System

Abstract

A system is provided.SOLUTION: A system comprising: input means for an artist to register works into the system; artificial intelligence means for learning user preferences and recommending artwork suitable for the user; interface means for the user to search for and purchase artwork; database means for storing work data registered by the artist; and recommendation means for recommending artwork to the user based on a preference profile generated by the artificial intelligence means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Describe the "problem that the invention aims to solve" and the "means for solving the problem."

[0005] In recent years, many art lovers and beginners lack information on where to buy art and how to choose it. Meanwhile, despite producing high-quality works, artists struggle to make a living due to a lack of marketing and sales knowledge. This creates a problem of inefficient matching between users who want to purchase art and artists who want to sell it. The present invention aims to enable art lovers and beginners to easily find art that suits them and to enable artists to reach a wider audience with their work. [Means for solving the problem]

[0006] The present invention is a system including an input means for artists to register their works in the system, an artificial intelligence means for learning user preferences and recommending artworks suitable for the user, an interface means for users to search for and purchase artworks, a database means for storing artwork data registered by artists, and a recommendation means for recommending artworks to users based on the preference profile generated by the artificial intelligence means. This allows users searching for art to easily find artworks that suit their tastes and allows artists to effectively sell their works.

[0007] "Input Means" means the device or interface used by an artist to register their work with the system.

[0008] "Artificial intelligence means" means an algorithm or program that learns a user's preferences and recommends appropriate artworks based on those preferences.

[0009] "Interface means" means a user interface through which a user can search for, view, and purchase artworks.

[0010] "Database means" refers to a database system for storing and managing work data registered by artists and user preference profiles.

[0011] The "recommendation means" is a function that recommends suitable artworks to a user based on the user's preference profile generated by the artificial intelligence means.

[0012] "Features" are characteristic information such as color, composition, and theme extracted from image data of an artwork.

[0013] A "preference profile" is data about a user's preferences that is generated based on the user's past behavior, survey results, preference patterns, etc. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] In one embodiment of the present invention, an artwork recommendation system is described that includes an artist, a user, and a server.

[0036] System configuration

[0037] This system consists of an input means for artists to register their artworks, an artificial intelligence means to learn user preferences, an interface means for users to search for and purchase artworks, a database means to store artwork data, and a recommendation means to recommend artworks.

[0038] Program processing flow

[0039] 1. Artists register their works

[0040] Artist device:

[0041] To register their work, artists first log in to the application, then enter details such as the work's title, description, genre, and price, and upload an image file.

[0042] server:

[0043] The server receives artwork data and image files from the artist's device and stores them in a database. At the same time, it uses an image analysis engine to extract features from the image data and provides this data to an AI model. The AI ​​model then uses these features to classify and tag the artworks into appropriate categories.

[0044] 2. Learning user preferences

[0045] User device:

[0046] When a new user logs in to the application, they are presented with a preference survey. They enter their favorite art genres, colors, themes, etc., and an initial preference profile is generated based on this. For existing users, past browsing history and "like" information is also included.

[0047] server:

[0048] The server receives user preference data and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in a database.

[0049] 3. Artwork Recommendations

[0050] User device:

[0051] When a user opens the "Recommended" section in the application, a recommendation request is sent to the server.

[0052] server:

[0053] The server acquires the user's preference profile and selects appropriate artworks based on that information. At this time, the AI ​​engine takes into account the user's historical data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[0054] 4. Buying artwork

[0055] User device:

[0056] When a user finds a title they like, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[0057] server:

[0058] The server receives the payment information and processes the secure payment. If the payment is successful, the purchase information is saved in a database and a sales notification is sent to the artist, who can then initiate the shipping process for the artwork.

[0059] Specific examples

[0060] Example 1: Artist's work registration

[0061] Artist device:

[0062] In the "Upload New Work" form, the artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape Painting," and the price "$500," selects the image file, and clicks the upload button.

[0063] server:

[0064] The server receives the artwork data and image files and stores them in a database. The image analysis engine extracts features from the image data and provides them to the AI ​​model. The AI ​​model generates the genre "landscape painting" and tags "city, night view, neon" and updates the database.

[0065] Example 2: User recommendations

[0066] User device:

[0067] New users will be asked to fill out a survey and select "Favorite genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[0068] server:

[0069] The server receives the user's preference data and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in the database.

[0070] When a user opens the "Recommended Works" section, the AI ​​engine generates a list of appropriate works, including "urban night views," and sends it to the user's device.

[0071] As a result, the present invention allows art lovers and beginners to easily find artworks that suit their tastes, and allows artists to effectively sell their works.

[0072] The processing flow will be explained below.

[0073] Artist registration

[0074] Step 1:

[0075] Artist terminal: The artist logs in to the system.

[0076] Step 2:

[0077] Artist device: The artist opens the "Upload New Work" page.

[0078] Step 3:

[0079] Artist device: Enter detailed information such as the work title, description, genre, and price, and select an image file.

[0080] Step 4:

[0081] Artist device: Click the upload button.

[0082] Step 5:

[0083] Server: Receives artwork data and image files from the artist's device.

[0084] Step 6:

[0085] Server: Save the image file in a temporary directory.

[0086] Step 7:

[0087] Server: Stores work information in a database.

[0088] Step 8:

[0089] Server: Generate a work ID, rename the image file with the work ID, and save it.

[0090] Step 9:

[0091] Server: Uses an image analysis engine to extract features from image files (color, composition, theme, etc.).

[0092] Step 10:

[0093] Server: Inputs feature data into the AI ​​model to generate genre and style tags.

[0094] Step 11:

[0095] Server: Adds and updates the generated tags and features to the artwork information stored in the database.

[0096] Learning user preferences

[0097] Step 1:

[0098] User terminal: A new user logs into the system.

[0099] Step 2:

[0100] User device: The initial setup wizard displays a preference survey.

[0101] Step 3:

[0102] User device: The user inputs their favorite art genre, color, theme, etc.

[0103] Step 4:

[0104] Server: Receives survey results and historical data from user devices.

[0105] Step 5:

[0106] Server: Inputs this data into the AI ​​engine to generate a user preference profile.

[0107] Step 6:

[0108] Server: Saves and updates user preference profiles in a database.

[0109] Artwork recommendations

[0110] Step 1:

[0111] User device: A logged-in user clicks on the "Featured Art" section.

[0112] Step 2:

[0113] Server: Receives the recommendation request.

[0114] Step 3:

[0115] Server: Reads the user's preference profile from the database.

[0116] Step 4:

[0117] Server: Inputs the entire art work database into the AI ​​engine and generates a list of works that best match the user's preferences.

[0118] Step 5:

[0119] Server: Sends the recommendation results to the user device.

[0120] Purchase artwork

[0121] Step 1:

[0122] User device: The user clicks on the work they like and presses the "Purchase" button.

[0123] Step 2:

[0124] User device: Enter payment and shipping information into the purchase form.

[0125] Step 3:

[0126] User device: Press the checkout button to send a purchase request.

[0127] Step 4:

[0128] Server: Receives the purchase request.

[0129] Step 5:

[0130] Server: Sends payment information to a payment gateway for secure processing.

[0131] Step 6:

[0132] Server: If the payment is successful, save the purchase information and transaction ID to the database.

[0133] Step 7:

[0134] Server: Sends a sales notification to the artist and instructs them to begin the shipping process.

[0135] The above are the specific processing steps for carrying out the present invention.

[0136] Example 1

[0137] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0138] Conventional art recommendation systems make it difficult for users to find artworks that match their tastes. Furthermore, they lacked efficient management of artist-registered artwork data and accurate feature extraction, resulting in inappropriate tagging and categorization. Furthermore, they lacked a consistent interface to facilitate a smooth user purchasing experience. These issues need to be addressed.

[0139] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0140] In this invention, the server includes image analysis means for extracting features from image data entered by artists and generating appropriate categories and tags, artificial intelligence means for learning user preferences and generating recommendation results, and database means for storing artwork data. This makes it possible to provide a system that efficiently recommends artworks that match users' preferences and makes it easy for artists to manage and sell their artworks.

[0141] Understood. Create the definition below.

[0142] An "artist" is a person or entity that registers their artwork in the system.

[0143] "User" means any person or entity that uses the System to search for and purchase artworks.

[0144] "Input means" refers to a device or application that allows an artist to input detailed information about their work into the system.

[0145] "Artificial intelligence means" are algorithms or software that learn a user's preferences and recommend suitable artworks.

[0146] An "interface means" is a device or application that provides a user interface for users to search for and purchase artworks.

[0147] "Database means" refers to a system or device for storing work data registered by artists and user preference profiles.

[0148] "Recommendation Means" means a device or algorithm for recommending artworks to a User based on a preference profile generated by an Artificial Intelligence Means.

[0149] "Image analysis means" refers to devices or software that extract the features of image data entered by the artist and generate appropriate categories and tags.

[0150] "Features" are analytical data such as color, shape, and texture of image data extracted by image analysis means.

[0151] In one embodiment of the present invention, an artwork recommendation system is described in which artists, users, and a server work in cooperation.

[0152] System configuration

[0153] The system consists of the following main tools:

[0154] 1. A means for artists to register their work in the system

[0155] 2. Artificial intelligence means to learn user preferences

[0156] 3. An interface for users to search for and purchase artworks

[0157] 4. Database means for storing work data

[0158] 5. Recommended Ways to Recommend

[0159] 6. Image analysis method to extract features from image data and generate categories and tags

[0160] Program processing flow

[0161] Artists register their works

[0162] Artist device:

[0163] The artist logs in to the application. After logging in, they click the "Register a new work" button to open the registration form for a new work. There, they enter the work title "Urban night view", description "Neon-lit cityscape", genre "Landscape painting", and price "$500", and upload the image file of the work. Once this is complete, they click the "Submit" button.

[0164] server:

[0165] The server receives the artwork data and image files sent by the artist and stores them in a database. At the same time, it launches an image analysis engine (e.g., OpenCV) to extract features from the received image data. The extracted features are then fed into an AI model (e.g., TensorFlow), which then generates appropriate artwork categories and tags.

[0166] Learning user preferences

[0167] User device:

[0168] When a new user logs in to the application, a preference survey is displayed, in which the user enters information such as "Favorite art genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[0169] server:

[0170] The server provides the user's preference data to an AI engine (e.g., TensorFlow). The AI ​​engine then generates a user preference profile based on the data and stores it in a database. In addition, existing users can update their profiles by including their browsing history, click history, purchase history, and likes.

[0171] Artwork recommendations

[0172] User device:

[0173] When a user selects the "Recommended Works" section in the application, a recommendation request is sent to the server.

[0174] server:

[0175] The server obtains the user's preference profile and provides it to a recommendation engine. The recommendation engine (e.g., an AI algorithm using KNN or collaborative filtering) then selects the most suitable artworks based on this. If the user likes landscape paintings and colorful urban themes, appropriate artworks such as "urban night view" will be selected. The selected recommendation list is then sent to the user's device.

[0176] Purchase artwork

[0177] User device:

[0178] The user opens the details page of the work they are interested in and clicks the "Purchase" button. They then enter their payment information and shipping address and click the "Confirm Purchase" button.

[0179] server:

[0180] The server receives the purchase request from the user and processes the payment securely (e.g., Stripe or PayPal). If the purchase is successful, the purchase information is saved in a database and a notification is sent to the artist, who can then initiate the shipping process for the artwork.

[0181] Specific examples

[0182] Example 1: Artist's work registration

[0183] Artist device:

[0184] The artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape painting," and the price "$500," and uploads the image file.

[0185] server:

[0186] The server receives the artwork data and image files and stores them in a database. It uses an image analysis engine (e.g., OpenCV) to extract features from the image data and provides them to an AI model (e.g., TensorFlow). The AI ​​model generates the genre "landscape painting" and tags "city, night view, neon" based on the features and updates the database.

[0187] Example 2: User recommendations

[0188] User device:

[0189] New users will be asked to fill out a survey and choose their favorite genre: landscape painting, favorite color scheme: colorful, and theme: urban.

[0190] server:

[0191] The server receives the user's preference data and supplies it to an AI engine (e.g., TensorFlow). The AI ​​engine generates a user preference profile and stores it in a database. When the user opens the "Recommended Works" section, the AI ​​engine generates a list of appropriate works, including "City Night Views," and sends it to the user's device.

[0192] Prompt Sentence Examples

[0193] "What kind of artwork do I like? I like landscapes, and I like colorful, urban-themed artwork."

[0194] The above is a specific embodiment of the artwork recommendation system according to the present invention, which allows users to efficiently discover artworks that match their tastes and allows artists to effectively sell their works.

[0195] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0196] Step 1:

[0197] Artists register their works

[0198] Artist device:

[0199] The artist logs in to the application. After logging in, they click the "Register a new work" button to open the registration form for a new work. There, they enter the work title "Urban night view", description "Neon-lit cityscape", genre "Landscape painting", and price "$500", and upload the image file of the work. Once complete, they click the "Submit" button.

[0200] Input: Title, description, genre, price, image file

[0201] Output: A request to send the work to the server

[0202] Step 2:

[0203] Receiving and storing artwork data and image files on the server

[0204] server:

[0205] The server receives the artwork data and image files sent by the artist and stores them in a database.

[0206] Input: Artwork data and image files from the artist

[0207] Output: Artwork data stored in a database and image files

[0208] Step 3:

[0209] Image data feature extraction and tag generation

[0210] server:

[0211] The server launches an image analysis engine (e.g., OpenCV) and extracts features (color, shape, texture, etc.) from the received image data. The extracted features are fed into an AI model (e.g., TensorFlow), which then generates appropriate categories and tags for the work. The results are then stored in a database.

[0212] Input: Image file

[0213] Data processing and data calculation: Feature extraction through image analysis, category and tag generation through AI models

[0214] Output: Features, categories, tags, updated database

[0215] Step 4:

[0216] Entering user preference data and creating profiles

[0217] User device:

[0218] When a new user logs in to the application, a preference survey is displayed, in which the user enters information such as "Favorite art genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[0219] Input: User preference data

[0220] Output: Preference data request to server

[0221] Step 5:

[0222] Server-generated and stored preference profiles

[0223] server:

[0224] The server provides the preference data acquired from the user to an AI engine (e.g., TensorFlow). The AI ​​engine generates a user preference profile based on this data and stores this profile in a database. In addition, existing users can update their profile by including their past browsing history, click history, purchase history, and likes.

[0225] Input: User preference data

[0226] Data processing and data calculation: AI engine generates preference profiles

[0227] Output: Preference profile, updated database

[0228] Step 6:

[0229] User submits recommendation request

[0230] User device:

[0231] When a user selects the "Recommended Works" section in the application, a recommendation request is sent to the server.

[0232] Input: Recommendation request

[0233] Output: Recommendation request sent to server

[0234] Step 7:

[0235] Recommendation generation and sending

[0236] server:

[0237] The server obtains the user's preference profile and provides it to the recommendation engine. The recommendation engine (e.g., an AI algorithm using KNN or collaborative filtering) selects the most suitable artwork based on the user's preference profile and historical data. For example, if the user prefers "landscapes, colorful, urban," "urban night views" will be selected. The selected recommendation list is sent to the user's device.

[0238] Input: Preference profile, historical data

[0239] Data processing and data calculation: Selection of works by recommendation engine

[0240] Output: Generate recommendation list and send it to user device

[0241] Step 8:

[0242] Users submitting purchase requests for works

[0243] User device:

[0244] The user opens the details page of the work they are interested in and clicks the "Purchase" button. They enter their payment information and shipping address and click the "Confirm Purchase" button.

[0245] Input: Purchase request, payment information, shipping information

[0246] Output: Purchase request sent to server

[0247] Step 9:

[0248] Server processes payments and stores purchase information

[0249] server:

[0250] The server receives the purchase request from the user and processes the payment securely (e.g., Stripe or PayPal). If the purchase is successful, the purchase information is saved in a database and a notification is sent to the artist, who can then initiate the shipping process for the artwork.

[0251] Input: Purchase request, payment information, shipping information

[0252] Data processing and calculation: payment processing, storing purchase information

[0253] Output: Purchase confirmation, notification sent, updated database

[0254] (Application example 1)

[0255] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0256] Conventional art recommendation systems have made it difficult for users to visualize artworks. They also require users to visit physical galleries, which is time-consuming and laborious. Furthermore, they can sometimes be less accurate in recommending appropriate artworks based on a user's preferences, which can discourage purchases. There is a need for new methods to resolve these issues and more effectively provide artworks to users.

[0257] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0258] In this invention, the server includes input means for artists to register their works in the system, artificial intelligence means for learning user preferences and recommending artworks that suit the user, interface means for users to search for and purchase artworks, and virtual display means for displaying artworks in a virtual space constructed on a smartphone. This allows users to easily appreciate artworks in the virtual space through their smartphones and receive highly accurate recommendations of artworks that suit their preferences, enabling them to make efficient purchases.

[0259] "Input means" refers to the means by which artists input the information necessary to register their artworks in the system. Specifically, it includes a form in which detailed information such as the title, description, genre, price, and image file of the artwork can be input.

[0260] An "artificial intelligence means" is a means equipped with machine learning algorithms that learn a user's preferences and, based on that data, recommend the most suitable artworks to the user.

[0261] "Interface means" means means including a user interface for users to search for and purchase artworks, specifically providing a GUI (Graphical User Interface) for browsing, filtering, searching, and purchasing artworks.

[0262] "Database means" refers to data storage means for storing work data registered by artists and user preference data. Specifically, it includes a cloud-based database system.

[0263] "Recommendation Means" means means for recommending artworks to users based on a preference profile generated by artificial intelligence means, and specifically includes a recommendation algorithm based on an AI engine.

[0264] "Virtual display means" refers to a means for displaying artworks in a virtual space created on a smartphone. Specifically, a virtual exhibition space is created using game engines such as Unity or Unreal Engine, allowing users to visually appreciate artworks in the virtual space.

[0265] System Overview

[0266] The present invention relates to an art recommendation system that includes an artist, a user, and a server. The system provides a function that allows users to view and purchase art in a virtual space on their smartphone. The system includes the following main means:

[0267] 1. A means for artists to register their work in the system

[0268] 2. Artificial intelligence means to learn user preferences and recommend suitable artworks

[0269] 3. An interface for users to search for and purchase artworks

[0270] 4. Database means for storing artwork data registered by artists

[0271] 5. Recommendation means for recommending artworks to a user based on a preference profile generated by artificial intelligence means

[0272] 6. A virtual display means for displaying artworks in a virtual space constructed on a smartphone

[0273] Program processing

[0274] This system is implemented using the following hardware and software:

[0275] Hardware: Smartphone (iOS or Android), server, cloud-based database (e.g., Firebase, Amazon DynamoDB)

[0276] Software and Data Processing:

[0277] Smartphone app: Building a virtual world using Unity and Unreal Engine

[0278] Cloud server: Manages artist artwork data, user preference data, and purchase data using Google Cloud and AWS

[0279] AI engine: Uses TensorFlow and PyTorch to learn user preferences and recommend works

[0280] Detailed Description

[0281] 1. Artist work registration:

[0282] Artists log in to the smartphone app, enter details such as the title, description, genre, and price of their work, and upload image files.

[0283] The server receives artwork data and image files from the artist's device and stores them in a cloud database. At the same time, it uses an image analysis engine to extract features from the image data and provides that data to the AI ​​model.

[0284] 2. Learning user preferences:

[0285] New users install the app and answer a questionnaire to generate an initial preference profile, including their favorite art genres, colors, and themes.

[0286] The server receives preference data from users and supplies it to the AI ​​engine. The AI ​​engine generates and updates preference profiles and stores them in a database. Existing users' past browsing history and "like" information are used to update their profiles.

[0287] 3. Artwork Recommendations:

[0288] When a user opens the "Recommended" section of the app, a recommendation request is sent to your server.

[0289] The server obtains the user's preference profile and uses an AI engine to select relevant works based on that information. The list of recommended works is then sent to the user's smartphone.

[0290] 4. Purchasing artwork:

[0291] When a user finds a title they like, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[0292] The server receives the payment information and processes the transaction. If the transaction is successful, the purchase information is saved in a database and a notification is sent to the artist, who can then begin the process of shipping the artwork.

[0293] Examples and prompts

[0294] Example 1:

[0295] The artist fills out the "Upload a New Work" form, entering the title of the work "Urban Dream," description "Neon-lit Night City," genre "Contemporary Art," and price "$1,000," then selects and uploads an image file.

[0296] The server receives this and stores it in a database. An AI model analyzes the image features and generates a genre and tag.

[0297] Example 2:

[0298] New users fill out a survey and select their favorite genre as "contemporary art," their favorite color as "vivid," and their theme as "urban."

[0299] Based on this, the AI ​​engine recommends suitable works that include "urban dreams."

[0300] Example prompt sentence:

[0301] Example artist submission prompt: "Submit a new artwork. Details are as follows: Title: 'Urban Dreams', Genre: 'Contemporary Art', Price: $1000."

[0302] Example prompt for user recommendations: "Recommend the most suitable artwork based on the user's profile. Favorite genre: 'Contemporary Art', Color: 'Vibrant', Theme: 'Urban'."

[0303] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0304] Step 1:

[0305] Registering your work

[0306] Input: Artists enter and upload detailed information such as the title, description, genre, and price of their work, as well as image files.

[0307] Operation: The artist's device sends the input information to the server, which receives the artwork data and image files.

[0308] Output: The server stores the received data in a database and supplies the image data to the image analysis engine, which extracts features from the image data.

[0309] Step 2:

[0310] Image analysis and tagging

[0311] Input: The image data received by the server in step 1.

[0312] How it works: Image features are extracted using the server's image analysis engine (e.g., TensorFlow) and these features are fed into the AI ​​model.

[0313] Output: The AI ​​model generates appropriate tags based on the image data features and updates the database.

[0314] Step 3:

[0315] User preference profile generation

[0316] Input: Survey responses from new users (favorite genres, colors, themes, etc.) and historical data from existing users.

[0317] How it works: The user device sends this data to the server, and the AI ​​engine generates and updates a preference profile.

[0318] Output: The server stores the generated and updated preference profile in the database.

[0319] Step 4:

[0320] Artwork Recommendations

[0321] Input: User preference profile and artwork data in the database.

[0322] How it works: When a user opens the "Recommended" section, their device sends a recommendation request to the server, where the server's AI engine selects relevant titles based on the user's preference profile.

[0323] Output: The list of recommended artworks is sent to the user's device and displayed.

[0324] Step 5:

[0325] Purchase artwork

[0326] Input: The user selects the title they want to purchase and enters their payment and shipping information.

[0327] How it works: The user device sends a purchase request to the server, and the server processes the payment (e.g., using a payment service such as Stripe or PayPal).

[0328] Output: If the payment is successful, the purchase information is saved in the database, a sale notification is sent to the artist, and information is provided to initiate the shipping process.

[0329] Step 6:

[0330] Displaying artwork in a virtual space

[0331] Input: List of recommended artworks and detailed data (QR codes, image files, etc.).

[0332] How it works: The user's device receives data sent from the server and displays the artwork in a virtual space. Using Unity or Unreal Engine, the user can navigate the virtual space and appreciate the artwork in detail.

[0333] Output: A virtual gallery will be displayed on the user's smartphone screen, allowing the user to freely view the artwork.

[0334] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0335] In one embodiment of the present invention, an artwork recommendation system is described that includes an artist, a user, and a server, and is capable of recommending artworks based on the user's emotional state, particularly by combining an emotion engine that recognizes the user's emotions.

[0336] System configuration

[0337] This system consists of an input means for artists to register their artworks, an artificial intelligence means to learn user preferences, an interface means for users to search for and purchase artworks, a database means to store artwork data, a recommendation means to recommend artworks, and an emotion engine to recognize user emotions.

[0338] Program processing flow

[0339] 1. Artists register their works

[0340] Artist device:

[0341] Artists log in to the system and open the "Upload a New Work" page to register their work. They enter details such as the work title, description, genre, and price, and then select and upload an image file.

[0342] server:

[0343] The server receives artwork data and image files from the artist's device and stores them in a database. It uses an image analysis engine to extract features from the image data and provides that data to an AI model. The AI ​​model then uses these features to classify and tag the artworks in appropriate categories.

[0344] 2. Learning user preferences

[0345] User device:

[0346] When a new user logs in to the system, they are presented with a preference survey in which they input their favorite art genres, colors, themes, etc. Existing users' preference profiles also include their past browsing history and "like" information.

[0347] server:

[0348] The server receives user preference data and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in a database.

[0349] 3. Implementing the Emotion Engine

[0350] User device:

[0351] When users use the system, emotional data is collected in real time through cameras, microphones, text input, and other means.

[0352] server:

[0353] The server inputs the collected emotional data into an emotion engine and performs facial expression analysis, tone of voice analysis, text emotion analysis, and other processes to recognize the user's current emotions.

[0354] 4. Artwork Recommendations

[0355] User device:

[0356] When a user opens the "Recommended" section in the application, a recommendation request is sent to the server.

[0357] server:

[0358] The server acquires the user's preference and emotional profile and selects appropriate artworks based on that information. The AI ​​engine takes into account the user's real-time emotional data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[0359] 5. Buying artwork

[0360] User device:

[0361] When a user finds a title they like, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[0362] server:

[0363] The server receives the payment information and processes the secure payment. If the payment is successful, the purchase information is saved in a database and a sales notification is sent to the artist, who can then initiate the shipping process for the artwork.

[0364] Specific examples

[0365] Example 1: Artist's work registration

[0366] Artist device:

[0367] In the "Upload New Work" form, the artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape Painting," and the price "$500," selects the image file, and clicks the upload button.

[0368] server:

[0369] The server receives the artwork data and image files and stores them in a database. The image analysis engine extracts features from the image data and provides them to the AI ​​model. The AI ​​model generates the genre "landscape painting" and tags "city, night view, neon" and updates the database.

[0370] Example 2: User recommendations

[0371] User device:

[0372] New users are asked to fill out a questionnaire and select their favorite genre: landscape painting, favorite color: colorful, and theme: urban. During use, the system also collects real-time emotional data from the camera and microphone.

[0373] server:

[0374] The server receives the user's preference data and real-time emotion data and supplies it to the AI ​​engine, which then generates and updates the user's preference profile and emotion profile and stores them in a database.

[0375] When a user opens the "Recommended Works" section, the AI ​​engine generates a list of appropriate works, including "urban night views," and sends it to the user's device.

[0376] In this way, the present invention allows users to easily find artworks that match their tastes and emotional state, and allows artists to effectively sell their works.

[0377] The processing flow will be explained below.

[0378] Artist registration

[0379] Step 1:

[0380] Artist terminal: The artist logs in to the system.

[0381] Step 2:

[0382] Artist device: The artist opens the "Upload New Work" page.

[0383] Step 3:

[0384] Artist device: Enter detailed information such as the work title, description, genre, and price, and select an image file.

[0385] Step 4:

[0386] Artist device: Click the upload button.

[0387] Step 5:

[0388] Server: Receives artwork data and image files from the artist's device.

[0389] Step 6:

[0390] Server: Save the image file in a temporary directory.

[0391] Step 7:

[0392] Server: Stores work information in a database.

[0393] Step 8:

[0394] Server: Generate a work ID, rename the image file with the work ID, and save it.

[0395] Step 9:

[0396] Server: Uses an image analysis engine to extract features from image files (color, composition, theme, etc.).

[0397] Step 10:

[0398] Server: Inputs feature data into the AI ​​model to generate genre and style tags.

[0399] Step 11:

[0400] Server: Adds and updates the generated tags and features to the artwork information stored in the database.

[0401] Learning user preferences

[0402] Step 1:

[0403] User terminal: A new user logs into the system.

[0404] Step 2:

[0405] User device: The initial setup wizard displays a preference survey.

[0406] Step 3:

[0407] User device: The user inputs their favorite art genre, color, theme, etc.

[0408] Step 4:

[0409] Server: Receives survey results and historical data from user devices.

[0410] Step 5:

[0411] Server: Inputs this data into the AI ​​engine to generate a user preference profile.

[0412] Step 6:

[0413] Server: Saves and updates user preference profiles in a database.

[0414] Implementing the Emotion Engine

[0415] Step 1:

[0416] User device: Emotional data is collected in real time from the camera and microphone while the user is using the system.

[0417] Step 2:

[0418] Server: Inputs the collected emotion data into the emotion engine.

[0419] Step 3:

[0420] Server: The emotion engine performs facial expression analysis, tone of voice analysis, and text emotion analysis.

[0421] Step 4:

[0422] Server: Generates real-time user emotion data and stores / updates it in the database.

[0423] Artwork recommendations

[0424] Step 1:

[0425] User device: A logged-in user clicks on the "Featured Art" section.

[0426] Step 2:

[0427] Server: Receives the recommendation request.

[0428] Step 3:

[0429] Server: Reads the user's preference profile and emotion profile from the database.

[0430] Step 4:

[0431] Server: Input the entire art work database into the AI ​​engine and generate a list of the most suitable artworks based on the user's preferences and real-time emotional data.

[0432] Step 5:

[0433] Server: Sends the recommendation results to the user device.

[0434] Purchase artwork

[0435] Step 1:

[0436] User device: The user clicks on the work they like and presses the "Purchase" button.

[0437] Step 2:

[0438] User device: Enter payment and shipping information into the purchase form.

[0439] Step 3:

[0440] User device: Press the checkout button to send a purchase request.

[0441] Step 4:

[0442] Server: Receives the purchase request.

[0443] Step 5:

[0444] Server: Sends payment information to a payment gateway for secure processing.

[0445] Step 6:

[0446] Server: If the payment is successful, save the purchase information and transaction ID to the database.

[0447] Step 7:

[0448] Server: Sends a sales notification to the artist and instructs them to begin the shipping process.

[0449] The above are the specific processing steps based on the invention that combines emotion engines.

[0450] Example 2

[0451] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0452] Conventional art recommendation systems can recommend artworks based on user preference data, but they cannot take into account the user's current emotional state. As a result, they are unable to provide artworks that match the user's real-time emotions, making it difficult to improve user satisfaction.

[0453] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for artists to register their works in the system, an artificial intelligence means for learning the user's preferences and recommending artworks suitable for the user, and an emotion engine means for recognizing the user's emotions. This makes it possible to recommend artworks that match not only the user's preferences but also their real-time emotional state.

[0454] "Input means" is an interface that allows artists to register information about their works in the system.

[0455] "Artificial intelligence means" refers to a function within the system that learns user preference data and recommends suitable artworks to users based on that data.

[0456] "Interface means" refers to a user interface that allows a user to search for and purchase artworks.

[0457] "Database means" refers to data storage within the system for storing work data registered by artists, user preference data, and emotional data.

[0458] "Recommendation Means" means functionality within the System for recommending artworks to Users based on their preference profiles generated by the Artificial Intelligence Means.

[0459] The "emotion engine means" is a function within the system for recognizing a user's emotions and generating an emotion profile based thereon.

[0460] An "emotion profile" is data representing the user's emotional state as recognized by the emotion engine means.

[0461] In one embodiment of the present invention, an artwork recommendation system including an artist, a user, and a server is described. In particular, the system is combined with an emotion engine that recognizes the user's emotions, enabling the recommendation of artworks based on the user's emotional state. The configuration of the system is described in detail below.

[0462] System configuration

[0463] This system consists of an input means for artists to register their artworks, an artificial intelligence means to learn user preferences, an interface means for users to search for and purchase artworks, a database means to store artwork data, a recommendation means to recommend artworks, and an emotion engine to recognize user emotions.

[0464] Explanation of program processing

[0465] Artists register their works

[0466] Artist device:

[0467] The artist logs in to the system and opens the "Upload New Work" page, where they enter details such as the work title, description, genre, and price, and then select and upload an image file.

[0468] server:

[0469] The server stores artwork data and image files received from the artist's device. It uses an image analysis engine to extract features from the image data and provides that data to the AI ​​model. The AI ​​model then uses these features to classify and tag the artworks in appropriate categories.

[0470] Examples:

[0471] The artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape painting," and the price "$500," and uploads the image file.

[0472] Learning user preferences

[0473] User device:

[0474] When a new user logs into the system, they are presented with a preference questionnaire in which they are asked to enter their favorite art genres, colors, themes, etc.

[0475] server:

[0476] The server receives preference data from the user device and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in a database.

[0477] Examples:

[0478] New users fill out the survey with "Favorite genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[0479] Implementing the Emotion Engine

[0480] User device:

[0481] When users use the system, emotional data is collected in real time through cameras, microphones, text input, and other means.

[0482] server:

[0483] The server inputs the collected emotional data into an emotion engine and performs facial expression analysis, tone of voice analysis, text emotion analysis, and other processes to recognize the user's current emotions.

[0484] Examples:

[0485] While the user is using the application, the camera captures the user's facial expressions and the emotion engine analyzes the data to identify emotions such as "happiness" or "sadness."

[0486] Artwork recommendations

[0487] User device:

[0488] When a user opens the "Recommended for You" section, a recommendation request is sent to the server.

[0489] server:

[0490] The server acquires the user's preference and emotional profile and selects appropriate artworks based on that information. The AI ​​engine takes into account the user's real-time emotional data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[0491] Examples:

[0492] The server generates a list of recommended works, including "urban night views," based on the user's preference data and real-time emotional data, and presents it to the user.

[0493] Purchase artwork

[0494] User device:

[0495] When a user finds a title they wish to purchase, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[0496] server:

[0497] The server securely processes the payment information received, records the successful transaction in a database, and sends a sales notification to the artist.

[0498] Examples:

[0499] To purchase "City Night View," the user enters payment and shipping information, which is then securely processed by the server, who then notifies the artist of the sale.

[0500] Examples of prompt statements

[0501] "Please log in."

[0502] Enter details about your work and upload an image file.

[0503] "Choose your favorite art genre, color, and theme."

[0504] "Allow use of your camera and microphone to begin collecting emotional data."

[0505] "Generating a list of recommended titles. Please wait a moment."

[0506] The system allows users to easily access artworks that best suit their tastes and emotions, and allows artists to efficiently sell their work.

[0507] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0508] Program processing flow

[0509] Artists register their works

[0510] Step 1:

[0511] Artist device:

[0512] The artist logs into the system using their own account, which gives them access to their own artist page within the system.

[0513] input:

[0514] Artist login information (username, password).

[0515] output:

[0516] The artist's authentication token.

[0517] Specific behavior:

[0518] The artist enters their username and password and clicks the login button.

[0519] Step 2:

[0520] Artist device:

[0521] Artists open the "Upload New Work" page, enter details such as the work's title, description, genre, and price, and select and upload an image file.

[0522] input:

[0523] Title, description, genre, price, and image file of the work.

[0524] output:

[0525] Request to upload new work.

[0526] Specific behavior:

[0527] The artist enters the title "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape," and the price "$500," selects the image file, and clicks the upload button.

[0528] Step 3:

[0529] server:

[0530] The server stores the artwork data and image files received from the artist's device, then uses an image analysis engine to extract features from the image data and feeds that data to the AI ​​model.

[0531] input:

[0532] Artwork data and image files received from the artist's device.

[0533] output:

[0534] Image data features and category classification results.

[0535] Specific behavior:

[0536] The server stores the artwork data in a database, uses an image analysis engine to extract features, and supplies them to an AI model to generate categories and tags.

[0537] Learning user preferences

[0538] Step 1:

[0539] User device:

[0540] When a new user logs into the system, they are presented with a preference questionnaire, which asks about their favorite art genres, colors, themes, etc.

[0541] input:

[0542] User preference information (art genre, color, theme).

[0543] output:

[0544] Request to send preference survey.

[0545] Specific behavior:

[0546] A new user fills out the survey with "Favorite genre: landscape painting," "Favorite color: colorful," and "Theme: urban," and clicks the submit button.

[0547] Step 2:

[0548] server:

[0549] The server receives the preference data sent from the user device and supplies it to the AI ​​engine, which then generates a user preference profile and stores it in a database.

[0550] input:

[0551] Preference data sent from user devices.

[0552] output:

[0553] User preference profile.

[0554] Specific behavior:

[0555] The server sends the preference data to the AI ​​engine and stores the generated preference profile in a database.

[0556] Implementing the Emotion Engine

[0557] Step 1:

[0558] User device:

[0559] When users use the system, emotional data is collected in real time through cameras, microphones, text input, and other means.

[0560] input:

[0561] User facial expression, voice, and text data.

[0562] output:

[0563] Request to send real-time sentiment data.

[0564] Specific behavior:

[0565] The user authorizes the camera and microphone, and the system collects emotional data from facial expressions and voice.

[0566] Step 2:

[0567] server:

[0568] The server inputs the collected emotional data into an emotion engine and performs facial expression analysis, tone of voice analysis, text emotion analysis, and other processes to recognize the user's current emotions.

[0569] input:

[0570] Collected user sentiment data.

[0571] output:

[0572] User emotional profile.

[0573] Specific behavior:

[0574] The server analyzes the emotional data and stores emotions such as "joy" and "sadness" as an emotional profile.

[0575] Artwork recommendations

[0576] Step 1:

[0577] User device:

[0578] When a user opens the "Recommended for You" section, a recommendation request is sent to the server.

[0579] input:

[0580] Request for the creation of a "Recommended Works" section.

[0581] output:

[0582] Submit a recommendation request.

[0583] Specific behavior:

[0584] The user clicks the "Recommended Works" button.

[0585] Step 2:

[0586] server:

[0587] The server acquires the user's preference and emotional profile and selects appropriate artworks based on that information. The AI ​​engine takes into account the user's real-time emotional data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[0588] input:

[0589] User preference and emotional profiles.

[0590] output:

[0591] A list of recommended artworks.

[0592] Specific behavior:

[0593] The server provides the preference profile and emotional profile to the AI ​​engine and sends the generated list of works to the user.

[0594] Purchase artwork

[0595] Step 1:

[0596] User device:

[0597] When a user finds a title they wish to purchase, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[0598] input:

[0599] Payment and shipping information.

[0600] output:

[0601] Submit a purchase request.

[0602] Specific behavior:

[0603] Users select the work they like, press the "Purchase" button and enter their details.

[0604] Step 2:

[0605] server:

[0606] The server securely processes the payment information received, records the successful transaction in a database, and sends a sales notification to the artist.

[0607] input:

[0608] Payment and shipping information.

[0609] output:

[0610] Payment results and sales notification.

[0611] Specific behavior:

[0612] The server processes the payment information and, once the payment is successful, sends a sales notification to the artist, prompting them to begin the shipping process for the artwork.

[0613] (Application example 2)

[0614] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0615] The online art sales market has expanded in recent years, but a key issue is whether users can easily find artworks that suit their emotional state. Conventional systems primarily base recommendations on users' preference data and browsing history, making it difficult to respond to real-time emotional changes. This has led to problems such as users finding artworks that fit their emotional state at any given time, resulting in a decrease in satisfaction.

[0616] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0617] In this invention, the server includes an input means for artists to register their artworks in the system, an artificial intelligence means for learning user preferences and recommending artworks suitable for the user, and an interface means for the user to search for and purchase artworks. This makes it possible to recognize the user's emotional state in real time and recommend artworks using emotional data.

[0618] "Input means" refers to the means by which artists register their artworks in the system.

[0619] "Artificial intelligence means" refers to means for learning a user's preferences and, based on the results, recommending artworks suitable for the user.

[0620] "Interface means" refers to the means by which a user searches for and purchases artworks.

[0621] "Database means" refers to a means for storing work data registered by artists.

[0622] "Recommendation means" means means for recommending artworks to a user based on a preference profile generated by an artificial intelligence means.

[0623] An "emotion engine" is a means for recognizing a user's emotional state in real time.

[0624] An "emotion-based recommendation method" is a method for recommending artworks using emotion data recognized by an emotion engine.

[0625] The present invention relates to a system for recommending artworks based on a user's emotional state, and is configured as follows.

[0626] System configuration

[0627] The system consists of the following main components:

[0628] 1. Input method: This is the method by which artists register their works in the system. Artists input the title, description, image data, etc. of their works and save them in the database.

[0629] 2. Artificial intelligence means: This means learns user preferences and recommends artworks based on that data. It also analyzes image data of artworks, extracts features, and stores them in a database.

[0630] 3. Interface: A means for users to search for and purchase artworks. Users can interact with the system using this interface.

[0631] 4. Database means: A means for storing artwork data registered by artists. The database stores detailed information about artworks, image data, and user preference profiles.

[0632] 5. Recommendation: This is a method for recommending artworks to users based on their preference profile generated by artificial intelligence. It also takes into account the user's history and preference data to generate appropriate recommendation results.

[0633] 6. Emotion Engine: A means of recognizing the user's emotional state in real time. It uses cameras and microphones to collect emotional data and analyzes it in real time.

[0634] 7. Emotion-based recommendation method: This method uses emotional data recognized by an emotion engine to recommend artworks to users.

[0635] System Operation

[0636] The server uses these means to perform the following processing.

[0637] 1. Artist registration

[0638] The server stores the artwork data (title, description, image data, etc.) received from the artist in a database. The image analysis engine analyzes the received image data and extracts features.

[0639] 2. Learning user preferences

[0640] The server analyzes the preference data entered by new users and the browsing history of existing users, and generates and updates preference profiles using an AI engine.

[0641] 3. Emotional Data Collection and Recognition

[0642] The user device collects emotion data in real time via a camera and microphone while the user is using the system, and the collected data is sent to a server where it is analyzed by an emotion engine.

[0643] 4. Artwork Recommendations

[0644] The server selects the most suitable artworks based on the user's preference and emotional profiles and recommends them to the user through recommendation methods.

[0645] 5. Buying artwork

[0646] On the user's device, the user clicks the purchase button and enters the necessary payment information. The server then processes the payment securely and stores the purchase information in a database.

[0647] Hardware and software used

[0648] Hardware: Camera, microphone

[0649] Software: OpenCV, EmotionRecognizer, AIEngine, ArtDatabase

[0650] Examples of specific examples and prompts

[0651] Example 1: When a user feels "tired"

[0652] Prompt: "What artwork helps you relax? Do you prefer tranquil landscapes or artwork with muted colors?"

[0653] Server operation: Captures the user's facial expression with a camera, determines their level of fatigue, and recommends relaxing artwork.

[0654] Example 2: When a user feels happy

[0655] Prompt: "What bright artwork would fit your current happy mood? Would you prefer something colorful and positive?"

[0656] Server operation: The camera captures the user's facial expression, determines whether they are "happy," and recommends bright and colorful artwork.

[0657] Thus, the present invention provides a system that allows users to easily find and purchase artwork that matches their current emotional state.

[0658] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0659] Step 1:

[0660] Artist registration

[0661] Input: Artist enters work title, description, genre, image file, etc.

[0662] Server operation: Receives input artwork data and stores it in a database. It also uses an image analysis engine to extract features from the image data and provides that data to an AI model. Based on the features, the AI ​​model classifies and tags the artwork into appropriate categories.

[0663] Output: The artwork data and its features are stored in a database, and the artworks are classified by category and tag.

[0664] Step 2:

[0665] Learning user preferences

[0666] Input: Response data from preference surveys from new users, past browsing history and likes from existing users, etc.

[0667] Server operation: Receives preference data and provides it to the AI ​​engine, which then generates and updates the user's preference profile and stores it in the database.

[0668] Output: Preference profile for each user stored in a database.

[0669] Step 3:

[0670] Emotion data collection and recognition

[0671] Input: Real-time emotion data collected via the user's device camera, microphone, and text input.

[0672] Server operation: The collected emotional data is input into the emotion engine, and the user's emotional state is recognized by performing facial expression analysis, tone of voice analysis, text emotion analysis, etc.

[0673] Output: Real-time emotional state data of the user.

[0674] Step 4:

[0675] Artwork recommendations

[0676] Input: User preference profile, real-time sentiment data, user request for recommendations.

[0677] Server operation: Based on this data, the AI ​​engine selects and recommends the most relevant artworks. A generative AI model is used to generate emotion-based prompts and recommend artworks based on them.

[0678] Output: A list of recommended artworks for the user.

[0679] Step 5:

[0680] Purchase artwork

[0681] Input: Artwork selected by the user, payment information, and shipping information.

[0682] User device actions: clicking the purchase button and entering required information.

[0683] Server Action: Receives payment information and processes the transaction. If successful, stores the purchase in a database and sends a sales notification to the artist.

[0684] Output: A confirmation message of the completed purchase and a sale notification to the artist.

[0685] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0686] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search<url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0687] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0688] [Second embodiment]

[0689] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0690] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0691] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0692] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0693] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0694] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0695] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0696] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0697] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0698] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0699] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0700] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0701] In one embodiment of the present invention, an artwork recommendation system is described that includes an artist, a user, and a server.

[0702] System configuration

[0703] This system consists of an input means for artists to register their artworks, an artificial intelligence means to learn user preferences, an interface means for users to search for and purchase artworks, a database means to store artwork data, and a recommendation means to recommend artworks.

[0704] Program processing flow

[0705] 1. Artists register their works

[0706] Artist device:

[0707] To register their work, artists first log in to the application, then enter details such as the work's title, description, genre, and price, and upload an image file.

[0708] server:

[0709] The server receives artwork data and image files from the artist's device and stores them in a database. At the same time, it uses an image analysis engine to extract features from the image data and provides this data to an AI model. The AI ​​model then uses these features to classify and tag the artworks into appropriate categories.

[0710] 2. Learning user preferences

[0711] User device:

[0712] When a new user logs in to the application, they are presented with a preference survey. They enter their favorite art genres, colors, themes, etc., and an initial preference profile is generated based on this. For existing users, past browsing history and "like" information is also included.

[0713] server:

[0714] The server receives user preference data and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in a database.

[0715] 3. Artwork Recommendations

[0716] User device:

[0717] When a user opens the "Recommended" section in the application, a recommendation request is sent to the server.

[0718] server:

[0719] The server acquires the user's preference profile and selects appropriate artworks based on that information. At this time, the AI ​​engine takes into account the user's historical data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[0720] 4. Buying artwork

[0721] User device:

[0722] When a user finds a title they like, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[0723] server:

[0724] The server receives the payment information and processes the secure payment. If the payment is successful, the purchase information is saved in a database and a sales notification is sent to the artist, who can then initiate the shipping process for the artwork.

[0725] Specific examples

[0726] Example 1: Artist's work registration

[0727] Artist device:

[0728] In the "Upload New Work" form, the artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape Painting," and the price "$500," selects the image file, and clicks the upload button.

[0729] server:

[0730] The server receives the artwork data and image files and stores them in a database. The image analysis engine extracts features from the image data and provides them to the AI ​​model. The AI ​​model generates the genre "landscape painting" and tags "city, night view, neon" and updates the database.

[0731] Example 2: User recommendations

[0732] User device:

[0733] New users will be asked to fill out a survey and select "Favorite genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[0734] server:

[0735] The server receives the user's preference data and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in the database.

[0736] When a user opens the "Recommended Works" section, the AI ​​engine generates a list of appropriate works, including "urban night views," and sends it to the user's device.

[0737] As a result, the present invention allows art lovers and beginners to easily find artworks that suit their tastes, and allows artists to effectively sell their works.

[0738] The processing flow will be explained below.

[0739] Artist registration

[0740] Step 1:

[0741] Artist terminal: The artist logs in to the system.

[0742] Step 2:

[0743] Artist device: The artist opens the "Upload New Work" page.

[0744] Step 3:

[0745] Artist device: Enter detailed information such as the work title, description, genre, and price, and select an image file.

[0746] Step 4:

[0747] Artist device: Click the upload button.

[0748] Step 5:

[0749] Server: Receives artwork data and image files from the artist's device.

[0750] Step 6:

[0751] Server: Save the image file in a temporary directory.

[0752] Step 7:

[0753] Server: Stores work information in a database.

[0754] Step 8:

[0755] Server: Generate a work ID, rename the image file with the work ID, and save it.

[0756] Step 9:

[0757] Server: Uses an image analysis engine to extract features from image files (color, composition, theme, etc.).

[0758] Step 10:

[0759] Server: Inputs feature data into the AI ​​model to generate genre and style tags.

[0760] Step 11:

[0761] Server: Adds and updates the generated tags and features to the artwork information stored in the database.

[0762] Learning user preferences

[0763] Step 1:

[0764] User terminal: A new user logs into the system.

[0765] Step 2:

[0766] User device: The initial setup wizard displays a preference survey.

[0767] Step 3:

[0768] User device: The user inputs their favorite art genre, color, theme, etc.

[0769] Step 4:

[0770] Server: Receives survey results and historical data from user devices.

[0771] Step 5:

[0772] Server: Inputs this data into the AI ​​engine to generate a user preference profile.

[0773] Step 6:

[0774] Server: Saves and updates user preference profiles in a database.

[0775] Artwork recommendations

[0776] Step 1:

[0777] User device: A logged-in user clicks on the "Featured Art" section.

[0778] Step 2:

[0779] Server: Receives the recommendation request.

[0780] Step 3:

[0781] Server: Reads the user's preference profile from the database.

[0782] Step 4:

[0783] Server: Inputs the entire art work database into the AI ​​engine and generates a list of works that best match the user's preferences.

[0784] Step 5:

[0785] Server: Sends the recommendation results to the user device.

[0786] Purchase artwork

[0787] Step 1:

[0788] User device: The user clicks on the work they like and presses the "Purchase" button.

[0789] Step 2:

[0790] User device: Enter payment and shipping information into the purchase form.

[0791] Step 3:

[0792] User device: Press the checkout button to send a purchase request.

[0793] Step 4:

[0794] Server: Receives the purchase request.

[0795] Step 5:

[0796] Server: Sends payment information to a payment gateway for secure processing.

[0797] Step 6:

[0798] Server: If the payment is successful, save the purchase information and transaction ID to the database.

[0799] Step 7:

[0800] Server: Sends a sales notification to the artist and instructs them to begin the shipping process.

[0801] The above are the specific processing steps for carrying out the present invention.

[0802] Example 1

[0803] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0804] Conventional art recommendation systems make it difficult for users to find artworks that match their tastes. Furthermore, they lacked efficient management of artist-registered artwork data and accurate feature extraction, resulting in inappropriate tagging and categorization. Furthermore, they lacked a consistent interface to facilitate a smooth user purchasing experience. These issues need to be addressed.

[0805] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0806] In this invention, the server includes image analysis means for extracting features from image data entered by artists and generating appropriate categories and tags, artificial intelligence means for learning user preferences and generating recommendation results, and database means for storing artwork data. This makes it possible to provide a system that efficiently recommends artworks that match users' preferences and makes it easy for artists to manage and sell their artworks.

[0807] Understood. Create the definition below.

[0808] An "artist" is a person or entity that registers their artwork in the system.

[0809] "User" means any person or entity that uses the System to search for and purchase artworks.

[0810] "Input means" refers to a device or application that allows an artist to input detailed information about their work into the system.

[0811] "Artificial intelligence means" are algorithms or software that learn a user's preferences and recommend suitable artworks.

[0812] An "interface means" is a device or application that provides a user interface for users to search for and purchase artworks.

[0813] "Database means" refers to a system or device for storing work data registered by artists and user preference profiles.

[0814] "Recommendation Means" means a device or algorithm for recommending artworks to a User based on a preference profile generated by an Artificial Intelligence Means.

[0815] "Image analysis means" refers to devices or software that extract the features of image data entered by the artist and generate appropriate categories and tags.

[0816] "Features" are analytical data such as color, shape, and texture of image data extracted by image analysis means.

[0817] In one embodiment of the present invention, an artwork recommendation system is described in which artists, users, and a server work in cooperation.

[0818] System configuration

[0819] The system consists of the following main tools:

[0820] 1. A means for artists to register their work in the system

[0821] 2. Artificial intelligence means to learn user preferences

[0822] 3. An interface for users to search for and purchase artworks

[0823] 4. Database means for storing work data

[0824] 5. Recommended Ways to Recommend

[0825] 6. Image analysis method to extract features from image data and generate categories and tags

[0826] Program processing flow

[0827] Artists register their works

[0828] Artist device:

[0829] The artist logs in to the application. After logging in, they click the "Register a new work" button to open the registration form for a new work. There, they enter the work title "Urban night view", description "Neon-lit cityscape", genre "Landscape painting", and price "$500", and upload the image file of the work. Once this is complete, they click the "Submit" button.

[0830] server:

[0831] The server receives the artwork data and image files sent by the artist and stores them in a database. At the same time, it launches an image analysis engine (e.g., OpenCV) to extract features from the received image data. The extracted features are then fed into an AI model (e.g., TensorFlow), which then generates appropriate artwork categories and tags.

[0832] Learning user preferences

[0833] User device:

[0834] When a new user logs in to the application, a preference survey is displayed, in which the user enters information such as "Favorite art genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[0835] server:

[0836] The server provides the user's preference data to an AI engine (e.g., TensorFlow). The AI ​​engine then generates a user preference profile based on the data and stores it in a database. In addition, existing users can update their profiles by including their browsing history, click history, purchase history, and likes.

[0837] Artwork recommendations

[0838] User device:

[0839] When a user selects the "Recommended Works" section in the application, a recommendation request is sent to the server.

[0840] server:

[0841] The server obtains the user's preference profile and provides it to a recommendation engine. The recommendation engine (e.g., an AI algorithm using KNN or collaborative filtering) then selects the most suitable artworks based on this. If the user likes landscape paintings and colorful urban themes, appropriate artworks such as "urban night view" will be selected. The selected recommendation list is then sent to the user's device.

[0842] Purchase artwork

[0843] User device:

[0844] The user opens the details page of the work they are interested in and clicks the "Purchase" button. They then enter their payment information and shipping address and click the "Confirm Purchase" button.

[0845] server:

[0846] The server receives the purchase request from the user and processes the payment securely (e.g., Stripe or PayPal). If the purchase is successful, the purchase information is saved in a database and a notification is sent to the artist, who can then initiate the shipping process for the artwork.

[0847] Specific examples

[0848] Example 1: Artist's work registration

[0849] Artist device:

[0850] The artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape painting," and the price "$500," and uploads the image file.

[0851] server:

[0852] The server receives the artwork data and image files and stores them in a database. It uses an image analysis engine (e.g., OpenCV) to extract features from the image data and provides them to an AI model (e.g., TensorFlow). The AI ​​model generates the genre "landscape painting" and tags "city, night view, neon" based on the features and updates the database.

[0853] Example 2: User recommendations

[0854] User device:

[0855] New users will be asked to fill out a survey and choose their favorite genre: landscape painting, favorite color scheme: colorful, and theme: urban.

[0856] server:

[0857] The server receives the user's preference data and supplies it to an AI engine (e.g., TensorFlow). The AI ​​engine generates a user preference profile and stores it in a database. When the user opens the "Recommended Works" section, the AI ​​engine generates a list of appropriate works, including "City Night Views," and sends it to the user's device.

[0858] Prompt Sentence Examples

[0859] "What kind of artwork do I like? I like landscapes, and I like colorful, urban-themed artwork."

[0860] The above is a specific embodiment of the artwork recommendation system according to the present invention, which allows users to efficiently discover artworks that match their tastes and allows artists to effectively sell their works.

[0861] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0862] Step 1:

[0863] Artists register their works

[0864] Artist device:

[0865] The artist logs in to the application. After logging in, they click the "Register a new work" button to open the registration form for a new work. There, they enter the work title "Urban night view", description "Neon-lit cityscape", genre "Landscape painting", and price "$500", and upload the image file of the work. Once complete, they click the "Submit" button.

[0866] Input: Title, description, genre, price, image file

[0867] Output: A request to send the work to the server

[0868] Step 2:

[0869] Receiving and storing artwork data and image files on the server

[0870] server:

[0871] The server receives the artwork data and image files sent by the artist and stores them in a database.

[0872] Input: Artwork data and image files from the artist

[0873] Output: Artwork data stored in a database and image files

[0874] Step 3:

[0875] Image data feature extraction and tag generation

[0876] server:

[0877] The server launches an image analysis engine (e.g., OpenCV) and extracts features (color, shape, texture, etc.) from the received image data. The extracted features are fed into an AI model (e.g., TensorFlow), which then generates appropriate categories and tags for the work. The results are then stored in a database.

[0878] Input: Image file

[0879] Data processing and data calculation: Feature extraction through image analysis, category and tag generation through AI models

[0880] Output: Features, categories, tags, updated database

[0881] Step 4:

[0882] Entering user preference data and creating profiles

[0883] User device:

[0884] When a new user logs in to the application, a preference survey is displayed, in which the user enters information such as "Favorite art genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[0885] Input: User preference data

[0886] Output: Preference data request to server

[0887] Step 5:

[0888] Server-generated and stored preference profiles

[0889] server:

[0890] The server provides the preference data acquired from the user to an AI engine (e.g., TensorFlow). The AI ​​engine generates a user preference profile based on this data and stores this profile in a database. In addition, existing users can update their profile by including their past browsing history, click history, purchase history, and likes.

[0891] Input: User preference data

[0892] Data processing and data calculation: AI engine generates preference profiles

[0893] Output: Preference profile, updated database

[0894] Step 6:

[0895] User submits recommendation request

[0896] User device:

[0897] When a user selects the "Recommended Works" section in the application, a recommendation request is sent to the server.

[0898] Input: Recommendation request

[0899] Output: Recommendation request sent to server

[0900] Step 7:

[0901] Recommendation generation and sending

[0902] server:

[0903] The server obtains the user's preference profile and provides it to the recommendation engine. The recommendation engine (e.g., an AI algorithm using KNN or collaborative filtering) selects the most suitable artwork based on the user's preference profile and historical data. For example, if the user prefers "landscapes, colorful, urban," "urban night views" will be selected. The selected recommendation list is sent to the user's device.

[0904] Input: Preference profile, historical data

[0905] Data processing and data calculation: Selection of works by recommendation engine

[0906] Output: Generate recommendation list and send it to user device

[0907] Step 8:

[0908] Users submitting purchase requests for works

[0909] User device:

[0910] The user opens the details page of the work they are interested in and clicks the "Purchase" button. They enter their payment information and shipping address and click the "Confirm Purchase" button.

[0911] Input: Purchase request, payment information, shipping information

[0912] Output: Purchase request sent to server

[0913] Step 9:

[0914] Server processes payments and stores purchase information

[0915] server:

[0916] The server receives the purchase request from the user and processes the payment securely (e.g., Stripe or PayPal). If the purchase is successful, the purchase information is saved in a database and a notification is sent to the artist, who can then initiate the shipping process for the artwork.

[0917] Input: Purchase request, payment information, shipping information

[0918] Data processing and calculation: payment processing, storing purchase information

[0919] Output: Purchase confirmation, notification sent, updated database

[0920] (Application example 1)

[0921] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0922] Conventional art recommendation systems have made it difficult for users to visualize artworks. They also require users to visit physical galleries, which is time-consuming and laborious. Furthermore, they can sometimes be less accurate in recommending appropriate artworks based on a user's preferences, which can discourage purchases. There is a need for new methods to resolve these issues and more effectively provide artworks to users.

[0923] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0924] In this invention, the server includes input means for artists to register their works in the system, artificial intelligence means for learning user preferences and recommending artworks that suit the user, interface means for users to search for and purchase artworks, and virtual display means for displaying artworks in a virtual space constructed on a smartphone. This allows users to easily appreciate artworks in the virtual space through their smartphones and receive highly accurate recommendations of artworks that suit their preferences, enabling them to make efficient purchases.

[0925] "Input means" refers to the means by which artists input the information necessary to register their artworks in the system. Specifically, it includes a form in which detailed information such as the title, description, genre, price, and image file of the artwork can be input.

[0926] An "artificial intelligence means" is a means equipped with machine learning algorithms that learn a user's preferences and, based on that data, recommend the most suitable artworks to the user.

[0927] "Interface means" means means including a user interface for users to search for and purchase artworks, specifically providing a GUI (Graphical User Interface) for browsing, filtering, searching, and purchasing artworks.

[0928] "Database means" refers to data storage means for storing work data registered by artists and user preference data. Specifically, it includes a cloud-based database system.

[0929] "Recommendation Means" means means for recommending artworks to users based on a preference profile generated by artificial intelligence means, and specifically includes a recommendation algorithm based on an AI engine.

[0930] "Virtual display means" refers to a means for displaying artworks in a virtual space created on a smartphone. Specifically, a virtual exhibition space is created using game engines such as Unity or Unreal Engine, allowing users to visually appreciate artworks in the virtual space.

[0931] System Overview

[0932] The present invention relates to an art recommendation system that includes an artist, a user, and a server. The system provides a function that allows users to view and purchase art in a virtual space on their smartphone. The system includes the following main means:

[0933] 1. A means for artists to register their work in the system

[0934] 2. Artificial intelligence means to learn user preferences and recommend suitable artworks

[0935] 3. An interface for users to search for and purchase artworks

[0936] 4. Database means for storing artwork data registered by artists

[0937] 5. Recommendation means for recommending artworks to a user based on a preference profile generated by artificial intelligence means

[0938] 6. A virtual display means for displaying artworks in a virtual space constructed on a smartphone

[0939] Program processing

[0940] This system is implemented using the following hardware and software:

[0941] Hardware: Smartphone (iOS or Android), server, cloud-based database (e.g., Firebase, Amazon DynamoDB)

[0942] Software and Data Processing:

[0943] Smartphone app: Building a virtual world using Unity and Unreal Engine

[0944] Cloud server: Manages artist artwork data, user preference data, and purchase data using Google Cloud and AWS

[0945] AI engine: Uses TensorFlow and PyTorch to learn user preferences and recommend works

[0946] Detailed Description

[0947] 1. Artist work registration:

[0948] Artists log in to the smartphone app, enter details such as the title, description, genre, and price of their work, and upload image files.

[0949] The server receives artwork data and image files from the artist's device and stores them in a cloud database. At the same time, it uses an image analysis engine to extract features from the image data and provides that data to the AI ​​model.

[0950] 2. Learning user preferences:

[0951] New users install the app and answer a questionnaire to generate an initial preference profile, including their favorite art genres, colors, and themes.

[0952] The server receives preference data from users and supplies it to the AI ​​engine. The AI ​​engine generates and updates preference profiles and stores them in a database. Existing users' past browsing history and "like" information are used to update their profiles.

[0953] 3. Artwork Recommendations:

[0954] When a user opens the "Recommended" section of the app, a recommendation request is sent to your server.

[0955] The server obtains the user's preference profile and uses an AI engine to select relevant works based on that information. The list of recommended works is then sent to the user's smartphone.

[0956] 4. Purchasing artwork:

[0957] When a user finds a title they like, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[0958] The server receives the payment information and processes the transaction. If the transaction is successful, the purchase information is saved in a database and a notification is sent to the artist, who can then begin the process of shipping the artwork.

[0959] Examples and prompts

[0960] Example 1:

[0961] The artist fills out the "Upload a New Work" form, entering the title of the work "Urban Dream," description "Neon-lit Night City," genre "Contemporary Art," and price "$1,000," then selects and uploads an image file.

[0962] The server receives this and stores it in a database. An AI model analyzes the image features and generates a genre and tag.

[0963] Example 2:

[0964] New users fill out a survey and select their favorite genre as "contemporary art," their favorite color as "vivid," and their theme as "urban."

[0965] Based on this, the AI ​​engine recommends suitable works that include "urban dreams."

[0966] Example prompt sentence:

[0967] Example artist submission prompt: "Submit a new artwork. Details are as follows: Title: 'Urban Dreams', Genre: 'Contemporary Art', Price: $1000."

[0968] Example prompt for user recommendations: "Recommend the most suitable artwork based on the user's profile. Favorite genre: 'Contemporary Art', Color: 'Vibrant', Theme: 'Urban'."

[0969] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0970] Step 1:

[0971] Registering your work

[0972] Input: Artists enter and upload detailed information such as the title, description, genre, and price of their work, as well as image files.

[0973] Operation: The artist's device sends the input information to the server, which receives the artwork data and image files.

[0974] Output: The server stores the received data in a database and supplies the image data to the image analysis engine, which extracts features from the image data.

[0975] Step 2:

[0976] Image analysis and tagging

[0977] Input: The image data received by the server in step 1.

[0978] How it works: Image features are extracted using the server's image analysis engine (e.g., TensorFlow) and these features are fed into the AI ​​model.

[0979] Output: The AI ​​model generates appropriate tags based on the image data features and updates the database.

[0980] Step 3:

[0981] User preference profile generation

[0982] Input: Survey responses from new users (favorite genres, colors, themes, etc.) and historical data from existing users.

[0983] How it works: The user device sends this data to the server, and the AI ​​engine generates and updates a preference profile.

[0984] Output: The server stores the generated and updated preference profile in the database.

[0985] Step 4:

[0986] Artwork Recommendations

[0987] Input: User preference profile and artwork data in the database.

[0988] How it works: When a user opens the "Recommended" section, their device sends a recommendation request to the server, where the server's AI engine selects relevant titles based on the user's preference profile.

[0989] Output: The list of recommended artworks is sent to the user's device and displayed.

[0990] Step 5:

[0991] Purchase artwork

[0992] Input: The user selects the title they want to purchase and enters their payment and shipping information.

[0993] How it works: The user device sends a purchase request to the server, and the server processes the payment (e.g., using a payment service such as Stripe or PayPal).

[0994] Output: If the payment is successful, the purchase information is saved in the database, a sale notification is sent to the artist, and information is provided to initiate the shipping process.

[0995] Step 6:

[0996] Displaying artwork in a virtual space

[0997] Input: List of recommended artworks and detailed data (QR codes, image files, etc.).

[0998] How it works: The user's device receives data sent from the server and displays the artwork in a virtual space. Using Unity or Unreal Engine, the user can navigate the virtual space and appreciate the artwork in detail.

[0999] Output: A virtual gallery will be displayed on the user's smartphone screen, allowing the user to freely view the artwork.

[1000] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1001] In one embodiment of the present invention, an artwork recommendation system is described that includes an artist, a user, and a server, and is capable of recommending artworks based on the user's emotional state, particularly by combining an emotion engine that recognizes the user's emotions.

[1002] System configuration

[1003] This system consists of an input means for artists to register their artworks, an artificial intelligence means to learn user preferences, an interface means for users to search for and purchase artworks, a database means to store artwork data, a recommendation means to recommend artworks, and an emotion engine to recognize user emotions.

[1004] Program processing flow

[1005] 1. Artists register their works

[1006] Artist device:

[1007] Artists log in to the system and open the "Upload a New Work" page to register their work. They enter details such as the work title, description, genre, and price, and then select and upload an image file.

[1008] server:

[1009] The server receives artwork data and image files from the artist's device and stores them in a database. It uses an image analysis engine to extract features from the image data and provides that data to an AI model. The AI ​​model then uses these features to classify and tag the artworks in appropriate categories.

[1010] 2. Learning user preferences

[1011] User device:

[1012] When a new user logs in to the system, they are presented with a preference survey in which they input their favorite art genres, colors, themes, etc. Existing users' preference profiles also include their past browsing history and "like" information.

[1013] server:

[1014] The server receives user preference data and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in a database.

[1015] 3. Implementing the Emotion Engine

[1016] User device:

[1017] When users use the system, emotional data is collected in real time through cameras, microphones, text input, and other means.

[1018] server:

[1019] The server inputs the collected emotional data into an emotion engine and performs facial expression analysis, tone of voice analysis, text emotion analysis, and other processes to recognize the user's current emotions.

[1020] 4. Artwork Recommendations

[1021] User device:

[1022] When a user opens the "Recommended" section in the application, a recommendation request is sent to the server.

[1023] server:

[1024] The server acquires the user's preference and emotional profile and selects appropriate artworks based on that information. The AI ​​engine takes into account the user's real-time emotional data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[1025] 5. Buying artwork

[1026] User device:

[1027] When a user finds a title they like, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[1028] server:

[1029] The server receives the payment information and processes the secure payment. If the payment is successful, the purchase information is saved in a database and a sales notification is sent to the artist, who can then initiate the shipping process for the artwork.

[1030] Specific examples

[1031] Example 1: Artist's work registration

[1032] Artist device:

[1033] In the "Upload New Work" form, the artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape Painting," and the price "$500," selects the image file, and clicks the upload button.

[1034] server:

[1035] The server receives the artwork data and image files and stores them in a database. The image analysis engine extracts features from the image data and provides them to the AI ​​model. The AI ​​model generates the genre "landscape painting" and tags "city, night view, neon" and updates the database.

[1036] Example 2: User recommendations

[1037] User device:

[1038] New users are asked to fill out a questionnaire and select their favorite genre: landscape painting, favorite color: colorful, and theme: urban. During use, the system also collects real-time emotional data from the camera and microphone.

[1039] server:

[1040] The server receives the user's preference data and real-time emotion data and supplies it to the AI ​​engine, which then generates and updates the user's preference profile and emotion profile and stores them in a database.

[1041] When a user opens the "Recommended Works" section, the AI ​​engine generates a list of appropriate works, including "urban night views," and sends it to the user's device.

[1042] In this way, the present invention allows users to easily find artworks that match their tastes and emotional state, and allows artists to effectively sell their works.

[1043] The processing flow will be explained below.

[1044] Artist registration

[1045] Step 1:

[1046] Artist terminal: The artist logs in to the system.

[1047] Step 2:

[1048] Artist device: The artist opens the "Upload New Work" page.

[1049] Step 3:

[1050] Artist device: Enter detailed information such as the work title, description, genre, and price, and select an image file.

[1051] Step 4:

[1052] Artist device: Click the upload button.

[1053] Step 5:

[1054] Server: Receives artwork data and image files from the artist's device.

[1055] Step 6:

[1056] Server: Save the image file in a temporary directory.

[1057] Step 7:

[1058] Server: Stores work information in a database.

[1059] Step 8:

[1060] Server: Generate a work ID, rename the image file with the work ID, and save it.

[1061] Step 9:

[1062] Server: Uses an image analysis engine to extract features from image files (color, composition, theme, etc.).

[1063] Step 10:

[1064] Server: Inputs feature data into the AI ​​model to generate genre and style tags.

[1065] Step 11:

[1066] Server: Adds and updates the generated tags and features to the artwork information stored in the database.

[1067] Learning user preferences

[1068] Step 1:

[1069] User terminal: A new user logs into the system.

[1070] Step 2:

[1071] User device: The initial setup wizard displays a preference survey.

[1072] Step 3:

[1073] User device: The user inputs their favorite art genre, color, theme, etc.

[1074] Step 4:

[1075] Server: Receives survey results and historical data from user devices.

[1076] Step 5:

[1077] Server: Inputs this data into the AI ​​engine to generate a user preference profile.

[1078] Step 6:

[1079] Server: Saves and updates user preference profiles in a database.

[1080] Implementing the Emotion Engine

[1081] Step 1:

[1082] User device: Emotional data is collected in real time from the camera and microphone while the user is using the system.

[1083] Step 2:

[1084] Server: Inputs the collected emotion data into the emotion engine.

[1085] Step 3:

[1086] Server: The emotion engine performs facial expression analysis, tone of voice analysis, and text emotion analysis.

[1087] Step 4:

[1088] Server: Generates real-time user emotion data and stores / updates it in the database.

[1089] Artwork recommendations

[1090] Step 1:

[1091] User device: A logged-in user clicks on the "Featured Art" section.

[1092] Step 2:

[1093] Server: Receives the recommendation request.

[1094] Step 3:

[1095] Server: Reads the user's preference profile and emotion profile from the database.

[1096] Step 4:

[1097] Server: Input the entire art work database into the AI ​​engine and generate a list of the most suitable artworks based on the user's preferences and real-time emotional data.

[1098] Step 5:

[1099] Server: Sends the recommendation results to the user device.

[1100] Purchase artwork

[1101] Step 1:

[1102] User device: The user clicks on the work they like and presses the "Purchase" button.

[1103] Step 2:

[1104] User device: Enter payment and shipping information into the purchase form.

[1105] Step 3:

[1106] User device: Press the checkout button to send a purchase request.

[1107] Step 4:

[1108] Server: Receives the purchase request.

[1109] Step 5:

[1110] Server: Sends payment information to a payment gateway for secure processing.

[1111] Step 6:

[1112] Server: If the payment is successful, save the purchase information and transaction ID to the database.

[1113] Step 7:

[1114] Server: Sends a sales notification to the artist and instructs them to begin the shipping process.

[1115] The above are the specific processing steps based on the invention that combines emotion engines.

[1116] Example 2

[1117] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1118] Conventional art recommendation systems can recommend artworks based on user preference data, but they cannot take into account the user's current emotional state. As a result, they are unable to provide artworks that match the user's real-time emotions, making it difficult to improve user satisfaction.

[1119] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for artists to register their works in the system, an artificial intelligence means for learning the user's preferences and recommending artworks suitable for the user, and an emotion engine means for recognizing the user's emotions. This makes it possible to recommend artworks that match not only the user's preferences but also their real-time emotional state.

[1120] "Input means" is an interface that allows artists to register information about their works in the system.

[1121] "Artificial intelligence means" refers to a function within the system that learns user preference data and recommends suitable artworks to users based on that data.

[1122] "Interface means" refers to a user interface that allows a user to search for and purchase artworks.

[1123] "Database means" refers to data storage within the system for storing work data registered by artists, user preference data, and emotional data.

[1124] "Recommendation Means" means functionality within the System for recommending artworks to Users based on their preference profiles generated by the Artificial Intelligence Means.

[1125] The "emotion engine means" is a function within the system for recognizing a user's emotions and generating an emotion profile based thereon.

[1126] An "emotion profile" is data representing the user's emotional state as recognized by the emotion engine means.

[1127] In one embodiment of the present invention, an artwork recommendation system including an artist, a user, and a server is described. In particular, the system is combined with an emotion engine that recognizes the user's emotions, enabling the recommendation of artworks based on the user's emotional state. The configuration of the system is described in detail below.

[1128] System configuration

[1129] This system consists of an input means for artists to register their artworks, an artificial intelligence means to learn user preferences, an interface means for users to search for and purchase artworks, a database means to store artwork data, a recommendation means to recommend artworks, and an emotion engine to recognize user emotions.

[1130] Explanation of program processing

[1131] Artists register their works

[1132] Artist device:

[1133] The artist logs in to the system and opens the "Upload New Work" page, where they enter details such as the work title, description, genre, and price, and then select and upload an image file.

[1134] server:

[1135] The server stores artwork data and image files received from the artist's device. It uses an image analysis engine to extract features from the image data and provides that data to the AI ​​model. The AI ​​model then uses these features to classify and tag the artworks in appropriate categories.

[1136] Examples:

[1137] The artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape painting," and the price "$500," and uploads the image file.

[1138] Learning user preferences

[1139] User device:

[1140] When a new user logs into the system, they are presented with a preference questionnaire in which they are asked to enter their favorite art genres, colors, themes, etc.

[1141] server:

[1142] The server receives preference data from the user device and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in a database.

[1143] Examples:

[1144] New users fill out the survey with "Favorite genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[1145] Implementing the Emotion Engine

[1146] User device:

[1147] When users use the system, emotional data is collected in real time through cameras, microphones, text input, and other means.

[1148] server:

[1149] The server inputs the collected emotional data into an emotion engine and performs facial expression analysis, tone of voice analysis, text emotion analysis, and other processes to recognize the user's current emotions.

[1150] Examples:

[1151] While the user is using the application, the camera captures the user's facial expressions and the emotion engine analyzes the data to identify emotions such as "happiness" or "sadness."

[1152] Artwork recommendations

[1153] User device:

[1154] When a user opens the "Recommended for You" section, a recommendation request is sent to the server.

[1155] server:

[1156] The server acquires the user's preference and emotional profile and selects appropriate artworks based on that information. The AI ​​engine takes into account the user's real-time emotional data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[1157] Examples:

[1158] The server generates a list of recommended works, including "urban night views," based on the user's preference data and real-time emotional data, and presents it to the user.

[1159] Purchase artwork

[1160] User device:

[1161] When a user finds a title they wish to purchase, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[1162] server:

[1163] The server securely processes the payment information received, records the successful transaction in a database, and sends a sales notification to the artist.

[1164] Examples:

[1165] To purchase "City Night View," the user enters payment and shipping information, which is then securely processed by the server, who then notifies the artist of the sale.

[1166] Examples of prompt statements

[1167] "Please log in."

[1168] Enter details about your work and upload an image file.

[1169] "Choose your favorite art genre, color, and theme."

[1170] "Allow use of your camera and microphone to begin collecting emotional data."

[1171] "Generating a list of recommended titles. Please wait a moment."

[1172] The system allows users to easily access artworks that best suit their tastes and emotions, and allows artists to efficiently sell their work.

[1173] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1174] Program processing flow

[1175] Artists register their works

[1176] Step 1:

[1177] Artist device:

[1178] The artist logs into the system using their own account, which gives them access to their own artist page within the system.

[1179] input:

[1180] Artist login information (username, password).

[1181] output:

[1182] The artist's authentication token.

[1183] Specific behavior:

[1184] The artist enters their username and password and clicks the login button.

[1185] Step 2:

[1186] Artist device:

[1187] Artists open the "Upload New Work" page, enter details such as the work's title, description, genre, and price, and select and upload an image file.

[1188] input:

[1189] Title, description, genre, price, and image file of the work.

[1190] output:

[1191] Request to upload new work.

[1192] Specific behavior:

[1193] The artist enters the title "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape," and the price "$500," selects the image file, and clicks the upload button.

[1194] Step 3:

[1195] server:

[1196] The server stores the artwork data and image files received from the artist's device, then uses an image analysis engine to extract features from the image data and feeds that data to the AI ​​model.

[1197] input:

[1198] Artwork data and image files received from the artist's device.

[1199] output:

[1200] Image data features and category classification results.

[1201] Specific behavior:

[1202] The server stores the artwork data in a database, uses an image analysis engine to extract features, and supplies them to an AI model to generate categories and tags.

[1203] Learning user preferences

[1204] Step 1:

[1205] User device:

[1206] When a new user logs into the system, they are presented with a preference questionnaire, which asks about their favorite art genres, colors, themes, etc.

[1207] input:

[1208] User preference information (art genre, color, theme).

[1209] output:

[1210] Request to send preference survey.

[1211] Specific behavior:

[1212] A new user fills out the survey with "Favorite genre: landscape painting," "Favorite color: colorful," and "Theme: urban," and clicks the submit button.

[1213] Step 2:

[1214] server:

[1215] The server receives the preference data sent from the user device and supplies it to the AI ​​engine, which then generates a user preference profile and stores it in a database.

[1216] input:

[1217] Preference data sent from user devices.

[1218] output:

[1219] User preference profile.

[1220] Specific behavior:

[1221] The server sends the preference data to the AI ​​engine and stores the generated preference profile in a database.

[1222] Implementing the Emotion Engine

[1223] Step 1:

[1224] User device:

[1225] When users use the system, emotional data is collected in real time through cameras, microphones, text input, and other means.

[1226] input:

[1227] User facial expression, voice, and text data.

[1228] output:

[1229] Request to send real-time sentiment data.

[1230] Specific behavior:

[1231] The user authorizes the camera and microphone, and the system collects emotional data from facial expressions and voice.

[1232] Step 2:

[1233] server:

[1234] The server inputs the collected emotional data into an emotion engine and performs facial expression analysis, tone of voice analysis, text emotion analysis, and other processes to recognize the user's current emotions.

[1235] input:

[1236] Collected user sentiment data.

[1237] output:

[1238] User emotional profile.

[1239] Specific behavior:

[1240] The server analyzes the emotional data and stores emotions such as "joy" and "sadness" as an emotional profile.

[1241] Artwork recommendations

[1242] Step 1:

[1243] User device:

[1244] When a user opens the "Recommended for You" section, a recommendation request is sent to the server.

[1245] input:

[1246] Request for the creation of a "Recommended Works" section.

[1247] output:

[1248] Submit a recommendation request.

[1249] Specific behavior:

[1250] The user clicks the "Recommended Works" button.

[1251] Step 2:

[1252] server:

[1253] The server acquires the user's preference and emotional profile and selects appropriate artworks based on that information. The AI ​​engine takes into account the user's real-time emotional data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[1254] input:

[1255] User preference and emotional profiles.

[1256] output:

[1257] A list of recommended artworks.

[1258] Specific behavior:

[1259] The server provides the preference profile and emotional profile to the AI ​​engine and sends the generated list of works to the user.

[1260] Purchase artwork

[1261] Step 1:

[1262] User device:

[1263] When a user finds a title they wish to purchase, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[1264] input:

[1265] Payment and shipping information.

[1266] output:

[1267] Submit a purchase request.

[1268] Specific behavior:

[1269] Users select the work they like, press the "Purchase" button and enter their details.

[1270] Step 2:

[1271] server:

[1272] The server securely processes the payment information received, records the successful transaction in a database, and sends a sales notification to the artist.

[1273] input:

[1274] Payment and shipping information.

[1275] output:

[1276] Payment results and sales notification.

[1277] Specific behavior:

[1278] The server processes the payment information and, once the payment is successful, sends a sales notification to the artist, prompting them to begin the shipping process for the artwork.

[1279] (Application example 2)

[1280] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1281] The online art sales market has expanded in recent years, but a key issue is whether users can easily find artworks that suit their emotional state. Conventional systems primarily base recommendations on users' preference data and browsing history, making it difficult to respond to real-time emotional changes. This has led to problems such as users finding artworks that fit their emotional state at any given time, resulting in a decrease in satisfaction.

[1282] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1283] In this invention, the server includes an input means for artists to register their artworks in the system, an artificial intelligence means for learning user preferences and recommending artworks suitable for the user, and an interface means for the user to search for and purchase artworks. This makes it possible to recognize the user's emotional state in real time and recommend artworks using emotional data.

[1284] "Input means" refers to the means by which artists register their artworks in the system.

[1285] "Artificial intelligence means" refers to means for learning a user's preferences and, based on the results, recommending artworks suitable for the user.

[1286] "Interface means" refers to the means by which a user searches for and purchases artworks.

[1287] "Database means" refers to a means for storing work data registered by artists.

[1288] "Recommendation means" means means for recommending artworks to a user based on a preference profile generated by an artificial intelligence means.

[1289] An "emotion engine" is a means for recognizing a user's emotional state in real time.

[1290] An "emotion-based recommendation method" is a method for recommending artworks using emotion data recognized by an emotion engine.

[1291] The present invention relates to a system for recommending artworks based on a user's emotional state, and is configured as follows.

[1292] System configuration

[1293] The system consists of the following main components:

[1294] 1. Input method: This is the method by which artists register their works in the system. Artists input the title, description, image data, etc. of their works and save them in the database.

[1295] 2. Artificial intelligence means: This means learns user preferences and recommends artworks based on that data. It also analyzes image data of artworks, extracts features, and stores them in a database.

[1296] 3. Interface: A means for users to search for and purchase artworks. Users can interact with the system using this interface.

[1297] 4. Database means: A means for storing artwork data registered by artists. The database stores detailed information about artworks, image data, and user preference profiles.

[1298] 5. Recommendation: This is a method for recommending artworks to users based on their preference profile generated by artificial intelligence. It also takes into account the user's history and preference data to generate appropriate recommendation results.

[1299] 6. Emotion Engine: A means of recognizing the user's emotional state in real time. It uses cameras and microphones to collect emotional data and analyzes it in real time.

[1300] 7. Emotion-based recommendation method: This method uses emotional data recognized by an emotion engine to recommend artworks to users.

[1301] System Operation

[1302] The server uses these means to perform the following processing.

[1303] 1. Artist registration

[1304] The server stores the artwork data (title, description, image data, etc.) received from the artist in a database. The image analysis engine analyzes the received image data and extracts features.

[1305] 2. Learning user preferences

[1306] The server analyzes the preference data entered by new users and the browsing history of existing users, and generates and updates preference profiles using an AI engine.

[1307] 3. Emotional Data Collection and Recognition

[1308] The user device collects emotion data in real time via a camera and microphone while the user is using the system, and the collected data is sent to a server where it is analyzed by an emotion engine.

[1309] 4. Artwork Recommendations

[1310] The server selects the most suitable artworks based on the user's preference and emotional profiles and recommends them to the user through recommendation methods.

[1311] 5. Buying artwork

[1312] On the user's device, the user clicks the purchase button and enters the necessary payment information. The server then processes the payment securely and stores the purchase information in a database.

[1313] Hardware and software used

[1314] Hardware: Camera, microphone

[1315] Software: OpenCV, EmotionRecognizer, AIEngine, ArtDatabase

[1316] Examples of specific examples and prompts

[1317] Example 1: When a user feels "tired"

[1318] Prompt: "What artwork helps you relax? Do you prefer tranquil landscapes or artwork with muted colors?"

[1319] Server operation: Captures the user's facial expression with a camera, determines their level of fatigue, and recommends relaxing artwork.

[1320] Example 2: When a user feels happy

[1321] Prompt: "What bright artwork would fit your current happy mood? Would you prefer something colorful and positive?"

[1322] Server operation: The camera captures the user's facial expression, determines whether they are "happy," and recommends bright and colorful artwork.

[1323] Thus, the present invention provides a system that allows users to easily find and purchase artwork that matches their current emotional state.

[1324] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1325] Step 1:

[1326] Artist registration

[1327] Input: Artist enters work title, description, genre, image file, etc.

[1328] Server operation: Receives input artwork data and stores it in a database. It also uses an image analysis engine to extract features from the image data and provides that data to an AI model. Based on the features, the AI ​​model classifies and tags the artwork into appropriate categories.

[1329] Output: The artwork data and its features are stored in a database, and the artworks are classified by category and tag.

[1330] Step 2:

[1331] Learning user preferences

[1332] Input: Response data from preference surveys from new users, past browsing history and likes from existing users, etc.

[1333] Server operation: Receives preference data and provides it to the AI ​​engine, which then generates and updates the user's preference profile and stores it in the database.

[1334] Output: Preference profile for each user stored in a database.

[1335] Step 3:

[1336] Emotion data collection and recognition

[1337] Input: Real-time emotion data collected via the user's device camera, microphone, and text input.

[1338] Server operation: The collected emotional data is input into the emotion engine, and the user's emotional state is recognized by performing facial expression analysis, tone of voice analysis, text emotion analysis, etc.

[1339] Output: Real-time emotional state data of the user.

[1340] Step 4:

[1341] Artwork recommendations

[1342] Input: User preference profile, real-time sentiment data, user request for recommendations.

[1343] Server operation: Based on this data, the AI ​​engine selects and recommends the most relevant artworks. A generative AI model is used to generate emotion-based prompts and recommend artworks based on them.

[1344] Output: A list of recommended artworks for the user.

[1345] Step 5:

[1346] Purchase artwork

[1347] Input: Artwork selected by the user, payment information, and shipping information.

[1348] User device actions: clicking the purchase button and entering required information.

[1349] Server Action: Receives payment information and processes the transaction. If successful, stores the purchase in a database and sends a sales notification to the artist.

[1350] Output: A confirmation message of the completed purchase and a sale notification to the artist.

[1351] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1352] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1353] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1354] [Third embodiment]

[1355] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1356] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1357] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1358] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1359] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1360] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1361] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1362] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1363] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1364] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1365] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1366] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1367] In one embodiment of the present invention, an artwork recommendation system is described that includes an artist, a user, and a server.

[1368] System configuration

[1369] This system consists of an input means for artists to register their artworks, an artificial intelligence means to learn user preferences, an interface means for users to search for and purchase artworks, a database means to store artwork data, and a recommendation means to recommend artworks.

[1370] Program processing flow

[1371] 1. Artists register their works

[1372] Artist device:

[1373] To register their work, artists first log in to the application, then enter details such as the work's title, description, genre, and price, and upload an image file.

[1374] server:

[1375] The server receives artwork data and image files from the artist's device and stores them in a database. At the same time, it uses an image analysis engine to extract features from the image data and provides this data to an AI model. The AI ​​model then uses these features to classify and tag the artworks into appropriate categories.

[1376] 2. Learning user preferences

[1377] User device:

[1378] When a new user logs in to the application, they are presented with a preference survey. They enter their favorite art genres, colors, themes, etc., and an initial preference profile is generated based on this. For existing users, past browsing history and "like" information is also included.

[1379] server:

[1380] The server receives user preference data and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in a database.

[1381] 3. Artwork Recommendations

[1382] User device:

[1383] When a user opens the "Recommended" section in the application, a recommendation request is sent to the server.

[1384] server:

[1385] The server acquires the user's preference profile and selects appropriate artworks based on that information. At this time, the AI ​​engine takes into account the user's historical data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[1386] 4. Buying artwork

[1387] User device:

[1388] When a user finds a title they like, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[1389] server:

[1390] The server receives the payment information and processes the secure payment. If the payment is successful, the purchase information is saved in a database and a sales notification is sent to the artist, who can then initiate the shipping process for the artwork.

[1391] Specific examples

[1392] Example 1: Artist's work registration

[1393] Artist device:

[1394] In the "Upload New Work" form, the artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape Painting," and the price "$500," selects the image file, and clicks the upload button.

[1395] server:

[1396] The server receives the artwork data and image files and stores them in a database. The image analysis engine extracts features from the image data and provides them to the AI ​​model. The AI ​​model generates the genre "landscape painting" and tags "city, night view, neon" and updates the database.

[1397] Example 2: User recommendations

[1398] User device:

[1399] New users will be asked to fill out a survey and select "Favorite genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[1400] server:

[1401] The server receives the user's preference data and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in the database.

[1402] When a user opens the "Recommended Works" section, the AI ​​engine generates a list of appropriate works, including "urban night views," and sends it to the user's device.

[1403] As a result, the present invention allows art lovers and beginners to easily find artworks that suit their tastes, and allows artists to effectively sell their works.

[1404] The processing flow will be explained below.

[1405] Artist registration

[1406] Step 1:

[1407] Artist terminal: The artist logs in to the system.

[1408] Step 2:

[1409] Artist device: The artist opens the "Upload New Work" page.

[1410] Step 3:

[1411] Artist device: Enter detailed information such as the work title, description, genre, and price, and select an image file.

[1412] Step 4:

[1413] Artist device: Click the upload button.

[1414] Step 5:

[1415] Server: Receives artwork data and image files from the artist's device.

[1416] Step 6:

[1417] Server: Save the image file in a temporary directory.

[1418] Step 7:

[1419] Server: Stores work information in a database.

[1420] Step 8:

[1421] Server: Generate a work ID, rename the image file with the work ID, and save it.

[1422] Step 9:

[1423] Server: Uses an image analysis engine to extract features from image files (color, composition, theme, etc.).

[1424] Step 10:

[1425] Server: Inputs feature data into the AI ​​model to generate genre and style tags.

[1426] Step 11:

[1427] Server: Adds and updates the generated tags and features to the artwork information stored in the database.

[1428] Learning user preferences

[1429] Step 1:

[1430] User terminal: A new user logs into the system.

[1431] Step 2:

[1432] User device: The initial setup wizard displays a preference survey.

[1433] Step 3:

[1434] User device: The user inputs their favorite art genre, color, theme, etc.

[1435] Step 4:

[1436] Server: Receives survey results and historical data from user devices.

[1437] Step 5:

[1438] Server: Inputs this data into the AI ​​engine to generate a user preference profile.

[1439] Step 6:

[1440] Server: Saves and updates user preference profiles in a database.

[1441] Artwork recommendations

[1442] Step 1:

[1443] User device: A logged-in user clicks on the "Featured Art" section.

[1444] Step 2:

[1445] Server: Receives the recommendation request.

[1446] Step 3:

[1447] Server: Reads the user's preference profile from the database.

[1448] Step 4:

[1449] Server: Inputs the entire art work database into the AI ​​engine and generates a list of works that best match the user's preferences.

[1450] Step 5:

[1451] Server: Sends the recommendation results to the user device.

[1452] Purchase artwork

[1453] Step 1:

[1454] User device: The user clicks on the work they like and presses the "Purchase" button.

[1455] Step 2:

[1456] User device: Enter payment and shipping information into the purchase form.

[1457] Step 3:

[1458] User device: Press the checkout button to send a purchase request.

[1459] Step 4:

[1460] Server: Receives the purchase request.

[1461] Step 5:

[1462] Server: Sends payment information to a payment gateway for secure processing.

[1463] Step 6:

[1464] Server: If the payment is successful, save the purchase information and transaction ID to the database.

[1465] Step 7:

[1466] Server: Sends a sales notification to the artist and instructs them to begin the shipping process.

[1467] The above are the specific processing steps for carrying out the present invention.

[1468] Example 1

[1469] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1470] Conventional art recommendation systems make it difficult for users to find artworks that match their tastes. Furthermore, they lacked efficient management of artist-registered artwork data and accurate feature extraction, resulting in inappropriate tagging and categorization. Furthermore, they lacked a consistent interface to facilitate a smooth user purchasing experience. These issues need to be addressed.

[1471] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1472] In this invention, the server includes image analysis means for extracting features from image data entered by artists and generating appropriate categories and tags, artificial intelligence means for learning user preferences and generating recommendation results, and database means for storing artwork data. This makes it possible to provide a system that efficiently recommends artworks that match users' preferences and makes it easy for artists to manage and sell their artworks.

[1473] Understood. Create the definition below.

[1474] An "artist" is a person or entity that registers their artwork in the system.

[1475] "User" means any person or entity that uses the System to search for and purchase artworks.

[1476] "Input means" refers to a device or application that allows an artist to input detailed information about their work into the system.

[1477] "Artificial intelligence means" are algorithms or software that learn a user's preferences and recommend suitable artworks.

[1478] An "interface means" is a device or application that provides a user interface for users to search for and purchase artworks.

[1479] "Database means" refers to a system or device for storing work data registered by artists and user preference profiles.

[1480] "Recommendation Means" means a device or algorithm for recommending artworks to a User based on a preference profile generated by an Artificial Intelligence Means.

[1481] "Image analysis means" refers to devices or software that extract the features of image data entered by the artist and generate appropriate categories and tags.

[1482] "Features" are analytical data such as color, shape, and texture of image data extracted by image analysis means.

[1483] In one embodiment of the present invention, an artwork recommendation system is described in which artists, users, and a server work in cooperation.

[1484] System configuration

[1485] The system consists of the following main tools:

[1486] 1. A means for artists to register their work in the system

[1487] 2. Artificial intelligence means to learn user preferences

[1488] 3. An interface for users to search for and purchase artworks

[1489] 4. Database means for storing work data

[1490] 5. Recommended Ways to Recommend

[1491] 6. Image analysis method to extract features from image data and generate categories and tags

[1492] Program processing flow

[1493] Artists register their works

[1494] Artist device:

[1495] The artist logs in to the application. After logging in, they click the "Register a new work" button to open the registration form for a new work. There, they enter the work title "Urban night view", description "Neon-lit cityscape", genre "Landscape painting", and price "$500", and upload the image file of the work. Once this is complete, they click the "Submit" button.

[1496] server:

[1497] The server receives the artwork data and image files sent by the artist and stores them in a database. At the same time, it launches an image analysis engine (e.g., OpenCV) to extract features from the received image data. The extracted features are then fed into an AI model (e.g., TensorFlow), which then generates appropriate artwork categories and tags.

[1498] Learning user preferences

[1499] User device:

[1500] When a new user logs in to the application, a preference survey is displayed, in which the user enters information such as "Favorite art genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[1501] server:

[1502] The server provides the user's preference data to an AI engine (e.g., TensorFlow). The AI ​​engine then generates a user preference profile based on the data and stores it in a database. In addition, existing users can update their profiles by including their browsing history, click history, purchase history, and likes.

[1503] Artwork recommendations

[1504] User device:

[1505] When a user selects the "Recommended Works" section in the application, a recommendation request is sent to the server.

[1506] server:

[1507] The server obtains the user's preference profile and provides it to a recommendation engine. The recommendation engine (e.g., an AI algorithm using KNN or collaborative filtering) then selects the most suitable artworks based on this. If the user likes landscape paintings and colorful urban themes, appropriate artworks such as "urban night view" will be selected. The selected recommendation list is then sent to the user's device.

[1508] Purchase artwork

[1509] User device:

[1510] The user opens the details page of the work they are interested in and clicks the "Purchase" button. They then enter their payment information and shipping address and click the "Confirm Purchase" button.

[1511] server:

[1512] The server receives the purchase request from the user and processes the payment securely (e.g., Stripe or PayPal). If the purchase is successful, the purchase information is saved in a database and a notification is sent to the artist, who can then initiate the shipping process for the artwork.

[1513] Specific examples

[1514] Example 1: Artist's work registration

[1515] Artist device:

[1516] The artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape painting," and the price "$500," and uploads the image file.

[1517] server:

[1518] The server receives the artwork data and image files and stores them in a database. It uses an image analysis engine (e.g., OpenCV) to extract features from the image data and provides them to an AI model (e.g., TensorFlow). The AI ​​model generates the genre "landscape painting" and tags "city, night view, neon" based on the features and updates the database.

[1519] Example 2: User recommendations

[1520] User device:

[1521] New users will be asked to fill out a survey and choose their favorite genre: landscape painting, favorite color scheme: colorful, and theme: urban.

[1522] server:

[1523] The server receives the user's preference data and supplies it to an AI engine (e.g., TensorFlow). The AI ​​engine generates a user preference profile and stores it in a database. When the user opens the "Recommended Works" section, the AI ​​engine generates a list of appropriate works, including "City Night Views," and sends it to the user's device.

[1524] Prompt Sentence Examples

[1525] "What kind of artwork do I like? I like landscapes, and I like colorful, urban-themed artwork."

[1526] The above is a specific embodiment of the artwork recommendation system according to the present invention, which allows users to efficiently discover artworks that match their tastes and allows artists to effectively sell their works.

[1527] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1528] Step 1:

[1529] Artists register their works

[1530] Artist device:

[1531] The artist logs in to the application. After logging in, they click the "Register a new work" button to open the registration form for a new work. There, they enter the work title "Urban night view", description "Neon-lit cityscape", genre "Landscape painting", and price "$500", and upload the image file of the work. Once complete, they click the "Submit" button.

[1532] Input: Title, description, genre, price, image file

[1533] Output: A request to send the work to the server

[1534] Step 2:

[1535] Receiving and storing artwork data and image files on the server

[1536] server:

[1537] The server receives the artwork data and image files sent by the artist and stores them in a database.

[1538] Input: Artwork data and image files from the artist

[1539] Output: Artwork data stored in a database and image files

[1540] Step 3:

[1541] Image data feature extraction and tag generation

[1542] server:

[1543] The server launches an image analysis engine (e.g., OpenCV) and extracts features (color, shape, texture, etc.) from the received image data. The extracted features are fed into an AI model (e.g., TensorFlow), which then generates appropriate categories and tags for the work. The results are then stored in a database.

[1544] Input: Image file

[1545] Data processing and data calculation: Feature extraction through image analysis, category and tag generation through AI models

[1546] Output: Features, categories, tags, updated database

[1547] Step 4:

[1548] Entering user preference data and creating profiles

[1549] User device:

[1550] When a new user logs in to the application, a preference survey is displayed, in which the user enters information such as "Favorite art genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[1551] Input: User preference data

[1552] Output: Preference data request to server

[1553] Step 5:

[1554] Server-generated and stored preference profiles

[1555] server:

[1556] The server provides the preference data acquired from the user to an AI engine (e.g., TensorFlow). The AI ​​engine generates a user preference profile based on this data and stores this profile in a database. In addition, existing users can update their profile by including their past browsing history, click history, purchase history, and likes.

[1557] Input: User preference data

[1558] Data processing and data calculation: AI engine generates preference profiles

[1559] Output: Preference profile, updated database

[1560] Step 6:

[1561] User submits recommendation request

[1562] User device:

[1563] When a user selects the "Recommended Works" section in the application, a recommendation request is sent to the server.

[1564] Input: Recommendation request

[1565] Output: Recommendation request sent to server

[1566] Step 7:

[1567] Recommendation generation and sending

[1568] server:

[1569] The server obtains the user's preference profile and provides it to the recommendation engine. The recommendation engine (e.g., an AI algorithm using KNN or collaborative filtering) selects the most suitable artwork based on the user's preference profile and historical data. For example, if the user prefers "landscapes, colorful, urban," "urban night views" will be selected. The selected recommendation list is sent to the user's device.

[1570] Input: Preference profile, historical data

[1571] Data processing and data calculation: Selection of works by recommendation engine

[1572] Output: Generate recommendation list and send it to user device

[1573] Step 8:

[1574] Users submitting purchase requests for works

[1575] User device:

[1576] The user opens the details page of the work they are interested in and clicks the "Purchase" button. They enter their payment information and shipping address and click the "Confirm Purchase" button.

[1577] Input: Purchase request, payment information, shipping information

[1578] Output: Purchase request sent to server

[1579] Step 9:

[1580] Server processes payments and stores purchase information

[1581] server:

[1582] The server receives the purchase request from the user and processes the payment securely (e.g., Stripe or PayPal). If the purchase is successful, the purchase information is saved in a database and a notification is sent to the artist, who can then initiate the shipping process for the artwork.

[1583] Input: Purchase request, payment information, shipping information

[1584] Data processing and calculation: payment processing, storing purchase information

[1585] Output: Purchase confirmation, notification sent, updated database

[1586] (Application example 1)

[1587] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1588] Conventional art recommendation systems have made it difficult for users to visualize artworks. They also require users to visit physical galleries, which is time-consuming and laborious. Furthermore, they can sometimes be less accurate in recommending appropriate artworks based on a user's preferences, which can discourage purchases. There is a need for new methods to resolve these issues and more effectively provide artworks to users.

[1589] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1590] In this invention, the server includes input means for artists to register their works in the system, artificial intelligence means for learning user preferences and recommending artworks that suit the user, interface means for users to search for and purchase artworks, and virtual display means for displaying artworks in a virtual space constructed on a smartphone. This allows users to easily appreciate artworks in the virtual space through their smartphones and receive highly accurate recommendations of artworks that suit their preferences, enabling them to make efficient purchases.

[1591] "Input means" refers to the means by which artists input the information necessary to register their artworks in the system. Specifically, it includes a form in which detailed information such as the title, description, genre, price, and image file of the artwork can be input.

[1592] An "artificial intelligence means" is a means equipped with machine learning algorithms that learn a user's preferences and, based on that data, recommend the most suitable artworks to the user.

[1593] "Interface means" means means including a user interface for users to search for and purchase artworks, specifically providing a GUI (Graphical User Interface) for browsing, filtering, searching, and purchasing artworks.

[1594] "Database means" refers to data storage means for storing work data registered by artists and user preference data. Specifically, it includes a cloud-based database system.

[1595] "Recommendation Means" means means for recommending artworks to users based on a preference profile generated by artificial intelligence means, and specifically includes a recommendation algorithm based on an AI engine.

[1596] "Virtual display means" refers to a means for displaying artworks in a virtual space created on a smartphone. Specifically, a virtual exhibition space is created using game engines such as Unity or Unreal Engine, allowing users to visually appreciate artworks in the virtual space.

[1597] System Overview

[1598] The present invention relates to an art recommendation system that includes an artist, a user, and a server. The system provides a function that allows users to view and purchase art in a virtual space on their smartphone. The system includes the following main means:

[1599] 1. A means for artists to register their work in the system

[1600] 2. Artificial intelligence means to learn user preferences and recommend suitable artworks

[1601] 3. An interface for users to search for and purchase artworks

[1602] 4. Database means for storing artwork data registered by artists

[1603] 5. Recommendation means for recommending artworks to a user based on a preference profile generated by artificial intelligence means

[1604] 6. A virtual display means for displaying artworks in a virtual space constructed on a smartphone

[1605] Program processing

[1606] This system is implemented using the following hardware and software:

[1607] Hardware: Smartphone (iOS or Android), server, cloud-based database (e.g., Firebase, Amazon DynamoDB)

[1608] Software and Data Processing:

[1609] Smartphone app: Building a virtual world using Unity and Unreal Engine

[1610] Cloud server: Manages artist artwork data, user preference data, and purchase data using Google Cloud and AWS

[1611] AI engine: Uses TensorFlow and PyTorch to learn user preferences and recommend works

[1612] Detailed Description

[1613] 1. Artist work registration:

[1614] Artists log in to the smartphone app, enter details such as the title, description, genre, and price of their work, and upload image files.

[1615] The server receives artwork data and image files from the artist's device and stores them in a cloud database. At the same time, it uses an image analysis engine to extract features from the image data and provides that data to the AI ​​model.

[1616] 2. Learning user preferences:

[1617] New users install the app and answer a questionnaire to generate an initial preference profile, including their favorite art genres, colors, and themes.

[1618] The server receives preference data from users and supplies it to the AI ​​engine. The AI ​​engine generates and updates preference profiles and stores them in a database. Existing users' past browsing history and "like" information are used to update their profiles.

[1619] 3. Artwork Recommendations:

[1620] When a user opens the "Recommended" section of the app, a recommendation request is sent to your server.

[1621] The server obtains the user's preference profile and uses an AI engine to select relevant works based on that information. The list of recommended works is then sent to the user's smartphone.

[1622] 4. Purchasing artwork:

[1623] When a user finds a title they like, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[1624] The server receives the payment information and processes the transaction. If the transaction is successful, the purchase information is saved in a database and a notification is sent to the artist, who can then begin the process of shipping the artwork.

[1625] Examples and prompts

[1626] Example 1:

[1627] The artist fills out the "Upload a New Work" form, entering the title of the work "Urban Dream," description "Neon-lit Night City," genre "Contemporary Art," and price "$1,000," then selects and uploads an image file.

[1628] The server receives this and stores it in a database. An AI model analyzes the image features and generates a genre and tag.

[1629] Example 2:

[1630] New users fill out a survey and select their favorite genre as "contemporary art," their favorite color as "vivid," and their theme as "urban."

[1631] Based on this, the AI ​​engine recommends suitable works that include "urban dreams."

[1632] Example prompt sentence:

[1633] Example artist submission prompt: "Submit a new artwork. Details are as follows: Title: 'Urban Dreams', Genre: 'Contemporary Art', Price: $1000."

[1634] Example prompt for user recommendations: "Recommend the most suitable artwork based on the user's profile. Favorite genre: 'Contemporary Art', Color: 'Vibrant', Theme: 'Urban'."

[1635] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1636] Step 1:

[1637] Registering your work

[1638] Input: Artists enter and upload detailed information such as the title, description, genre, and price of their work, as well as image files.

[1639] Operation: The artist's device sends the input information to the server, which receives the artwork data and image files.

[1640] Output: The server stores the received data in a database and supplies the image data to the image analysis engine, which extracts features from the image data.

[1641] Step 2:

[1642] Image analysis and tagging

[1643] Input: The image data received by the server in step 1.

[1644] How it works: Image features are extracted using the server's image analysis engine (e.g., TensorFlow) and these features are fed into the AI ​​model.

[1645] Output: The AI ​​model generates appropriate tags based on the image data features and updates the database.

[1646] Step 3:

[1647] User preference profile generation

[1648] Input: Survey responses from new users (favorite genres, colors, themes, etc.) and historical data from existing users.

[1649] How it works: The user device sends this data to the server, and the AI ​​engine generates and updates a preference profile.

[1650] Output: The server stores the generated and updated preference profile in the database.

[1651] Step 4:

[1652] Artwork Recommendations

[1653] Input: User preference profile and artwork data in the database.

[1654] How it works: When a user opens the "Recommended" section, their device sends a recommendation request to the server, where the server's AI engine selects relevant titles based on the user's preference profile.

[1655] Output: The list of recommended artworks is sent to the user's device and displayed.

[1656] Step 5:

[1657] Purchase artwork

[1658] Input: The user selects the title they want to purchase and enters their payment and shipping information.

[1659] How it works: The user device sends a purchase request to the server, and the server processes the payment (e.g., using a payment service such as Stripe or PayPal).

[1660] Output: If the payment is successful, the purchase information is saved in the database, a sale notification is sent to the artist, and information is provided to initiate the shipping process.

[1661] Step 6:

[1662] Displaying artwork in a virtual space

[1663] Input: List of recommended artworks and detailed data (QR codes, image files, etc.).

[1664] How it works: The user's device receives data sent from the server and displays the artwork in a virtual space. Using Unity or Unreal Engine, the user can navigate the virtual space and appreciate the artwork in detail.

[1665] Output: A virtual gallery will be displayed on the user's smartphone screen, allowing the user to freely view the artwork.

[1666] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1667] In one embodiment of the present invention, an artwork recommendation system is described that includes an artist, a user, and a server, and is capable of recommending artworks based on the user's emotional state, particularly by combining an emotion engine that recognizes the user's emotions.

[1668] System configuration

[1669] This system consists of an input means for artists to register their artworks, an artificial intelligence means to learn user preferences, an interface means for users to search for and purchase artworks, a database means to store artwork data, a recommendation means to recommend artworks, and an emotion engine to recognize user emotions.

[1670] Program processing flow

[1671] 1. Artists register their works

[1672] Artist device:

[1673] Artists log in to the system and open the "Upload a New Work" page to register their work. They enter details such as the work title, description, genre, and price, and then select and upload an image file.

[1674] server:

[1675] The server receives artwork data and image files from the artist's device and stores them in a database. It uses an image analysis engine to extract features from the image data and provides that data to an AI model. The AI ​​model then uses these features to classify and tag the artworks in appropriate categories.

[1676] 2. Learning user preferences

[1677] User device:

[1678] When a new user logs in to the system, they are presented with a preference survey in which they input their favorite art genres, colors, themes, etc. Existing users' preference profiles also include their past browsing history and "like" information.

[1679] server:

[1680] The server receives user preference data and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in a database.

[1681] 3. Implementing the Emotion Engine

[1682] User device:

[1683] When users use the system, emotional data is collected in real time through cameras, microphones, text input, and other means.

[1684] server:

[1685] The server inputs the collected emotional data into an emotion engine and performs facial expression analysis, tone of voice analysis, text emotion analysis, and other processes to recognize the user's current emotions.

[1686] 4. Artwork Recommendations

[1687] User device:

[1688] When a user opens the "Recommended" section in the application, a recommendation request is sent to the server.

[1689] server:

[1690] The server acquires the user's preference and emotional profile and selects appropriate artworks based on that information. The AI ​​engine takes into account the user's real-time emotional data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[1691] 5. Buying artwork

[1692] User device:

[1693] When a user finds a title they like, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[1694] server:

[1695] The server receives the payment information and processes the secure payment. If the payment is successful, the purchase information is saved in a database and a sales notification is sent to the artist, who can then initiate the shipping process for the artwork.

[1696] Specific examples

[1697] Example 1: Artist's work registration

[1698] Artist device:

[1699] In the "Upload New Work" form, the artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape Painting," and the price "$500," selects the image file, and clicks the upload button.

[1700] server:

[1701] The server receives the artwork data and image files and stores them in a database. The image analysis engine extracts features from the image data and provides them to the AI ​​model. The AI ​​model generates the genre "landscape painting" and tags "city, night view, neon" and updates the database.

[1702] Example 2: User recommendations

[1703] User device:

[1704] New users are asked to fill out a questionnaire and select their favorite genre: landscape painting, favorite color: colorful, and theme: urban. During use, the system also collects real-time emotional data from the camera and microphone.

[1705] server:

[1706] The server receives the user's preference data and real-time emotion data and supplies it to the AI ​​engine, which then generates and updates the user's preference profile and emotion profile and stores them in a database.

[1707] When a user opens the "Recommended Works" section, the AI ​​engine generates a list of appropriate works, including "urban night views," and sends it to the user's device.

[1708] In this way, the present invention allows users to easily find artworks that match their tastes and emotional state, and allows artists to effectively sell their works.

[1709] The processing flow will be explained below.

[1710] Artist registration

[1711] Step 1:

[1712] Artist terminal: The artist logs in to the system.

[1713] Step 2:

[1714] Artist device: The artist opens the "Upload New Work" page.

[1715] Step 3:

[1716] Artist device: Enter detailed information such as the work title, description, genre, and price, and select an image file.

[1717] Step 4:

[1718] Artist device: Click the upload button.

[1719] Step 5:

[1720] Server: Receives artwork data and image files from the artist's device.

[1721] Step 6:

[1722] Server: Save the image file in a temporary directory.

[1723] Step 7:

[1724] Server: Stores work information in a database.

[1725] Step 8:

[1726] Server: Generate a work ID, rename the image file with the work ID, and save it.

[1727] Step 9:

[1728] Server: Uses an image analysis engine to extract features from image files (color, composition, theme, etc.).

[1729] Step 10:

[1730] Server: Inputs feature data into the AI ​​model to generate genre and style tags.

[1731] Step 11:

[1732] Server: Adds and updates the generated tags and features to the artwork information stored in the database.

[1733] Learning user preferences

[1734] Step 1:

[1735] User terminal: A new user logs into the system.

[1736] Step 2:

[1737] User device: The initial setup wizard displays a preference survey.

[1738] Step 3:

[1739] User device: The user inputs their favorite art genre, color, theme, etc.

[1740] Step 4:

[1741] Server: Receives survey results and historical data from user devices.

[1742] Step 5:

[1743] Server: Inputs this data into the AI ​​engine to generate a user preference profile.

[1744] Step 6:

[1745] Server: Saves and updates user preference profiles in a database.

[1746] Implementing the Emotion Engine

[1747] Step 1:

[1748] User device: Emotional data is collected in real time from the camera and microphone while the user is using the system.

[1749] Step 2:

[1750] Server: Inputs the collected emotion data into the emotion engine.

[1751] Step 3:

[1752] Server: The emotion engine performs facial expression analysis, tone of voice analysis, and text emotion analysis.

[1753] Step 4:

[1754] Server: Generates real-time user emotion data and stores / updates it in the database.

[1755] Artwork recommendations

[1756] Step 1:

[1757] User device: A logged-in user clicks on the "Featured Art" section.

[1758] Step 2:

[1759] Server: Receives the recommendation request.

[1760] Step 3:

[1761] Server: Reads the user's preference profile and emotion profile from the database.

[1762] Step 4:

[1763] Server: Input the entire art work database into the AI ​​engine and generate a list of the most suitable artworks based on the user's preferences and real-time emotional data.

[1764] Step 5:

[1765] Server: Sends the recommendation results to the user device.

[1766] Purchase artwork

[1767] Step 1:

[1768] User device: The user clicks on the work they like and presses the "Purchase" button.

[1769] Step 2:

[1770] User device: Enter payment and shipping information into the purchase form.

[1771] Step 3:

[1772] User device: Press the checkout button to send a purchase request.

[1773] Step 4:

[1774] Server: Receives the purchase request.

[1775] Step 5:

[1776] Server: Sends payment information to a payment gateway for secure processing.

[1777] Step 6:

[1778] Server: If the payment is successful, save the purchase information and transaction ID to the database.

[1779] Step 7:

[1780] Server: Sends a sales notification to the artist and instructs them to begin the shipping process.

[1781] The above are the specific processing steps based on the invention that combines emotion engines.

[1782] Example 2

[1783] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1784] Conventional art recommendation systems can recommend artworks based on user preference data, but they cannot take into account the user's current emotional state. As a result, they are unable to provide artworks that match the user's real-time emotions, making it difficult to improve user satisfaction.

[1785] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for artists to register their works in the system, an artificial intelligence means for learning the user's preferences and recommending artworks suitable for the user, and an emotion engine means for recognizing the user's emotions. This makes it possible to recommend artworks that match not only the user's preferences but also their real-time emotional state.

[1786] "Input means" is an interface that allows artists to register information about their works in the system.

[1787] "Artificial intelligence means" refers to a function within the system that learns user preference data and recommends suitable artworks to users based on that data.

[1788] "Interface means" refers to a user interface that allows a user to search for and purchase artworks.

[1789] "Database means" refers to data storage within the system for storing work data registered by artists, user preference data, and emotional data.

[1790] "Recommendation Means" means functionality within the System for recommending artworks to Users based on their preference profiles generated by the Artificial Intelligence Means.

[1791] The "emotion engine means" is a function within the system for recognizing a user's emotions and generating an emotion profile based thereon.

[1792] An "emotion profile" is data representing the user's emotional state as recognized by the emotion engine means.

[1793] In one embodiment of the present invention, an artwork recommendation system including an artist, a user, and a server is described. In particular, the system is combined with an emotion engine that recognizes the user's emotions, enabling the recommendation of artworks based on the user's emotional state. The configuration of the system is described in detail below.

[1794] System configuration

[1795] This system consists of an input means for artists to register their artworks, an artificial intelligence means to learn user preferences, an interface means for users to search for and purchase artworks, a database means to store artwork data, a recommendation means to recommend artworks, and an emotion engine to recognize user emotions.

[1796] Explanation of program processing

[1797] Artists register their works

[1798] Artist device:

[1799] The artist logs in to the system and opens the "Upload New Work" page, where they enter details such as the work title, description, genre, and price, and then select and upload an image file.

[1800] server:

[1801] The server stores artwork data and image files received from the artist's device. It uses an image analysis engine to extract features from the image data and provides that data to the AI ​​model. The AI ​​model then uses these features to classify and tag the artworks in appropriate categories.

[1802] Examples:

[1803] The artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape painting," and the price "$500," and uploads the image file.

[1804] Learning user preferences

[1805] User device:

[1806] When a new user logs into the system, they are presented with a preference questionnaire in which they are asked to enter their favorite art genres, colors, themes, etc.

[1807] server:

[1808] The server receives preference data from the user device and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in a database.

[1809] Examples:

[1810] New users fill out the survey with "Favorite genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[1811] Implementing the Emotion Engine

[1812] User device:

[1813] When users use the system, emotional data is collected in real time through cameras, microphones, text input, and other means.

[1814] server:

[1815] The server inputs the collected emotional data into an emotion engine and performs facial expression analysis, tone of voice analysis, text emotion analysis, and other processes to recognize the user's current emotions.

[1816] Examples:

[1817] While the user is using the application, the camera captures the user's facial expressions and the emotion engine analyzes the data to identify emotions such as "happiness" or "sadness."

[1818] Artwork recommendations

[1819] User device:

[1820] When a user opens the "Recommended for You" section, a recommendation request is sent to the server.

[1821] server:

[1822] The server acquires the user's preference and emotional profile and selects appropriate artworks based on that information. The AI ​​engine takes into account the user's real-time emotional data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[1823] Examples:

[1824] The server generates a list of recommended works, including "urban night views," based on the user's preference data and real-time emotional data, and presents it to the user.

[1825] Purchase artwork

[1826] User device:

[1827] When a user finds a title they wish to purchase, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[1828] server:

[1829] The server securely processes the payment information received, records the successful transaction in a database, and sends a sales notification to the artist.

[1830] Examples:

[1831] To purchase "City Night View," the user enters payment and shipping information, which is then securely processed by the server, who then notifies the artist of the sale.

[1832] Examples of prompt statements

[1833] "Please log in."

[1834] Enter details about your work and upload an image file.

[1835] "Choose your favorite art genre, color, and theme."

[1836] "Allow use of your camera and microphone to begin collecting emotional data."

[1837] "Generating a list of recommended titles. Please wait a moment."

[1838] The system allows users to easily access artworks that best suit their tastes and emotions, and allows artists to efficiently sell their work.

[1839] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1840] Program processing flow

[1841] Artists register their works

[1842] Step 1:

[1843] Artist device:

[1844] The artist logs into the system using their own account, which gives them access to their own artist page within the system.

[1845] input:

[1846] Artist login information (username, password).

[1847] output:

[1848] The artist's authentication token.

[1849] Specific behavior:

[1850] The artist enters their username and password and clicks the login button.

[1851] Step 2:

[1852] Artist device:

[1853] Artists open the "Upload New Work" page, enter details such as the work's title, description, genre, and price, and select and upload an image file.

[1854] input:

[1855] Title, description, genre, price, and image file of the work.

[1856] output:

[1857] Request to upload new work.

[1858] Specific behavior:

[1859] The artist enters the title "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape," and the price "$500," selects the image file, and clicks the upload button.

[1860] Step 3:

[1861] server:

[1862] The server stores the artwork data and image files received from the artist's device, then uses an image analysis engine to extract features from the image data and feeds that data to the AI ​​model.

[1863] input:

[1864] Artwork data and image files received from the artist's device.

[1865] output:

[1866] Image data features and category classification results.

[1867] Specific behavior:

[1868] The server stores the artwork data in a database, uses an image analysis engine to extract features, and supplies them to an AI model to generate categories and tags.

[1869] Learning user preferences

[1870] Step 1:

[1871] User device:

[1872] When a new user logs into the system, they are presented with a preference questionnaire, which asks about their favorite art genres, colors, themes, etc.

[1873] input:

[1874] User preference information (art genre, color, theme).

[1875] output:

[1876] Request to send preference survey.

[1877] Specific behavior:

[1878] A new user fills out the survey with "Favorite genre: landscape painting," "Favorite color: colorful," and "Theme: urban," and clicks the submit button.

[1879] Step 2:

[1880] server:

[1881] The server receives the preference data sent from the user device and supplies it to the AI ​​engine, which then generates a user preference profile and stores it in a database.

[1882] input:

[1883] Preference data sent from user devices.

[1884] output:

[1885] User preference profile.

[1886] Specific behavior:

[1887] The server sends the preference data to the AI ​​engine and stores the generated preference profile in a database.

[1888] Implementing the Emotion Engine

[1889] Step 1:

[1890] User device:

[1891] When users use the system, emotional data is collected in real time through cameras, microphones, text input, and other means.

[1892] input:

[1893] User facial expression, voice, and text data.

[1894] output:

[1895] Request to send real-time sentiment data.

[1896] Specific behavior:

[1897] The user authorizes the camera and microphone, and the system collects emotional data from facial expressions and voice.

[1898] Step 2:

[1899] server:

[1900] The server inputs the collected emotional data into an emotion engine and performs facial expression analysis, tone of voice analysis, text emotion analysis, and other processes to recognize the user's current emotions.

[1901] input:

[1902] Collected user sentiment data.

[1903] output:

[1904] User emotional profile.

[1905] Specific behavior:

[1906] The server analyzes the emotional data and stores emotions such as "joy" and "sadness" as an emotional profile.

[1907] Artwork recommendations

[1908] Step 1:

[1909] User device:

[1910] When a user opens the "Recommended for You" section, a recommendation request is sent to the server.

[1911] input:

[1912] Request for the creation of a "Recommended Works" section.

[1913] output:

[1914] Submit a recommendation request.

[1915] Specific behavior:

[1916] The user clicks the "Recommended Works" button.

[1917] Step 2:

[1918] server:

[1919] The server acquires the user's preference and emotional profile and selects appropriate artworks based on that information. The AI ​​engine takes into account the user's real-time emotional data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[1920] input:

[1921] User preference and emotional profiles.

[1922] output:

[1923] A list of recommended artworks.

[1924] Specific behavior:

[1925] The server provides the preference profile and emotional profile to the AI ​​engine and sends the generated list of works to the user.

[1926] Purchase artwork

[1927] Step 1:

[1928] User device:

[1929] When a user finds a title they wish to purchase, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[1930] input:

[1931] Payment and shipping information.

[1932] output:

[1933] Submit a purchase request.

[1934] Specific behavior:

[1935] Users select the work they like, press the "Purchase" button and enter their details.

[1936] Step 2:

[1937] server:

[1938] The server securely processes the payment information received, records the successful transaction in a database, and sends a sales notification to the artist.

[1939] input:

[1940] Payment and shipping information.

[1941] output:

[1942] Payment results and sales notification.

[1943] Specific behavior:

[1944] The server processes the payment information and, once the payment is successful, sends a sales notification to the artist, prompting them to begin the shipping process for the artwork.

[1945] (Application example 2)

[1946] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1947] The online art sales market has expanded in recent years, but a key issue is whether users can easily find artworks that suit their emotional state. Conventional systems primarily base recommendations on users' preference data and browsing history, making it difficult to respond to real-time emotional changes. This has led to problems such as users finding artworks that fit their emotional state at any given time, resulting in a decrease in satisfaction.

[1948] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1949] In this invention, the server includes an input means for artists to register their artworks in the system, an artificial intelligence means for learning user preferences and recommending artworks suitable for the user, and an interface means for the user to search for and purchase artworks. This makes it possible to recognize the user's emotional state in real time and recommend artworks using emotional data.

[1950] "Input means" refers to the means by which artists register their artworks in the system.

[1951] "Artificial intelligence means" refers to means for learning a user's preferences and, based on the results, recommending artworks suitable for the user.

[1952] "Interface means" refers to the means by which a user searches for and purchases artworks.

[1953] "Database means" refers to a means for storing work data registered by artists.

[1954] "Recommendation means" means means for recommending artworks to a user based on a preference profile generated by an artificial intelligence means.

[1955] An "emotion engine" is a means for recognizing a user's emotional state in real time.

[1956] An "emotion-based recommendation method" is a method for recommending artworks using emotion data recognized by an emotion engine.

[1957] The present invention relates to a system for recommending artworks based on a user's emotional state, and is configured as follows.

[1958] System configuration

[1959] The system consists of the following main components:

[1960] 1. Input method: This is the method by which artists register their works in the system. Artists input the title, description, image data, etc. of their works and save them in the database.

[1961] 2. Artificial intelligence means: This means learns user preferences and recommends artworks based on that data. It also analyzes image data of artworks, extracts features, and stores them in a database.

[1962] 3. Interface: A means for users to search for and purchase artworks. Users can interact with the system using this interface.

[1963] 4. Database means: A means for storing artwork data registered by artists. The database stores detailed information about artworks, image data, and user preference profiles.

[1964] 5. Recommendation: This is a method for recommending artworks to users based on their preference profile generated by artificial intelligence. It also takes into account the user's history and preference data to generate appropriate recommendation results.

[1965] 6. Emotion Engine: A means of recognizing the user's emotional state in real time. It uses cameras and microphones to collect emotional data and analyzes it in real time.

[1966] 7. Emotion-based recommendation method: This method uses emotional data recognized by an emotion engine to recommend artworks to users.

[1967] System Operation

[1968] The server uses these means to perform the following processing.

[1969] 1. Artist registration

[1970] The server stores the artwork data (title, description, image data, etc.) received from the artist in a database. The image analysis engine analyzes the received image data and extracts features.

[1971] 2. Learning user preferences

[1972] The server analyzes the preference data entered by new users and the browsing history of existing users, and generates and updates preference profiles using an AI engine.

[1973] 3. Emotional Data Collection and Recognition

[1974] The user device collects emotion data in real time via a camera and microphone while the user is using the system, and the collected data is sent to a server where it is analyzed by an emotion engine.

[1975] 4. Artwork Recommendations

[1976] The server selects the most suitable artworks based on the user's preference and emotional profiles and recommends them to the user through recommendation methods.

[1977] 5. Buying artwork

[1978] On the user's device, the user clicks the purchase button and enters the necessary payment information. The server then processes the payment securely and stores the purchase information in a database.

[1979] Hardware and software used

[1980] Hardware: Camera, microphone

[1981] Software: OpenCV, EmotionRecognizer, AIEngine, ArtDatabase

[1982] Examples of specific examples and prompts

[1983] Example 1: When a user feels "tired"

[1984] Prompt: "What artwork helps you relax? Do you prefer tranquil landscapes or artwork with muted colors?"

[1985] Server operation: Captures the user's facial expression with a camera, determines their level of fatigue, and recommends relaxing artwork.

[1986] Example 2: When a user feels happy

[1987] Prompt: "What bright artwork would fit your current happy mood? Would you prefer something colorful and positive?"

[1988] Server operation: The camera captures the user's facial expression, determines whether they are "happy," and recommends bright and colorful artwork.

[1989] Thus, the present invention provides a system that allows users to easily find and purchase artwork that matches their current emotional state.

[1990] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1991] Step 1:

[1992] Artist registration

[1993] Input: Artist enters work title, description, genre, image file, etc.

[1994] Server operation: Receives input artwork data and stores it in a database. It also uses an image analysis engine to extract features from the image data and provides that data to an AI model. Based on the features, the AI ​​model classifies and tags the artwork into appropriate categories.

[1995] Output: The artwork data and its features are stored in a database, and the artworks are classified by category and tag.

[1996] Step 2:

[1997] Learning user preferences

[1998] Input: Response data from preference surveys from new users, past browsing history and likes from existing users, etc.

[1999] Server operation: Receives preference data and provides it to the AI ​​engine, which then generates and updates the user's preference profile and stores it in the database.

[2000] Output: Preference profile for each user stored in a database.

[2001] Step 3:

[2002] Emotion data collection and recognition

[2003] Input: Real-time emotion data collected via the user's device camera, microphone, and text input.

[2004] Server operation: The collected emotional data is input into the emotion engine, and the user's emotional state is recognized by performing facial expression analysis, tone of voice analysis, text emotion analysis, etc.

[2005] Output: Real-time emotional state data of the user.

[2006] Step 4:

[2007] Artwork recommendations

[2008] Input: User preference profile, real-time sentiment data, user request for recommendations.

[2009] Server operation: Based on this data, the AI ​​engine selects and recommends the most relevant artworks. A generative AI model is used to generate emotion-based prompts and recommend artworks based on them.

[2010] Output: A list of recommended artworks for the user.

[2011] Step 5:

[2012] Purchase artwork

[2013] Input: Artwork selected by the user, payment information, and shipping information.

[2014] User device actions: clicking the purchase button and entering required information.

[2015] Server Action: Receives payment information and processes the transaction. If successful, stores the purchase in a database and sends a sales notification to the artist.

[2016] Output: A confirmation message of the completed purchase and a sale notification to the artist.

[2017] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[2018] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2019] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[2020] [Fourth embodiment]

[2021] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2022] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[2023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2024] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[2025] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[2026] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[2027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2028] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[2029] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[2030] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[2031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2032] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[2033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2034] In one embodiment of the present invention, an artwork recommendation system is described that includes an artist, a user, and a server.

[2035] System configuration

[2036] This system consists of an input means for artists to register their artworks, an artificial intelligence means to learn users' preferences, an interface means for users to search for and purchase artworks, a database means to store artwork data, and a recommendation means to recommend artworks.

[2037] Program processing flow

[2038] 1. Artists register their works

[2039] Artist device:

[2040] To register their work, artists first log in to the application, then enter details such as the work's title, description, genre, and price, and upload an image file.

[2041] server:

[2042] The server receives artwork data and image files from the artist's device and stores them in a database. At the same time, it uses an image analysis engine to extract features from the image data and provides this data to an AI model. The AI ​​model then uses these features to classify and tag the artworks into appropriate categories.

[2043] 2. Learning user preferences

[2044] User device:

[2045] When a new user logs in to the application, they are presented with a preference survey. They enter their favorite art genres, colors, themes, etc., and an initial preference profile is generated based on this. For existing users, past browsing history and "like" information is also included.

[2046] server:

[2047] The server receives user preference data and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in a database.

[2048] 3. Artwork Recommendations

[2049] User device:

[2050] When a user opens the "Recommended" section in the application, a recommendation request is sent to the server.

[2051] server:

[2052] The server acquires the user's preference profile and selects appropriate artworks based on that information. At this time, the AI ​​engine takes into account the user's historical data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[2053] 4. Buying artwork

[2054] User device:

[2055] When a user finds a title they like, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[2056] server:

[2057] The server receives the payment information and processes the secure transaction. If the transaction is successful, the purchase information is saved in a database and a sales notification is sent to the artist, who can then initiate the shipping process for the artwork.

[2058] Specific examples

[2059] Example 1: Artist's work registration

[2060] Artist device:

[2061] In the "Upload New Work" form, the artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape Painting," and the price "$500," then selects the image file and clicks the upload button.

[2062] server:

[2063] The server receives the artwork data and image files and stores them in a database. The image analysis engine extracts features from the image data and provides them to the AI ​​model. The AI ​​model generates the genre "landscape painting" and tags "city, night view, neon" and updates the database.

[2064] Example 2: User recommendations

[2065] User device:

[2066] New users will be asked to fill out a survey and select "Favorite genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[2067] server:

[2068] The server receives the user's preference data and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in the database.

[2069] When a user opens the "Recommended Works" section, the AI ​​engine generates a list of appropriate works, including "urban night views," and sends it to the user's device.

[2070] As a result, the present invention allows art lovers and beginners to easily find artworks that suit their tastes, and allows artists to effectively sell their works.

[2071] The processing flow will be explained below.

[2072] Artist registration

[2073] Step 1:

[2074] Artist terminal: The artist logs in to the system.

[2075] Step 2:

[2076] Artist device: The artist opens the "Upload New Work" page.

[2077] Step 3:

[2078] Artist device: Enter detailed information such as the work title, description, genre, and price, and select an image file.

[2079] Step 4:

[2080] Artist device: Click the upload button.

[2081] Step 5:

[2082] Server: Receives artwork data and image files from the artist's device.

[2083] Step 6:

[2084] Server: Save the image file in a temporary directory.

[2085] Step 7:

[2086] Server: Stores work information in a database.

[2087] Step 8:

[2088] Server: Generate a work ID, rename the image file with the work ID, and save it.

[2089] Step 9:

[2090] Server: Uses an image analysis engine to extract features from image files (color, composition, theme, etc.).

[2091] Step 10:

[2092] Server: Inputs feature data into the AI ​​model to generate genre and style tags.

[2093] Step 11:

[2094] Server: Adds and updates the generated tags and features to the artwork information stored in the database.

[2095] Learning user preferences

[2096] Step 1:

[2097] User terminal: A new user logs into the system.

[2098] Step 2:

[2099] User device: The initial setup wizard displays a preference survey.

[2100] Step 3:

[2101] User device: The user inputs their favorite art genre, color, theme, etc.

[2102] Step 4:

[2103] Server: Receives survey results and historical data from user devices.

[2104] Step 5:

[2105] Server: Inputs this data into the AI ​​engine to generate a user preference profile.

[2106] Step 6:

[2107] Server: Stores and updates user preference profiles in a database.

[2108] Artwork recommendations

[2109] Step 1:

[2110] User device: A logged-in user clicks on the "Featured Art" section.

[2111] Step 2:

[2112] Server: Receives the recommendation request.

[2113] Step 3:

[2114] Server: Reads the user's preference profile from the database.

[2115] Step 4:

[2116] Server: Inputs the entire art work database into the AI ​​engine and generates a list of works that best match the user's preferences.

[2117] Step 5:

[2118] Server: Sends the recommendation results to the user device.

[2119] Purchase artwork

[2120] Step 1:

[2121] User device: The user clicks on the work they like and presses the "Purchase" button.

[2122] Step 2:

[2123] User device: Enter payment and shipping information into the purchase form.

[2124] Step 3:

[2125] User device: Press the checkout button to send a purchase request.

[2126] Step 4:

[2127] Server: Receives the purchase request.

[2128] Step 5:

[2129] Server: Sends payment information to a payment gateway for secure processing.

[2130] Step 6:

[2131] Server: If the payment is successful, save the purchase information and transaction ID to the database.

[2132] Step 7:

[2133] Server: Sends a sales notification to the artist and instructs them to begin the shipping process.

[2134] The above are the specific processing steps for carrying out the present invention.

[2135] Example 1

[2136] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2137] Conventional art recommendation systems make it difficult for users to find artworks that match their tastes. Furthermore, they lacked efficient management of artist-registered artwork data and accurate feature extraction, resulting in inappropriate tagging and categorization. Furthermore, they lacked a consistent interface to facilitate a smooth user purchasing experience. These issues need to be addressed.

[2138] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2139] In this invention, the server includes image analysis means for extracting features from image data entered by artists and generating appropriate categories and tags, artificial intelligence means for learning user preferences and generating recommendation results, and database means for storing artwork data. This makes it possible to provide a system that efficiently recommends artworks that match users' preferences and makes it easy for artists to manage and sell their artworks.

[2140] Understood. Create the definition below.

[2141] An "artist" is a person or entity that registers their artwork in the system.

[2142] "User" means any person or entity that uses the System to search for and purchase artworks.

[2143] "Input means" refers to a device or application that allows an artist to input detailed information about their work into the system.

[2144] "Artificial intelligence means" are algorithms or software that learn a user's preferences and recommend suitable artworks.

[2145] An "interface means" is a device or application that provides a user interface for users to search for and purchase artworks.

[2146] "Database means" refers to a system or device for storing work data registered by artists and user preference profiles.

[2147] "Recommendation Means" means a device or algorithm for recommending artworks to a User based on a preference profile generated by an Artificial Intelligence Means.

[2148] "Image analysis means" refers to devices or software that extract the features of image data entered by the artist and generate appropriate categories and tags.

[2149] "Features" are analytical data such as color, shape, and texture of image data extracted by image analysis means.

[2150] In one embodiment of the present invention, an artwork recommendation system is described in which artists, users, and a server work in cooperation.

[2151] System configuration

[2152] The system consists of the following main tools:

[2153] 1. A means for artists to register their work in the system

[2154] 2. Artificial intelligence means to learn user preferences

[2155] 3. An interface for users to search for and purchase artworks

[2156] 4. Database means for storing work data

[2157] 5. Recommended Ways to Recommend

[2158] 6. Image analysis method to extract features from image data and generate categories and tags

[2159] Program processing flow

[2160] Artists register their works

[2161] Artist device:

[2162] The artist logs in to the application. After logging in, they click the "Register a new work" button to open the registration form for a new work. There, they enter the work title "Urban night view", description "Neon-lit cityscape", genre "Landscape painting", and price "$500", and upload the image file of the work. Once this is complete, they click the "Submit" button.

[2163] server:

[2164] The server receives the artwork data and image files sent by the artist and stores them in a database. At the same time, it launches an image analysis engine (e.g., OpenCV) to extract features from the received image data. The extracted features are then fed into an AI model (e.g., TensorFlow), which then generates appropriate artwork categories and tags.

[2165] Learning user preferences

[2166] User device:

[2167] When a new user logs in to the application, a preference survey is displayed, in which the user enters information such as "Favorite art genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[2168] server:

[2169] The server provides the user's preference data to an AI engine (e.g., TensorFlow). The AI ​​engine then generates a user preference profile based on the data and stores it in a database. In addition, existing users can update their profiles by including their browsing history, click history, purchase history, and likes.

[2170] Artwork recommendations

[2171] User device:

[2172] When a user selects the "Recommended Works" section in the application, a recommendation request is sent to the server.

[2173] server:

[2174] The server obtains the user's preference profile and provides it to a recommendation engine. The recommendation engine (e.g., an AI algorithm using KNN or collaborative filtering) then selects the most suitable artworks based on this. If the user likes landscape paintings and colorful urban themes, appropriate artworks such as "urban night view" will be selected. The selected recommendation list is then sent to the user's device.

[2175] Purchase artwork

[2176] User device:

[2177] The user opens the details page of the work they are interested in and clicks the "Purchase" button. They then enter their payment information and shipping address and click the "Confirm Purchase" button.

[2178] server:

[2179] The server receives the purchase request from the user and processes the payment securely (e.g., Stripe or PayPal). If the purchase is successful, the purchase information is saved in a database and a notification is sent to the artist, who can then initiate the shipping process for the artwork.

[2180] Specific examples

[2181] Example 1: Artist's work registration

[2182] Artist device:

[2183] The artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape painting," and the price "$500," and uploads the image file.

[2184] server:

[2185] The server receives the artwork data and image files and stores them in a database. It uses an image analysis engine (e.g., OpenCV) to extract features from the image data and provides them to an AI model (e.g., TensorFlow). The AI ​​model generates the genre "landscape painting" and tags "city, night view, neon" based on the features and updates the database.

[2186] Example 2: User recommendations

[2187] User device:

[2188] New users can choose their favorite genre: landscape painting, favorite color: colorful, and theme: urban in the survey.

[2189] server:

[2190] The server receives the user's preference data and supplies it to an AI engine (e.g., TensorFlow). The AI ​​engine generates a user preference profile and stores it in a database. When the user opens the "Recommended Works" section, the AI ​​engine generates a list of appropriate works, including "City Night Views," and sends it to the user's device.

[2191] Prompt Sentence Examples

[2192] "What kind of artwork do I like? I like landscapes, and I like colorful, urban-themed artwork."

[2193] The above is a specific embodiment of the artwork recommendation system according to the present invention, which allows users to efficiently discover artworks that match their tastes and allows artists to effectively sell their works.

[2194] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2195] Step 1:

[2196] Artists register their works

[2197] Artist device:

[2198] The artist logs in to the application. After logging in, they click the "Register a new work" button to open the registration form for a new work. There, they enter the work title "Urban night view", description "Neon-lit cityscape", genre "Landscape painting", and price "$500", and upload the image file of the work. Once complete, they click the "Submit" button.

[2199] Input: Title, description, genre, price, image file

[2200] Output: A request to send the work to the server

[2201] Step 2:

[2202] Receiving and storing artwork data and image files on the server

[2203] server:

[2204] The server receives the artwork data and image files sent by the artist and stores them in a database.

[2205] Input: Artwork data and image files from the artist

[2206] Output: Artwork data stored in a database and image files

[2207] Step 3:

[2208] Image data feature extraction and tag generation

[2209] server:

[2210] The server launches an image analysis engine (e.g., OpenCV) and extracts features (color, shape, texture, etc.) from the received image data. The extracted features are fed into an AI model (e.g., TensorFlow), which then generates appropriate categories and tags for the work. The results are then stored in a database.

[2211] Input: Image file

[2212] Data processing and data calculation: Feature extraction through image analysis, category and tag generation through AI models

[2213] Output: Features, categories, tags, updated database

[2214] Step 4:

[2215] Entering user preference data and creating profiles

[2216] User device:

[2217] When a new user logs in to the application, a preference survey is displayed, in which the user enters information such as "Favorite art genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[2218] Input: User preference data

[2219] Output: Preference data request to server

[2220] Step 5:

[2221] Server-generated and stored preference profiles

[2222] server:

[2223] The server provides the preference data acquired from the user to an AI engine (e.g., TensorFlow). The AI ​​engine generates a user preference profile based on this data and stores this profile in a database. In addition, existing users can update their profile by including their past browsing history, click history, purchase history, and likes.

[2224] Input: User preference data

[2225] Data processing and data calculation: AI engine generates preference profiles

[2226] Output: Preference profile, updated database

[2227] Step 6:

[2228] User submits recommendation request

[2229] User device:

[2230] When a user selects the "Recommended Works" section in the application, a recommendation request is sent to the server.

[2231] Input: Recommendation request

[2232] Output: Recommendation request sent to server

[2233] Step 7:

[2234] Recommendation generation and sending

[2235] server:

[2236] The server obtains the user's preference profile and provides it to the recommendation engine. The recommendation engine (e.g., an AI algorithm using KNN or collaborative filtering) selects the most suitable artwork based on the user's preference profile and historical data. For example, if the user prefers "landscapes, colorful, urban," "urban night views" will be selected. The selected recommendation list is sent to the user's device.

[2237] Input: Preference profile, historical data

[2238] Data processing and data calculation: Selection of works by recommendation engine

[2239] Output: Generate recommendation list and send it to user device

[2240] Step 8:

[2241] Users submitting purchase requests for works

[2242] User device:

[2243] The user opens the details page of the work they are interested in and clicks the "Purchase" button. They enter their payment information and shipping address and click the "Confirm Purchase" button.

[2244] Input: Purchase request, payment information, shipping information

[2245] Output: Purchase request sent to server

[2246] Step 9:

[2247] Server processes payments and stores purchase information

[2248] server:

[2249] The server receives the purchase request from the user and processes the payment securely (e.g., Stripe or PayPal). If the purchase is successful, the purchase information is saved in a database and a notification is sent to the artist, who can then initiate the shipping process for the artwork.

[2250] Input: Purchase request, payment information, shipping information

[2251] Data processing and calculation: payment processing, storing purchase information

[2252] Output: Purchase confirmation, notification sent, updated database

[2253] (Application example 1)

[2254] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2255] Conventional art recommendation systems have made it difficult for users to visualize artworks. They also require users to visit physical galleries, which is time-consuming and laborious. Furthermore, they can sometimes be less accurate in recommending appropriate artworks based on a user's preferences, which can discourage purchases. There is a need for new methods to resolve these issues and more effectively provide artworks to users.

[2256] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2257] In this invention, the server includes input means for artists to register their works in the system, artificial intelligence means for learning user preferences and recommending artworks that suit the user, interface means for users to search for and purchase artworks, and virtual display means for displaying artworks in a virtual space constructed on a smartphone. This allows users to easily appreciate artworks in the virtual space through their smartphones and receive highly accurate recommendations of artworks that suit their preferences, enabling them to make efficient purchases.

[2258] "Input means" refers to the means by which artists input the information necessary to register their artworks in the system. Specifically, it includes a form in which detailed information such as the title, description, genre, price, and image file of the artwork can be input.

[2259] An "artificial intelligence means" is a means equipped with machine learning algorithms that learn a user's preferences and, based on that data, recommend the most suitable artworks to the user.

[2260] "Interface means" means means including a user interface for users to search for and purchase artworks, specifically providing a GUI (Graphical User Interface) for browsing, filtering, searching, and purchasing artworks.

[2261] "Database means" refers to data storage means for storing work data registered by artists and user preference data. Specifically, it includes a cloud-based database system.

[2262] "Recommendation Means" means means for recommending artworks to users based on a preference profile generated by artificial intelligence means, and specifically includes a recommendation algorithm based on an AI engine.

[2263] "Virtual display means" refers to a means for displaying artworks in a virtual space created on a smartphone. Specifically, a virtual exhibition space is created using game engines such as Unity or Unreal Engine, allowing users to visually appreciate artworks in the virtual space.

[2264] System Overview

[2265] The present invention relates to an art recommendation system that includes an artist, a user, and a server. The system provides a function that allows users to view and purchase art in a virtual space on their smartphone. The system includes the following main means:

[2266] 1. A means for artists to register their work in the system

[2267] 2. Artificial intelligence means to learn user preferences and recommend suitable artworks

[2268] 3. An interface for users to search for and purchase artworks

[2269] 4. Database means for storing artwork data registered by artists

[2270] 5. Recommendation means for recommending artworks to a user based on a preference profile generated by artificial intelligence means

[2271] 6. A virtual display means for displaying artworks in a virtual space constructed on a smartphone

[2272] Program processing

[2273] This system is implemented using the following hardware and software:

[2274] Hardware: Smartphone (iOS or Android), server, cloud-based database (e.g., Firebase, Amazon DynamoDB)

[2275] Software and Data Processing:

[2276] Smartphone app: Building a virtual world using Unity and Unreal Engine

[2277] Cloud server: Manages artist artwork data, user preference data, and purchase data using Google Cloud and AWS

[2278] AI engine: Uses TensorFlow and PyTorch to learn user preferences and recommend works

[2279] Detailed Description

[2280] 1. Artist work registration:

[2281] Artists log in to the smartphone app, enter details such as the title, description, genre, and price of their work, and upload image files.

[2282] The server receives artwork data and image files from the artist's device and stores them in a cloud database. At the same time, it uses an image analysis engine to extract features from the image data and provides that data to the AI ​​model.

[2283] 2. Learning user preferences:

[2284] New users install the app and answer a questionnaire to generate an initial preference profile, including their favorite art genres, colors, and themes.

[2285] The server receives preference data from users and supplies it to the AI ​​engine. The AI ​​engine generates and updates preference profiles and stores them in a database. Existing users' past browsing history and "like" information are used to update their profiles.

[2286] 3. Artwork Recommendations:

[2287] When a user opens the "Recommended" section of the app, a recommendation request is sent to your server.

[2288] The server obtains the user's preference profile and uses an AI engine to select relevant works based on that information. The list of recommended works is then sent to the user's smartphone.

[2289] 4. Purchasing artwork:

[2290] When a user finds a title they like, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[2291] The server receives the payment information and processes the transaction. If the transaction is successful, the purchase information is saved in a database and a notification is sent to the artist, who can then begin the process of shipping the artwork.

[2292] Examples and prompts

[2293] Example 1:

[2294] The artist fills out the "Upload a New Work" form, entering the title of the work "Urban Dream," description "Neon-lit Night City," genre "Contemporary Art," and price "$1,000," then selects and uploads an image file.

[2295] The server receives this and stores it in a database. An AI model analyzes the image features and generates a genre and tag.

[2296] Example 2:

[2297] New users fill out a survey and select their favorite genre as "contemporary art," their favorite color as "vivid," and their theme as "urban."

[2298] Based on this, the AI ​​engine recommends suitable works that include "urban dreams."

[2299] Example prompt sentence:

[2300] Example artist submission prompt: "Submit a new artwork. Details are as follows: Title: 'Urban Dreams', Genre: 'Contemporary Art', Price: $1000."

[2301] Example prompt for user recommendations: "Recommend the most suitable artwork based on the user's profile. Favorite genre: 'Contemporary Art', Color: 'Vibrant', Theme: 'Urban'."

[2302] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2303] Step 1:

[2304] Registering your work

[2305] Input: Artists enter and upload detailed information such as the title, description, genre, and price of their work, as well as image files.

[2306] Operation: The artist's device sends the input information to the server, which receives the artwork data and image files.

[2307] Output: The server stores the received data in a database and supplies the image data to the image analysis engine, which extracts features from the image data.

[2308] Step 2:

[2309] Image analysis and tagging

[2310] Input: The image data received by the server in step 1.

[2311] How it works: Image features are extracted using the server's image analysis engine (e.g., TensorFlow) and these features are fed into the AI ​​model.

[2312] Output: The AI ​​model generates appropriate tags based on the image data features and updates the database.

[2313] Step 3:

[2314] User preference profile generation

[2315] Input: Survey responses from new users (favorite genres, colors, themes, etc.) and historical data from existing users.

[2316] How it works: The user device sends this data to the server, and the AI ​​engine generates and updates a preference profile.

[2317] Output: The server stores the generated and updated preference profile in the database.

[2318] Step 4:

[2319] Artwork Recommendations

[2320] Input: User preference profile and artwork data in the database.

[2321] How it works: When a user opens the "Recommended" section, their device sends a recommendation request to the server, where the server's AI engine selects relevant titles based on the user's preference profile.

[2322] Output: The list of recommended artworks is sent to the user's device and displayed.

[2323] Step 5:

[2324] Purchase artwork

[2325] Input: The user selects the title they want to purchase and enters their payment and shipping information.

[2326] How it works: The user device sends a purchase request to the server, and the server processes the payment (e.g., using a payment service such as Stripe or PayPal).

[2327] Output: If the payment is successful, the purchase information is saved in the database, a sale notification is sent to the artist, and information is provided to initiate the shipping process.

[2328] Step 6:

[2329] Displaying artwork in a virtual space

[2330] Input: List of recommended artworks and detailed data (QR codes, image files, etc.).

[2331] How it works: The user's device receives data sent from the server and displays the artwork in a virtual space. Using Unity or Unreal Engine, the user can navigate the virtual space and appreciate the artwork in detail.

[2332] Output: A virtual gallery will be displayed on the user's smartphone screen, allowing the user to freely view the artwork.

[2333] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2334] In one embodiment of the present invention, an artwork recommendation system is described that includes an artist, a user, and a server, and is capable of recommending artworks based on the user's emotional state, particularly by combining an emotion engine that recognizes the user's emotions.

[2335] System configuration

[2336] This system consists of an input means for artists to register their artworks, an artificial intelligence means to learn user preferences, an interface means for users to search for and purchase artworks, a database means to store artwork data, a recommendation means to recommend artworks, and an emotion engine to recognize user emotions.

[2337] Program processing flow

[2338] 1. Artists register their works

[2339] Artist device:

[2340] Artists log in to the system and open the "Upload a New Work" page to register their work. They enter details such as the work title, description, genre, and price, and then select and upload an image file.

[2341] server:

[2342] The server receives artwork data and image files from the artist's device and stores them in a database. It uses an image analysis engine to extract features from the image data and provides that data to an AI model. The AI ​​model then uses these features to classify and tag the artworks in appropriate categories.

[2343] 2. Learning user preferences

[2344] User device:

[2345] When a new user logs in to the system, they are presented with a preference survey in which they input their favorite art genres, colors, themes, etc. Existing users' preference profiles also include their past browsing history and "like" information.

[2346] server:

[2347] The server receives user preference data and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in a database.

[2348] 3. Implementing the Emotion Engine

[2349] User device:

[2350] When users use the system, emotional data is collected in real time through cameras, microphones, text input, and other means.

[2351] server:

[2352] The server inputs the collected emotional data into an emotion engine and performs facial expression analysis, tone of voice analysis, text emotion analysis, and other processes to recognize the user's current emotions.

[2353] 4. Artwork Recommendations

[2354] User device:

[2355] When a user opens the "Recommended" section in the application, a recommendation request is sent to the server.

[2356] server:

[2357] The server acquires the user's preference and emotional profile and selects appropriate artworks based on that information. The AI ​​engine takes into account the user's real-time emotional data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[2358] 5. Buying artwork

[2359] User device:

[2360] When a user finds a title they like, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[2361] server:

[2362] The server receives the payment information and processes the secure payment. If the payment is successful, the purchase information is saved in a database and a sales notification is sent to the artist, who can then initiate the shipping process for the artwork.

[2363] Specific examples

[2364] Example 1: Artist's work registration

[2365] Artist device:

[2366] In the "Upload New Work" form, the artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape Painting," and the price "$500," selects the image file, and clicks the upload button.

[2367] server:

[2368] The server receives the artwork data and image files and stores them in a database. The image analysis engine extracts features from the image data and provides them to the AI ​​model. The AI ​​model generates the genre "landscape painting" and tags "city, night view, neon" and updates the database.

[2369] Example 2: User recommendations

[2370] User device:

[2371] New users are asked to fill out a questionnaire and select their favorite genre: landscape painting, favorite color: colorful, and theme: urban. During use, the system also collects real-time emotional data from the camera and microphone.

[2372] server:

[2373] The server receives the user's preference data and real-time emotion data and supplies it to the AI ​​engine, which then generates and updates the user's preference profile and emotion profile and stores them in a database.

[2374] When a user opens the "Recommended Works" section, the AI ​​engine generates a list of appropriate works, including "urban night views," and sends it to the user's device.

[2375] In this way, the present invention allows users to easily find artworks that match their tastes and emotional state, and allows artists to effectively sell their works.

[2376] The processing flow will be explained below.

[2377] Artist registration

[2378] Step 1:

[2379] Artist terminal: The artist logs in to the system.

[2380] Step 2:

[2381] Artist device: The artist opens the "Upload New Work" page.

[2382] Step 3:

[2383] Artist device: Enter detailed information such as the work title, description, genre, and price, and select an image file.

[2384] Step 4:

[2385] Artist device: Click the upload button.

[2386] Step 5:

[2387] Server: Receives artwork data and image files from the artist's device.

[2388] Step 6:

[2389] Server: Save the image file in a temporary directory.

[2390] Step 7:

[2391] Server: Stores work information in a database.

[2392] Step 8:

[2393] Server: Generate a work ID, rename the image file with the work ID, and save it.

[2394] Step 9:

[2395] Server: Uses an image analysis engine to extract features from image files (color, composition, theme, etc.).

[2396] Step 10:

[2397] Server: Inputs feature data into the AI ​​model to generate genre and style tags.

[2398] Step 11:

[2399] Server: Adds and updates the generated tags and features to the artwork information stored in the database.

[2400] Learning user preferences

[2401] Step 1:

[2402] User terminal: A new user logs into the system.

[2403] Step 2:

[2404] User device: The initial setup wizard displays a preference survey.

[2405] Step 3:

[2406] User device: The user inputs their favorite art genre, color, theme, etc.

[2407] Step 4:

[2408] Server: Receives survey results and historical data from user devices.

[2409] Step 5:

[2410] Server: Inputs this data into the AI ​​engine to generate a user preference profile.

[2411] Step 6:

[2412] Server: Saves and updates user preference profiles in a database.

[2413] Implementing the Emotion Engine

[2414] Step 1:

[2415] User device: Emotional data is collected in real time from the camera and microphone while the user is using the system.

[2416] Step 2:

[2417] Server: Inputs the collected emotion data into the emotion engine.

[2418] Step 3:

[2419] Server: The emotion engine performs facial expression analysis, tone of voice analysis, and text emotion analysis.

[2420] Step 4:

[2421] Server: Generates real-time user emotion data and stores / updates it in the database.

[2422] Artwork recommendations

[2423] Step 1:

[2424] User device: A logged-in user clicks on the "Featured Art" section.

[2425] Step 2:

[2426] Server: Receives the recommendation request.

[2427] Step 3:

[2428] Server: Reads the user's preference profile and emotion profile from the database.

[2429] Step 4:

[2430] Server: Input the entire art work database into the AI ​​engine and generate a list of the most suitable artworks based on the user's preferences and real-time emotional data.

[2431] Step 5:

[2432] Server: Sends the recommendation results to the user device.

[2433] Purchase artwork

[2434] Step 1:

[2435] User device: The user clicks on the work they like and presses the "Purchase" button.

[2436] Step 2:

[2437] User device: Enter payment and shipping information into the purchase form.

[2438] Step 3:

[2439] User device: Press the checkout button to send a purchase request.

[2440] Step 4:

[2441] Server: Receives the purchase request.

[2442] Step 5:

[2443] Server: Sends payment information to a payment gateway for secure processing.

[2444] Step 6:

[2445] Server: If the payment is successful, save the purchase information and transaction ID to the database.

[2446] Step 7:

[2447] Server: Sends a sales notification to the artist and instructs them to begin the shipping process.

[2448] The above are the specific processing steps based on the invention that combines emotion engines.

[2449] Example 2

[2450] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2451] Conventional art recommendation systems can recommend artworks based on user preference data, but they cannot take into account the user's current emotional state. As a result, they are unable to provide artworks that match the user's real-time emotions, making it difficult to improve user satisfaction.

[2452] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for artists to register their works in the system, an artificial intelligence means for learning the user's preferences and recommending artworks suitable for the user, and an emotion engine means for recognizing the user's emotions. This makes it possible to recommend artworks that match not only the user's preferences but also their real-time emotional state.

[2453] "Input means" is an interface that allows artists to register information about their works in the system.

[2454] "Artificial intelligence means" refers to a function within the system that learns user preference data and recommends suitable artworks to users based on that data.

[2455] "Interface means" refers to a user interface that allows a user to search for and purchase artworks.

[2456] "Database means" refers to data storage within the system for storing work data registered by artists, user preference data, and emotional data.

[2457] "Recommendation Means" means functionality within the System for recommending artworks to Users based on their preference profiles generated by the Artificial Intelligence Means.

[2458] The "emotion engine means" is a function within the system for recognizing a user's emotions and generating an emotion profile based thereon.

[2459] An "emotion profile" is data representing the user's emotional state as recognized by the emotion engine means.

[2460] In one embodiment of the present invention, an artwork recommendation system including an artist, a user, and a server is described. In particular, the system is combined with an emotion engine that recognizes the user's emotions, enabling the recommendation of artworks based on the user's emotional state. The configuration of the system is described in detail below.

[2461] System configuration

[2462] This system consists of an input means for artists to register their artworks, an artificial intelligence means to learn user preferences, an interface means for users to search for and purchase artworks, a database means to store artwork data, a recommendation means to recommend artworks, and an emotion engine to recognize user emotions.

[2463] Explanation of program processing

[2464] Artists register their works

[2465] Artist device:

[2466] The artist logs in to the system and opens the "Upload New Work" page, where they enter details such as the work title, description, genre, and price, and then select and upload an image file.

[2467] server:

[2468] The server stores artwork data and image files received from the artist's device. It uses an image analysis engine to extract features from the image data and provides that data to the AI ​​model. The AI ​​model then uses these features to classify and tag the artworks in appropriate categories.

[2469] Examples:

[2470] The artist enters the title of the work "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape painting," and the price "$500," and uploads the image file.

[2471] Learning user preferences

[2472] User device:

[2473] When a new user logs into the system, they are presented with a preference questionnaire in which they are asked to enter their favorite art genres, colors, themes, etc.

[2474] server:

[2475] The server receives preference data from the user device and provides it to the AI ​​engine, which then creates and updates the user's preference profile and stores it in a database.

[2476] Examples:

[2477] New users fill out the survey with "Favorite genre: landscape painting," "Favorite color: colorful," and "Theme: urban."

[2478] Implementing the Emotion Engine

[2479] User device:

[2480] When users use the system, emotional data is collected in real time through cameras, microphones, text input, and other means.

[2481] server:

[2482] The server inputs the collected emotional data into an emotion engine and performs facial expression analysis, tone of voice analysis, text emotion analysis, and other processes to recognize the user's current emotions.

[2483] Examples:

[2484] While the user is using the application, the camera captures the user's facial expressions and the emotion engine analyzes the data to identify emotions such as "happiness" or "sadness."

[2485] Artwork recommendations

[2486] User device:

[2487] When a user opens the "Recommended for You" section, a recommendation request is sent to the server.

[2488] server:

[2489] The server acquires the user's preference and emotional profile and selects appropriate artworks based on that information. The AI ​​engine takes into account the user's real-time emotional data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[2490] Examples:

[2491] The server generates a list of recommended works, including "urban night views," based on the user's preference data and real-time emotional data, and presents it to the user.

[2492] Purchase artwork

[2493] User device:

[2494] When a user finds a title they wish to purchase, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[2495] server:

[2496] The server securely processes the payment information received, records the successful transaction in a database, and sends a sales notification to the artist.

[2497] Examples:

[2498] To purchase "City Night View," the user enters payment and shipping information, which is then securely processed by the server, who then notifies the artist of the sale.

[2499] Examples of prompt statements

[2500] "Please log in."

[2501] Enter details about your work and upload an image file.

[2502] "Choose your favorite art genre, color, and theme."

[2503] "Allow use of your camera and microphone to begin collecting emotional data."

[2504] "Generating a list of recommended titles. Please wait a moment."

[2505] The system allows users to easily access artworks that best suit their tastes and emotions, and allows artists to efficiently sell their work.

[2506] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2507] Program processing flow

[2508] Artists register their works

[2509] Step 1:

[2510] Artist device:

[2511] The artist logs into the system using their own account, which gives them access to their own artist page within the system.

[2512] input:

[2513] Artist login information (username, password).

[2514] output:

[2515] The artist's authentication token.

[2516] Specific behavior:

[2517] The artist enters their username and password and clicks the login button.

[2518] Step 2:

[2519] Artist device:

[2520] Artists open the "Upload New Work" page, enter details such as the work's title, description, genre, and price, and select and upload an image file.

[2521] input:

[2522] Title, description, genre, price, and image file of the work.

[2523] output:

[2524] Request to upload new work.

[2525] Specific behavior:

[2526] The artist enters the title "Urban Night View," the description "Neon-lit cityscape," the genre "Landscape," and the price "$500," selects the image file, and clicks the upload button.

[2527] Step 3:

[2528] server:

[2529] The server stores the artwork data and image files received from the artist's device, then uses an image analysis engine to extract features from the image data and feeds that data to the AI ​​model.

[2530] input:

[2531] Artwork data and image files received from the artist's device.

[2532] output:

[2533] Image data features and category classification results.

[2534] Specific behavior:

[2535] The server stores the artwork data in a database, uses an image analysis engine to extract features, and supplies them to an AI model to generate categories and tags.

[2536] Learning user preferences

[2537] Step 1:

[2538] User device:

[2539] When a new user logs into the system, they are presented with a preference questionnaire, which asks about their favorite art genres, colors, themes, etc.

[2540] input:

[2541] User preference information (art genre, color, theme).

[2542] output:

[2543] Request to send preference survey.

[2544] Specific behavior:

[2545] A new user fills out the survey with "Favorite genre: landscape painting," "Favorite color: colorful," and "Theme: urban," and clicks the submit button.

[2546] Step 2:

[2547] server:

[2548] The server receives the preference data sent from the user device and supplies it to the AI ​​engine, which then generates a user preference profile and stores it in a database.

[2549] input:

[2550] Preference data sent from user devices.

[2551] output:

[2552] User preference profile.

[2553] Specific behavior:

[2554] The server sends the preference data to the AI ​​engine and stores the generated preference profile in a database.

[2555] Implementing the Emotion Engine

[2556] Step 1:

[2557] User device:

[2558] When users use the system, emotional data is collected in real time through cameras, microphones, text input, and other means.

[2559] input:

[2560] User facial expression, voice, and text data.

[2561] output:

[2562] Request to send real-time sentiment data.

[2563] Specific behavior:

[2564] The user authorizes the camera and microphone, and the system collects emotional data from facial expressions and voice.

[2565] Step 2:

[2566] server:

[2567] The server inputs the collected emotional data into an emotion engine and performs facial expression analysis, tone of voice analysis, text emotion analysis, and other processes to recognize the user's current emotions.

[2568] input:

[2569] Collected user sentiment data.

[2570] output:

[2571] User emotional profile.

[2572] Specific behavior:

[2573] The server analyzes the emotional data and stores emotions such as "joy" and "sadness" as an emotional profile.

[2574] Artwork recommendations

[2575] Step 1:

[2576] User device:

[2577] When a user opens the "Recommended for You" section, a recommendation request is sent to the server.

[2578] input:

[2579] Request for the creation of a "Recommended Works" section.

[2580] output:

[2581] Submit a recommendation request.

[2582] Specific behavior:

[2583] The user clicks the "Recommended Works" button.

[2584] Step 2:

[2585] server:

[2586] The server acquires the user's preference and emotional profile and selects appropriate artworks based on that information. The AI ​​engine takes into account the user's real-time emotional data and recommends the most relevant artworks. The list of recommended artworks is then sent to the user's device.

[2587] input:

[2588] User preference and emotional profiles.

[2589] output:

[2590] A list of recommended artworks.

[2591] Specific behavior:

[2592] The server provides the preference profile and emotional profile to the AI ​​engine and sends the generated list of works to the user.

[2593] Purchase artwork

[2594] Step 1:

[2595] User device:

[2596] When a user finds a title they wish to purchase, they click the "Buy" button, enter their payment and shipping information, and a purchase request is sent to the server.

[2597] input:

[2598] Payment and shipping information.

[2599] output:

[2600] Submit a purchase request.

[2601] Specific behavior:

[2602] Users select the work they like, press the "Purchase" button and enter their details.

[2603] Step 2:

[2604] server:

[2605] The server securely processes the payment information received, records the successful transaction in a database, and sends a sales notification to the artist.

[2606] input:

[2607] Payment and shipping information.

[2608] output:

[2609] Payment results and sales notification.

[2610] Specific behavior:

[2611] The server processes the payment information and, once the payment is successful, sends a sales notification to the artist, prompting them to begin the shipping process for the artwork.

[2612] (Application example 2)

[2613] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2614] The online art sales market has expanded in recent years, but a key issue is whether users can easily find artworks that suit their emotional state. Conventional systems primarily base recommendations on users' preference data and browsing history, making it difficult to respond to real-time emotional changes. This has led to problems such as users finding artworks that fit their emotional state at any given time, resulting in a decrease in satisfaction.

[2615] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2616] In this invention, the server includes an input means for artists to register their artworks in the system, an artificial intelligence means for learning user preferences and recommending artworks suitable for the user, and an interface means for the user to search for and purchase artworks. This makes it possible to recognize the user's emotional state in real time and recommend artworks using emotional data.

[2617] "Input means" refers to the means by which artists register their artworks in the system.

[2618] "Artificial intelligence means" refers to means for learning a user's preferences and, based on the results, recommending artworks suitable for the user.

[2619] "Interface means" refers to the means by which a user searches for and purchases artworks.

[2620] "Database means" refers to a means for storing work data registered by artists.

[2621] "Recommendation means" means means for recommending artworks to a user based on a preference profile generated by an artificial intelligence means.

[2622] An "emotion engine" is a means for recognizing a user's emotional state in real time.

[2623] An "emotion-based recommendation method" is a method for recommending artworks using emotion data recognized by an emotion engine.

[2624] The present invention relates to a system for recommending artworks based on a user's emotional state, and is configured as follows.

[2625] System configuration

[2626] The system consists of the following main components:

[2627] 1. Input method: This is the method by which artists register their works in the system. Artists input the title, description, image data, etc. of their works and save them in the database.

[2628] 2. Artificial intelligence means: This means learns user preferences and recommends artworks based on that data. It also analyzes image data of artworks, extracts features, and stores them in a database.

[2629] 3. Interface: A means for users to search for and purchase artworks. Users can interact with the system using this interface.

[2630] 4. Database means: A means for storing artwork data registered by artists. The database stores detailed information about artworks, image data, and user preference profiles.

[2631] 5. Recommendation: This is a method for recommending artworks to users based on their preference profile generated by artificial intelligence. It also takes into account the user's history and preference data to generate appropriate recommendation results.

[2632] 6. Emotion Engine: A means of recognizing the user's emotional state in real time. It uses cameras and microphones to collect emotional data and analyzes it in real time.

[2633] 7. Emotion-based recommendation method: This method uses emotional data recognized by an emotion engine to recommend artworks to users.

[2634] System Operation

[2635] The server uses these means to perform the following processing.

[2636] 1. Artist registration

[2637] The server stores the artwork data (title, description, image data, etc.) received from the artist in a database. The image analysis engine analyzes the received image data and extracts features.

[2638] 2. Learning user preferences

[2639] The server analyzes the preference data entered by new users and the browsing history of existing users, and generates and updates preference profiles using an AI engine.

[2640] 3. Emotional Data Collection and Recognition

[2641] The user device collects emotion data in real time via a camera and microphone while the user is using the system, and the collected data is sent to a server where it is analyzed by an emotion engine.

[2642] 4. Artwork Recommendations

[2643] The server selects the most suitable artworks based on the user's preference and emotional profiles and recommends them to the user through recommendation methods.

[2644] 5. Buying artwork

[2645] On the user's device, the user clicks the purchase button and enters the necessary payment information. The server then processes the payment securely and stores the purchase information in a database.

[2646] Hardware and software used

[2647] Hardware: Camera, microphone

[2648] Software: OpenCV, EmotionRecognizer, AIEngine, ArtDatabase

[2649] Examples of specific examples and prompts

[2650] Example 1: When a user feels "tired"

[2651] Prompt: "What artwork helps you relax? Do you prefer tranquil landscapes or artwork with muted colors?"

[2652] Server operation: Captures the user's facial expression with a camera, determines their level of fatigue, and recommends relaxing artwork.

[2653] Example 2: When a user feels happy

[2654] Prompt: "What bright artwork would fit your current happy mood? Would you prefer something colorful and positive?"

[2655] Server operation: The camera captures the user's facial expression, determines whether they are "happy," and recommends bright and colorful artwork.

[2656] Thus, the present invention provides a system that allows users to easily find and purchase artwork that matches their current emotional state.

[2657] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2658] Step 1:

[2659] Artist registration

[2660] Input: Artist enters work title, description, genre, image file, etc.

[2661] Server operation: Receives input artwork data and stores it in a database. It also uses an image analysis engine to extract features from the image data and provides that data to an AI model. Based on the features, the AI ​​model classifies and tags the artwork into appropriate categories.

[2662] Output: The artwork data and its features are stored in a database, and the artworks are classified by category and tag.

[2663] Step 2:

[2664] Learning user preferences

[2665] Input: Response data from preference surveys from new users, past browsing history and likes from existing users, etc.

[2666] Server operation: Receives preference data and provides it to the AI ​​engine, which then generates and updates the user's preference profile and stores it in the database.

[2667] Output: Preference profile for each user stored in a database.

[2668] Step 3:

[2669] Emotion data collection and recognition

[2670] Input: Real-time emotion data collected via the user's device camera, microphone, and text input.

[2671] Server operation: The collected emotional data is input into the emotion engine, and the user's emotional state is recognized by performing facial expression analysis, tone of voice analysis, text emotion analysis, etc.

[2672] Output: Real-time emotional state data of the user.

[2673] Step 4:

[2674] Artwork recommendations

[2675] Input: User preference profile, real-time sentiment data, user request for recommendations.

[2676] Server operation: Based on this data, the AI ​​engine selects and recommends the most relevant artworks. A generative AI model is used to generate emotion-based prompts and recommend artworks based on them.

[2677] Output: A list of recommended artworks for the user.

[2678] Step 5:

[2679] Purchase artwork

[2680] Input: Artwork selected by the user, payment information, and shipping information.

[2681] User device actions: clicking the purchase button and entering required information.

[2682] Server Action: Receives payment information and processes the transaction. If successful, stores the purchase in a database and sends a sales notification to the artist.

[2683] Output: A confirmation message of the completed purchase and a sale notification to the artist.

[2684] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2685] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2686] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2687] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2688] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2689] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2690] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2691] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2692] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2693] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2694] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2695] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2696] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2697] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2698] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2699] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2700] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2701] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2702] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2703] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2704] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2705] The following is further disclosed regarding the above embodiment.

[2706] (Claim 1)

[2707] An input means for artists to register their works in the system;

[2708] an artificial intelligence means for learning user preferences and recommending suitable artworks to the user;

[2709] an interface for users to search for and purchase artworks;

[2710] a database means for storing work data registered by said artist;

[2711] recommendation means for recommending artworks to the user based on the preference profile generated by said artificial intelligence means;

[2712] A system including:

[2713] (Claim 2)

[2714] The system of claim 1 , wherein the recommending means generates the recommendation results based on user history data and preference data.

[2715] (Claim 3)

[2716] 2. The system according to claim 1, wherein the artificial intelligence means includes means for analyzing image data of artworks, extracting features, and storing the features in a database.

[2717] "Example 1"

[2718] (Claim 1)

[2719] An input means for artists to register their works in the system;

[2720] an artificial intelligence means for learning user preferences and recommending suitable artworks to the user;

[2721] an interface for users to search for and purchase artworks;

[2722] a database means for storing work data registered by said artist;

[2723] recommendation means for recommending artworks to the user based on the preference profile generated by said artificial intelligence means;

[2724] Image analysis method to extract features from image data entered by artists and generate appropriate categories and tags

[2725] A system including:

[2726] (Claim 2)

[2727] The system of claim 1 , wherein the recommending means generates the recommendation results based on user history data and preference data.

[2728] (Claim 3)

[2729] 2. The system according to claim 1, wherein the artificial intelligence means includes means for analyzing image data of artworks, extracting features, and storing the features in a database.

[2730] "Application Example 1"

[2731] (Claim 1)

[2732] An input means for artists to register their works in the system;

[2733] an artificial intelligence means for learning user preferences and recommending suitable artworks to the user;

[2734] an interface for users to search for and purchase artworks;

[2735] a database means for storing work data registered by said artist;

[2736] recommendation means for recommending artworks to the user based on the preference profile generated by said artificial intelligence means;

[2737] a virtual display means for displaying an artwork in a virtual space constructed on a smartphone;

[2738] A system including:

[2739] (Claim 2)

[2740] The system of claim 1 , wherein the recommending means generates the recommendation results based on user history data and preference data.

[2741] (Claim 3)

[2742] 2. The system according to claim 1, wherein the artificial intelligence means includes means for analyzing image data of artworks, extracting features, and storing the features in a database.

[2743] "Example 2: Combining Emotion Engines"

[2744] (Claim 1)

[2745] An input means for artists to register their works in the system;

[2746] an artificial intelligence means for learning user preferences and recomme...

Claims

1. An input means for artists to register their works in the system; an artificial intelligence means for learning user preferences and recommending suitable artworks to the user; an interface for users to search for and purchase artworks; a database means for storing work data registered by said artist; recommendation means for recommending artworks to the user based on the preference profile generated by said artificial intelligence means; A system including:

2. The system of claim 1 , wherein the recommending means generates the recommendation results based on user history data and preference data.

3. 2. The system according to claim 1, wherein the artificial intelligence means includes means for analyzing image data of artworks, extracting feature amounts, and storing the extracted feature amounts in a database.

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