Input bar for color information, IC13 - Input bar for comments, IC14 - Input bar for sales period, IC15 - Input bar for sales price, ID1 - First image data, ID2 - Second image data, ID3 - Third image data, ID4 - Fourth image data, ID5 - Fifth image data, Im1 - Image, Im2 - Image, Im3 - Image, ImC - Tiled image, LA1 - Animation indicating printing in progress, PM1 - Message indicating printing in progress, PM2 - Message indicating printing completed, U1 - First user, U2 - Second user.

Claims

1. An information processing apparatus comprising a processor, the processor performs the following processing: acquiring first image data and first information, the first information including information of an evaluation for an image represented by the first image data; analyzing the first image data and classifying it into a plurality of categories; calculating an evaluation value for each of the categories from the first information; and generating second information of second image data from the evaluation value. 2.The information processing apparatus according to claim 1, wherein the second information is information that contributes to promotion of sales. 3.The information processing apparatus according to claim 2, wherein the first information includes information of a case where the image represented by the first image data is gazed at, a case where it is selected, or a case where it is put in a shopping cart. 4.The information processing apparatus according to claim 2, wherein with respect to the evaluation, the shorter the period from the start of sales of the image represented by the first image data to the purchase, the higher the evaluation. 5.The information processing apparatus according to claim 1, wherein the first information includes information of sales amount as the information of the evaluation. 6.The information processing apparatus according to claim 5, wherein the evaluation value is an average value of sales amount. 7.The information processing apparatus according to any one of claims 1 to 6, wherein the first information includes information of a subject and / or a photographer, the processor calculates the evaluation value by weighting the information of the evaluation according to the information of the subject and / or the photographer. 8.The information processing apparatus according to any one of claims 1 to 6, wherein the second information is information of a priority order of publication in a case where a plurality of the second image data is sold. 9.The information processing apparatus according to claim 8, wherein the first information includes information of a date and time of sales, the processor performs the following processing: generating, for each of the categories, statistical information of the evaluation based on the date and time of sales; and generating the second information from the statistical information of the evaluation. 10.The information processing apparatus according to any one of claims 1 to 6, wherein the categories are classified according to a composition and / or a pose, the second information is information of a recommended composition and / or a pose. 11.The information processing apparatus according to claim 10, wherein the processor performs the following processing: extracting a composition and / or a pose for which the evaluation value is equal to or higher than a threshold value or a ranking of the evaluation value is equal to or higher than a threshold value; and generating information of the extracted composition and / or pose as the second information. 12.The information processing apparatus according to claim 11, wherein the processor performs the following processing: acquiring information of a composition and / or a pose that has been sold; and generating information of a composition and / or a pose that has not been sold from the extracted composition and / or pose as the second information. 13.The information processing apparatus according to claim 10, wherein the processor performs the following processing: extracting a composition and / or a pose for which the evaluation value is equal to or higher than a threshold value or a ranking of the evaluation value is equal to or higher than a threshold value; and generating information combining the extracted composition and the pose as the second information.

14. The information processing apparatus according to claim 10, wherein the processor performs the following processing: acquiring information of the number of persons in one shot; extracting a composition and / or a pose for which the evaluation value is above a threshold value or the ranking of the evaluation value is above a threshold value; and generating information of a composition and / or a pose corresponding to the number of persons from the extracted information of the composition and / or the pose as the second information.

15. The information processing apparatus according to any one of claims 1 to 6, wherein the categories are classified according to a composition and / or a pose, the second information is information of a combination of a composition and / or a pose at the time of generating a collage image.

16. An information processing method, wherein first image data and first information containing information of evaluation for an image represented by the first image data are acquired, the first image data is analyzed and classified into a plurality of categories, an evaluation value of each of the categories is calculated from the first information, second image data is generated from the evaluation value.

17. An information processing program which causes a computer to function as: a function of acquiring first image data and first information containing information of evaluation for an image represented by the first image data; a function of analyzing the first image data and classifying it into a plurality of categories; a function of calculating an evaluation value of each of the categories from the first information; and a function of generating second image data from the evaluation value.

18. A recording medium which is a nonvolatile and computer-readable recording medium, the recording medium recording the program according to claim 17.

Images