System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting feature data; means for analyzing the collected feature data to extract a trend; means for generating a new design based on the trend; means for presenting the generated design; and means for receiving input data from a user and providing a design based on the input data.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] It is difficult for individual artists to grasp the trends and currents of contemporary art, making it difficult to discover new forms of expression. In particular, the task of collecting and analyzing a wide range of data is enormous, making it virtually impossible for individual artists to accomplish. Furthermore, it is difficult to evaluate how the generated ideas will be useful in actual art production and to continually improve them. The present invention has been proposed to solve these problems. [Means for solving the problem]

[0005] According to the present invention, there is provided a system including means for collecting feature data, means for analyzing the collected feature data to extract trends, means for generating new designs based on the trends, means for presenting the generated designs, and means for receiving input data from a user and providing designs based on the input data.The system further includes means for providing a user interface, allowing the user to input a desired style or theme, and means for receiving feedback from the user, analyzing the feedback, and using the feedback to improve the generating means, thereby helping artists to create innovative and meaningful art.

[0006] "Feature data" is data that includes various information about contemporary art (e.g., images, text, colors, themes).

[0007] "Trends" are patterns or trends in art styles or themes that are extracted based on collected feature data.

[0008] "Designs" are ideas or concepts for new artworks that are created by generative AI based on trends.

[0009] "User input data" refers to information about a desired art style or theme that an artist inputs into the system.

[0010] A "user interface" is a screen or interaction means that allows an artist to interact with the system.

[0011] "Feedback" refers to evaluations and comments that users make on the designs provided, and is information that can be used to improve the system. [Brief explanation of the drawings]

[0012] [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

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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).

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] The present invention is a system in which a server, terminals, and users work together to analyze contemporary art data and provide artists with new ideas for creating art. The processing for each role will be explained in detail below.

[0034] Server Processing

[0035] The server first collects data about contemporary art from various sources. For example, it uses APIs and web scraping techniques from social media, online art galleries, and specialized magazines to collect posts and articles tagged with "contemporary art." This data includes image data, text data, and more.

[0036] The collected data is stored in a database and then undergoes a data cleaning process. Data cleaning removes unnecessary information that creates noise and converts it into a format suitable for analysis. For example, advertisements and irrelevant content are removed from text data, and image data is converted into a unified format.

[0037] Next, the cleaned data undergoes trend analysis. The server uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data, and image processing algorithms to analyze popular colors, shapes, and compositions. This trend data is then used to run a generative AI to generate ideas for new artworks. These ideas are then stored in a database.

[0038] Terminal handling

[0039] The terminal provides a user interface (GUI) for users to access the server through which they can browse contemporary art trend information and generated ideas. The interface also includes fields for artists to input their desired art style and theme.

[0040] When the user inputs their preferences into the device, the data is sent to the server. The server regenerates the best art ideas based on the user's input data and sends them to the device. The user can then review the provided ideas and select one.

[0041] User Action

[0042] First, users operate the terminal interface to log in to the system. Then, they input their requirements and preferences for their art creation. For example, if a user requests an "abstract style" or "art themed around natural elements," that data is sent to the server.

[0043] The user reviews the ideas sent by the server and selects the one they like best. They then create art based on the selected idea and provide feedback to the system via their device. The feedback evaluates how useful the selected idea was in the actual creation, and the server uses this feedback to improve the performance of the generative AI and the quality of the ideas it provides.

[0044] A specific example is the following flow:

[0045] The user inputs into the interface their desire to create a "nature" themed artwork.

[0046] The terminal transmits this data to the server.

[0047] The server runs a generative AI using trend data related to the "nature" theme to generate ideas for "nature-themed abstract art."

[0048] The generated ideas are sent to the terminal and presented to the user.

[0049] Users select an idea and then work on creating it. After creation, the results are fed back to the user.

[0050] In this way, a system is provided in which servers, terminals, and users work together to help create innovative and meaningful art.

[0051] The processing flow will be explained below.

[0052] Step 1:

[0053] The server collects feature data about art from data sources such as social media, online art galleries, and specialized magazines, including by using APIs to retrieve tagged posts and web scraping techniques to download articles and images.

[0054] Step 2:

[0055] The server stores the collected data in a database. Before storing it, it performs a data cleaning process to remove unnecessary information that creates noise from the text and images. For example, it removes advertisements and irrelevant comments from the text data, and converts image data into a unified format (JPEG, PNG, etc.).

[0056] Step 3:

[0057] The server analyzes the cleaned data and uses natural language processing (NLP) techniques to extract frequent keywords and themes, as well as image processing algorithms to analyze popular colors, shapes, and compositions, thereby identifying trends in contemporary art.

[0058] Step 4:

[0059] The server runs a generative AI based on the analyzed trend data to generate ideas for new artworks. The generative AI uses the learned model to propose designs based on the trends.

[0060] Step 5:

[0061] The server stores the generated art ideas in a database and makes them accessible to users through a user interface.

[0062] Step 6:

[0063] The terminal provides a graphical user interface (GUI) for users to access, through which they can view contemporary art trend information and generated ideas.

[0064] Step 7:

[0065] Users input their desired art style and theme into the interface via their device, such as "abstract style" or "art with a natural element theme."

[0066] Step 8:

[0067] The device sends the user's input data to the server, which analyzes the data and regenerates the optimal art idea based on the user's wishes.

[0068] Step 9:

[0069] The server sends the generated ideas to the terminal, which displays them on the user interface. The user can review the ideas provided and select the one they like best.

[0070] Step 10:

[0071] Users create art based on the selected idea, and after completing the work, they input feedback on the finished product into the system via their terminal.

[0072] Step 11:

[0073] The server receives user feedback and analyzes it to improve the performance of the generative AI and the quality of the ideas it provides. This feedback cycle improves the accuracy of the system and helps with future idea generation.

[0074] Through these steps, a system is realized in which the server, terminals, and users work together to analyze trends in contemporary art and help create innovative and meaningful art.

[0075] Example 1

[0076] 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."

[0077] In the creation of contemporary art, it is important for artists to gain new ideas and inspiration, but currently this requires a lot of time and effort. It is also difficult to integrate and analyze information obtained from different data sources. For this reason, a system that allows artists to gain new art ideas effectively and quickly is needed.

[0078] 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.

[0079] In this invention, the server includes means for collecting data related to contemporary art, means for storing the collected data in a database and cleaning the data to remove noise, trend analysis means using natural language processing technology to analyze the cleaned data and extract frequently occurring keywords and themes, trend analysis means using image processing technology to analyze image data and extract colors and shapes, means for operating a generative AI model that generates ideas for new artworks based on the trend data, means for storing the generated art ideas in a database and displaying them through a user interface, and means for receiving input data from a user and re-operating the generative AI model based on the input data to provide optimal art ideas, thereby enabling artists to efficiently and quickly obtain new art ideas.

[0080] "Data collection" refers to the acquisition of data related to contemporary art from multiple sources on the Internet.

[0081] A "database" is a repository where collected data is stored so that it can be accessed and analyzed later.

[0082] "Data cleaning" refers to the process of removing unnecessary noise and irrelevant information from collected data and converting it into a form suitable for analysis.

[0083] "Natural Language Processing (NLP)" is a technique used to extract frequent keywords and themes from text data.

[0084] "Image processing" is a technology that analyzes image data and extracts features such as color and shape.

[0085] "Trend analysis" is an analytical method that uses clean data to detect frequently occurring keywords, themes, colors, shapes, etc. in contemporary art.

[0086] A "generative AI model" is an artificial intelligence model that generates new ideas and works based on given data and prompts.

[0087] A "user interface (GUI)" is a visual interface that allows a user to access a system, input data, and view results.

[0088] "Input data" refers to the information that a user inputs into the system, such as the desired art style and theme.

[0089] "Feedback" means that the user uses the generated ideas to create art and then returns the results and evaluation to the system.

[0090] The present invention is a system in which a server, terminals, and users work together to analyze contemporary art data and provide artists with new ideas for creating art. The processing for each role will be explained in detail below.

[0091] Server Processing

[0092] The server first collects data about contemporary art from multiple sources on the Internet. For example, the server uses social media APIs (e.g., Instagram API) to retrieve posts tagged with "contemporary art." It also uses web scraping technologies (e.g., BeautifulSoup and Scrapy) to collect data from websites of online art galleries and specialized magazines. The collected data includes image data and text data.

[0093] The collected data is first stored in a database. It then undergoes a data cleaning process. During data cleaning, noise information is removed from the collected data and it is converted into a format suitable for analysis. For example, advertisements and irrelevant information are removed from text data using regular expressions, and image data is standardized into JPEG format using PIL (Python Imaging Library) and converted to 800x800 pixels.

[0094] The server then performs trend analysis using the cleaned data. Here, natural language processing (NLP) techniques (e.g., SpaCy and NLTK) are used to extract frequently occurring keywords and themes from the text data. Image processing techniques (e.g., OpenCV) are also used to analyze popular colors and shapes from the image data. Specifically, frequently occurring keywords such as "abstract," "natural," and "modern" are extracted from the text data, and the image data confirms that "blue" and "circular composition" are common.

[0095] Based on the trend data, the server runs a generative AI model (e.g., GPT-3 or DALL-E) to generate ideas for new artworks. The prompts are in the form of "Generate ideas for abstract, blue artwork incorporating natural elements." The generated ideas are stored in a database and displayed through a user interface.

[0096] Terminal handling

[0097] The terminal provides a user interface (GUI) for users to access the system. The GUI has fields for displaying collected contemporary art trend information and generated art ideas. Through this interface, users can input their desired art style and theme to the server.

[0098] When a user inputs the desired art style or theme into the device interface, the data is sent by the device to the server. Here, the data can be sent asynchronously using Ajax technology. For example, if a user wants art ideas with the theme "abstract and natural," the data is sent to the server.

[0099] The server then runs the generative AI model again based on the user's input data, generates optimal art ideas, and sends them to the device. These generated art ideas are displayed on the device's interface, allowing the user to select the best one.

[0100] User Action

[0101] Users log in to the system through a terminal interface. When logging in, the user's authentication information is transmitted encrypted for security reasons. Next, the user enters their requests and wishes regarding their art creation. For example, if they want art in an "abstract style" or with a "natural element" theme, they enter that into the interface.

[0102] The user reviews the art ideas sent by the server and selects the one they like best. The user then creates the art based on the selected idea. After completing the creation, the results are fed back to the system. This feedback evaluates how useful the selected idea was in the actual creation. The server uses this feedback to improve the performance of the generative AI model and the quality of the ideas it provides.

[0103] As described above, a system is realized in which the server, terminals, and users work together to efficiently and quickly generate new art ideas.

[0104] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0105] Step 1:

[0106] The server collects data about contemporary art from social media, online art galleries, and specialized magazine websites using tools such as the Instagram API, BeautifulSoup, and Scrapy. Specifically, the server calls the Instagram API to retrieve the 10,000 most recent posts tagged "contemporaryart." It also uses Scrapy to retrieve information about new exhibitions from specific online art gallery pages.

[0107] Input: Contemporary art data collected via API and web scraping

[0108] Output: Collected text and image data

[0109] Step 2:

[0110] The server stores the collected data in a database and then performs a data cleaning process. Specifically, it uses regular expressions to remove advertisements and irrelevant information from the collected text data, and then uses PIL (Python Imaging Library) to standardize the image data into JPEG format and convert it to 800x800 pixels.

[0111] Input: Collected text and image data

[0112] Output: Clean text and image data

[0113] Step 3:

[0114] The server uses the cleaned data to perform trend analysis. Specifically, it uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data. SpaCy and NLTK are used for NLP. It also uses OpenCV to analyze popular colors and shapes from image data. For example, it extracts frequently occurring keywords such as "abstract," "nature," and "modern" from the text data, and determines that "blue" and "circular composition" are common in the image data.

[0115] Input: Clean text and image data

[0116] Output: Extracted frequent keywords, themes, colors, shapes, and other trend data

[0117] Step 4:

[0118] The server runs a generative AI model based on the trend data to generate ideas for new artworks. Specifically, the generative AI is run using GPT-3 and DALL-E, and generates ideas by entering the prompt "Generate ideas for abstract blue artwork incorporating natural elements." The generated ideas are stored in a database.

[0119] Input: Trend data and prompt statement

[0120] Output: Generated art ideas

[0121] Step 5:

[0122] The terminal receives and displays the generated art ideas from the server through a user interface provided to the user, which also includes a field for the artist to input their desired art style and theme.

[0123] Input: Generated art ideas

[0124] Output: The idea displayed in the user interface

[0125] Step 6:

[0126] The user inputs the desired art style or theme into the device interface, and the data is sent to the server. For example, if the user wants an "abstract and natural" themed art idea, the data is sent to the server.

[0127] Input: User-entered art style and theme

[0128] Output: Desired data to be sent to the server

[0129] Step 7:

[0130] The server re-runs the generative AI model based on the user's input data, generates optimal art ideas, and sends them to the device. For example, the AI ​​can generate ideas using new prompts that match the user's wishes.

[0131] Input: User's desired data and prompt

[0132] Output: Best art ideas

[0133] Step 8:

[0134] The terminal displays the best art ideas sent from the server on the user interface, and the user can review the provided ideas and select the one they like best.

[0135] Input: The best art ideas sent by the server

[0136] Output: The best idea displayed in the user interface

[0137] Step 9:

[0138] The user creates an artwork based on the selected idea and provides feedback to the system via the terminal about the results. The feedback includes an evaluation of how the selected idea was useful in the actual creation.

[0139] Input: Results and feedback on the selected ideas

[0140] Output: Feedback data sent to the server

[0141] Step 10:

[0142] The server analyzes user feedback and uses it to improve the performance of the generative AI model and the quality of the ideas it provides, thereby increasing the accuracy and usefulness of the entire system.

[0143] Input: Feedback data from users

[0144] Output: Improved generative AI models and the quality of generated ideas

[0145] (Application example 1)

[0146] 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."

[0147] In the creation of modern art, artists need to collect and analyze vast amounts of data to come up with new ideas. It is also difficult to quickly grasp the latest trends. Furthermore, there are few ways for artists to obtain specific ideas that fit their own style or theme. As a result, there is a need for an efficient system to enhance creativity.

[0148] 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.

[0149] In this invention, the server includes means for collecting feature data, means for analyzing the collected feature data to extract trends, means for generating new designs based on the trends, means for presenting the generated designs, means for receiving input data from a user and providing designs based on the input data, and means for operating a generative AI model based on a theme and style specified by the user to generate art ideas. This allows users to easily obtain new art ideas that meet their individual needs and enhance their creativity. It also allows users to efficiently grasp trends in contemporary art and utilize them in their creations.

[0150] "Feature data" refers to information collected for analysis, such as text data, image data, and social media posts related to the contemporary art in question.

[0151] "Analysis" refers to the process of extracting trends from collected feature data and converting it into meaningful information.

[0152] "Trends" are common themes or patterns that emerge from the analyzed data.

[0153] "Designs" refer to artworks and concepts created using generative AI models.

[0154] A "generative AI model" is a program that uses machine learning algorithms to automatically generate new design and art ideas.

[0155] "Presenting" means displaying the generated designs and ideas to the user.

[0156] A "user interface" is an interactive system that has screens and input fields that allow a user to operate the system.

[0157] "Feedback" refers to information that allows a user to evaluate the generated design and its proposals and provide their opinions and suggestions for improvement to the system.

[0158] "Generative means" refers to the functions and techniques for generating new designs and ideas based on the analyzed trends.

[0159] "Trend data" refers to information about distinctive themes or patterns that emerge as a result of analysis.

[0160] "Behavior" refers to the process by which the generative AI model generates art ideas based on themes and styles specified by the user.

[0161] This invention is a system in which a server, terminals, and users cooperate to collect and analyze data on contemporary art and provide ideas for new art creation. This system has the function of collecting and analyzing characteristic data, extracting trends, generating designs based on that, and presenting them to users.

[0162] The server collects data about contemporary art from various sources, including social media, online art galleries, and specialized magazines, using APIs and web scraping techniques. The collected data is then stored in a database and undergoes data cleaning, during which unnecessary information is removed and the data is converted into a format suitable for analysis.

[0163] The server then analyzes the cleaned data for trends. This analysis uses natural language processing (NLP) techniques and image processing algorithms to extract frequently occurring keywords, themes, popular colors, shapes, and compositions. The results of this analysis are fed into a generative AI model to generate new art ideas. These ideas are then stored in a database.

[0164] The terminal provides a user interface (UI) for users to access the system. The UI allows users to view trend information and generated ideas. It also includes a field for artists to input their desired art style and theme. For example, if a user inputs their desire to create a "nature"-themed artwork, that data is sent to the server.

[0165] Based on the user's input, the server regenerates optimal art ideas and sends them to the device. The user can browse these ideas and select one. They then create art based on the selected idea and provide feedback on the results. The feedback is an evaluation of how useful the idea was in actual production, and the server uses this feedback to improve the performance of the generation AI and the quality of the ideas it provides.

[0166] Hardware and software used

[0167] On the server side, Amazon Web Services (AWS) is used for data storage and analysis, Google Cloud Natural Language API is used for natural language processing, OpenCV is used for image processing, and OpenAI's GPT-4 model is used for generative AI.

[0168] Specific examples

[0169] If a user wishes to create a piece of art with a "nature" theme, they input their request through the device's UI. Based on the input, the server inputs the following prompt into the generative AI model:

[0170] "Prompt for generative AI model: Generate ideas for nature-themed abstract art."

[0171] Based on this prompt, the generative AI model generates multiple art ideas, including specific suggestions for color, composition, and theme. The generated ideas are displayed on the device's UI, allowing the user to use them as reference for creating their own artwork.

[0172] As a result, the entire process from collecting and analyzing feature data to generating new art ideas and presenting them to users is automated, realizing a system that supports artists' creative activities.

[0173] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0174] Step 1:

[0175] The server collects data related to contemporary art. Specifically, it uses APIs and web scraping technologies from social media, online art galleries, and specialized magazines to collect text and image data. The input is data from API calls and web scraping, and the output is the collected raw data.

[0176] Step 2:

[0177] The server stores the collected data in a database and performs a data cleaning process. Specifically, it removes unnecessary information that creates noise and converts it into a format suitable for analysis. For example, it removes advertisements and irrelevant content and converts image data into a unified format. The input is the raw data obtained in step 1, and the output is clean data.

[0178] Step 3:

[0179] The server then uses the cleaned data to analyze trends. It uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data, and image processing algorithms to analyze popular colors, shapes, compositions, etc. The input is the cleaned data generated in step 2, and the output is trend data resulting from the analysis.

[0180] Step 4:

[0181] The server runs a generative AI model based on the analyzed trend data to generate new art ideas. Specifically, the trend data is input into the generative AI as prompts to generate ideas. The generated ideas are stored in a database. The input is trend data, and the output is the generated art idea.

[0182] Step 5:

[0183] The terminal provides a user interface, allowing users to access the system. The UI displays generated art ideas and trend information, and includes a field where artists can input their desired style and theme. The input is data based on the user's preferences and interests, and the output is new art ideas based on that data.

[0184] Step 6:

[0185] The user operates the terminal interface and inputs the desired style or theme, for example, "nature-themed artwork." The input is the theme or style the user inputs, and the output is the corresponding prompt.

[0186] Step 7:

[0187] The server regenerates optimal art ideas based on the user's input data and sends them to the device. The server creates prompts using the user's input theme and runs the generative AI model again. The input is the user's theme and style data, and the output is updated art ideas.

[0188] Step 8:

[0189] The user reviews the provided ideas and selects the one they like best. They then create art based on the selected idea and provide feedback to the system. The input is the selected art idea, and the output is the resulting feedback data.

[0190] Step 9:

[0191] The server analyzes user feedback to improve the performance of the generative AI and the quality of the ideas it provides. Specifically, it analyzes user evaluations and uses them as training data for the generative AI. The input is user feedback data, and the output is an updated generative AI model.

[0192] The above process provides a support system that allows servers, terminals, and users to work together to create innovative and meaningful art.

[0193] 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.

[0194] This invention is a system in which a server, a terminal, a user, and an emotion engine work together to analyze contemporary art data, provide artists with new ideas for creating art, and recognize the user's emotions and reflect them in the design. The processing for each role is explained in detail below.

[0195] Server Processing

[0196] The server first collects feature data about contemporary art from various sources, such as social media, online art galleries, and specialized magazines, using APIs and web scraping techniques to collect posts and articles tagged with "contemporary art." This data includes image data and text data.

[0197] The collected data is stored in a database and then undergoes a data cleaning process. Data cleaning removes unnecessary information that creates noise and converts it into a format suitable for analysis. For example, advertisements and irrelevant content are removed from text data, and image data is converted into a unified format.

[0198] Next, the cleaned data undergoes trend analysis. The server uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data, and image processing algorithms to analyze popular colors, shapes, and compositions. This trend data is then used to run a generative AI to generate ideas for new artworks. These ideas are then stored in a database.

[0199] Emotion engine processing

[0200] The emotion engine analyzes facial expressions, click patterns, operation logs, etc. as users browse designs, recognizing emotions in real time. Emotional data is acquired using a camera and microphone built into the user interface.

[0201] The recognized emotion data is sent to a server and used as feedback for the design the user has viewed, allowing the generative AI to provide designs that take the user's emotions into consideration when generating designs from then on.

[0202] Terminal handling

[0203] The terminal provides a graphical user interface (GUI) for users to access the server and browse contemporary art trend information and generated ideas. The interface also includes fields for artists to input their desired art style and theme.

[0204] When the user inputs their preferences into the device, the data is sent to the server. The server regenerates the best art ideas based on the user's input data and sends them to the device. The user can then review the provided ideas and select one.

[0205] User Action

[0206] First, users operate the terminal interface to log in to the system. Then, they input their requirements and preferences for their art creation. For example, if a user requests an "abstract style" or "art themed around natural elements," that data is sent to the server.

[0207] The user reviews the ideas sent from the server and selects the one they like best. They then create art based on the selected idea and feed the results back to the system via their device. At this time, the user's emotional data, as recognized by the emotion engine, is also fed back.

[0208] A specific example is the following flow:

[0209] The user inputs into the interface their desire to create an artwork with the theme of "inspirational landscapes."

[0210] The terminal transmits this data to the server.

[0211] The server runs a generative AI using trend data related to the theme of "inspirational landscapes" to generate ideas for "abstract art with the theme of inspiring landscapes."

[0212] The generated ideas are sent to the terminal and presented to the user.

[0213] While the user is browsing the presented ideas, the emotion engine recognizes the user's emotions in real time and transmits this information to the server.

[0214] Users select an idea and then work on creating it. After creation, the results are given along with emotional feedback.

[0215] In this way, a system is provided in which the server, terminal, user and emotion engine work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[0216] The processing flow will be explained below.

[0217] Step 1:

[0218] The server collects feature data from data sources such as social media, online art galleries, and specialty magazines, using APIs to retrieve tagged art posts and web scraping techniques to download articles and images.

[0219] Step 2:

[0220] The server stores the collected feature data in a database. Before storing, the data is cleaned to remove unnecessary information from the text and images. For example, advertisements and irrelevant comments are removed from the text data, and image data is converted into a unified format.

[0221] Step 3:

[0222] The server analyzes the cleaned data and uses natural language processing (NLP) techniques to extract frequent keywords and themes, as well as image processing algorithms to analyze popular colors, shapes, and compositions, thereby identifying trends in contemporary art.

[0223] Step 4:

[0224] The server runs a generative AI based on the analyzed trend data to generate ideas for new artworks. The generative AI uses the learned model to propose designs based on the trends.

[0225] Step 5:

[0226] The server stores the generated art ideas in a database and makes them accessible to users through a user interface.

[0227] Step 6:

[0228] The terminal provides a graphical user interface (GUI) for users to access, through which they can view contemporary art trend information and generated ideas.

[0229] Step 7:

[0230] The user interface is equipped with an emotion engine that recognizes emotions in real time by analyzing facial expressions, click patterns, and operation logs as users browse designs.

[0231] Step 8:

[0232] Users input their desired art style and theme into the interface via their device, such as "abstract style" or "art with a natural element theme."

[0233] Step 9:

[0234] The device sends the user's input data to the server, which then combines and analyzes this data with the emotional data recognized by the emotion engine to regenerate optimal art ideas based on the user's wishes.

[0235] Step 10:

[0236] The server sends the generated ideas to the terminal, which displays them on the user interface. The user can review the ideas provided and select the one they like best.

[0237] Step 11:

[0238] The user creates art based on the selected idea. After completing the work, the user inputs feedback on the finished product into the system via a terminal. At this time, the user's emotional data recognized by the emotion engine is also fed back.

[0239] Step 12:

[0240] The server receives user feedback and emotional data, analyzes it, and improves the performance of the AI ​​and the quality of the ideas it provides. This feedback cycle improves the system's accuracy and helps with future idea generation.

[0241] In this way, a system is provided in which the server, terminal, user and emotion engine work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[0242] Example 2

[0243] 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."

[0244] In the creation of contemporary art, it is extremely difficult for artists to find new ideas and create designs that match the user's emotions. Furthermore, generating personalized designs based on individual users' emotions requires advanced analytical technology and real-time emotion recognition, which conventional systems often cannot adequately address. Therefore, there is a need to analyze contemporary art data and provide new artwork ideas that take the user's emotions into consideration.

[0245] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0246] In this invention, the server includes means for collecting feature data, means for analyzing the collected feature data to extract trends, means for operating a generative AI model to generate new designs based on the trends, means for analyzing user emotion data in real time and using the data as feedback, means for providing a user interface for the user to input a desired style or theme, means for presenting the generated designs to the user, and means for receiving feedback from the user, analyzing the feedback, and using the feedback to improve the generation means. This makes it possible to efficiently analyze a variety of contemporary art data and provide personalized designs that match the user's emotions.

[0247] "Feature data" refers to information such as image data and text data related to contemporary art, including data collected from social media and online galleries.

[0248] A "generative AI model" refers to an algorithm that uses large-scale language models and deep learning algorithms to generate ideas for new designs and artworks.

[0249] An "emotion engine" refers to a system that analyzes a user's facial expressions, click patterns, operation logs, etc. to recognize emotions in real time and analyzes that data.

[0250] "User Interface" refers to a graphical interface that allows a user to input a desired style or theme and view the generated design.

[0251] "Trend data" refers to information such as frequently occurring keywords, themes, colors, shapes, and compositions extracted by analyzing collected feature data.

[0252] "Feedback" refers to the opinions and reactions users provide to the generated designs, including emotional data obtained through the emotion engine.

[0253] This invention is a system in which a server, a terminal, a user, and an emotion engine work together to analyze contemporary art data, provide artists with new ideas for creating art, and recognize the user's emotions and reflect them in the design. The processing for each role is explained in detail below.

[0254] Server Processing

[0255] The server first collects feature data about contemporary art from social media, online art galleries, and specialized magazines using APIs and web scraping. The APIs of Instagram and online galleries are used as the primary data source. For example, the server uses the Instagram API to collect images tagged with "contemporary art" and their captions.

[0256] The collected data is temporarily stored in a database such as MySQL or MongoDB, after which it undergoes a data cleaning process: regular expressions in Python are used to remove advertisements and irrelevant content from the text data, and image data is converted into a unified format (e.g., JPEG).

[0257] Based on the clean data, the server uses natural language processing (NLP) techniques to extract frequent keywords and themes from the text data, and Python libraries (NLTK and OpenCV) to analyze popular colors, shapes, and compositions from image data.

[0258] Based on the analysis results, the server runs a generative AI model to generate ideas for new artworks. For example, the generative AI model uses a large-scale language model such as GPT-4. The following prompt is input to the model:

[0259] Please generate ideas for artwork with the theme of "inspirational landscapes." Please also suggest specific colors, shapes, and compositions.

[0260] The generated ideas are then stored in the database.

[0261] Emotion engine processing

[0262] The emotion engine analyzes facial expressions, click patterns, operation logs, and other data as users browse designs to recognize emotions in real time. This is achieved using a camera and microphone built into the interface. For example, the camera captures the user's facial expressions, and the microphone acquires audio data. This data is sent to an API for emotion analysis (e.g., Microsoft Azure's Emotion API) and analyzed in real time.

[0263] The recognized emotion data is sent to the server and used as feedback data for the design the user viewed. This data will serve as the basis for providing designs that take the user's emotions into consideration when generating designs from now on.

[0264] Terminal handling

[0265] The terminal provides a graphical user interface (GUI) for users to access. This interface is likely to be implemented using React.js or Vue.js. Through this interface, users can access the server and view contemporary art trend information and generated art ideas. There is also a field for users to input their desired art style and theme.

[0266] Users input their desired style and theme into the interface, and the data is sent to the server via an AJAX request. The server generates new art ideas based on this data and sends the results to the device, where the user can view and select from the presented ideas.

[0267] User Action

[0268] Users first log in to the system through a terminal interface and enter their requirements and preferences for their art creation, such as specific preferences such as "abstract style" or "art themed around natural elements."

[0269] The user reviews the ideas sent from the server and selects the one they like best. After making their selection, they then create the actual artwork, and the results are fed back to the system via their device. At this time, the user's emotional data, as recognized by the emotion engine, is also fed back.

[0270] In this way, the system of the present invention enables the server, terminal, user, and emotion engine to work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[0271] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0272] Step 1:

[0273] The server collects feature data. As input, it obtains data about contemporary art from social media APIs and web scraping tools. This includes social media (e.g., posts tagged "contemporary art" using the Instagram API), online art galleries, and specialized magazines. Specifically, it sends API requests and receives the retrieved data in JSON format. The output is the collected image data and text data.

[0274] Step 2:

[0275] The server stores the collected data in a database. The input is the collected feature data. For example, the data is temporarily stored in a database such as MongoDB. Specifically, it executes an insert query on the database to save image data and text data. The output is the uncleaned data stored in the database.

[0276] Step 3:

[0277] The server performs data cleaning. The input is the raw feature data stored in the database. It uses Python regular expressions to remove advertisements and irrelevant content from the text data and converts the image data into a unified format (e.g., JPEG). Specific operations include filtering the text data and converting the image data format. The output is clean data converted into a format suitable for analysis.

[0278] Step 4:

[0279] The server performs trend analysis using cleaned data. The input is cleaned feature data. Natural language processing (NLP) techniques are used to extract frequently occurring keywords and themes from the text data, and Python libraries (NLTK and OpenCV) are used to analyze popular colors, shapes, and compositions from image data. Specifically, it performs text analysis using keyword extraction algorithms and image processing algorithms. The output is trend data extracted as frequently occurring keywords, themes, colors, shapes, compositions, etc.

[0280] Step 5:

[0281] The server runs a generative AI model based on the trend data to generate ideas for new artworks. The input is the trend data and a generative AI model (e.g., GPT-4 model). Specifically, the server gives the generative AI model the following prompt:

[0282] Please generate ideas for artwork with the theme of "inspirational landscapes." Please also suggest specific colors, shapes, and compositions.

[0283] The output is the generated new art idea.

[0284] Step 6:

[0285] The server saves the generated art ideas in a database. The input is the generated art ideas. The specific operation is to execute an insert query on the database to save the art ideas. The output is the art ideas saved in the database.

[0286] Step 7:

[0287] The terminal provides a user interface. The input is the art idea sent from the server and the user's input data. Through an interface built with React.js or Vue.js, it provides fields for the user to enter their desired art style and theme. Specific operations include rendering a form and accepting user input. The output is the user's input data.

[0288] Step 8:

[0289] A user operates the terminal interface to log in to the system. The input is the user's authentication information. Specifically, the user enters a username and password into the login form and sends an authentication request to the server. The output is the authenticated user's session information.

[0290] Step 9:

[0291] The user inputs their requirements and desires for their art creation. The input is the user's desired style or theme. Specific actions include entering "abstract style" or "art themed around natural elements" into a text box on the interface. The output is the user's input data, which is sent to the server.

[0292] Step 10:

[0293] The server generates new art ideas based on the user's wishes and sends them to the device. The inputs are the user's input data and the generative AI model. Specifically, the server provides the user's wishes as prompts to the generative AI model, which then generates new art ideas. The output is the new art ideas sent to the device.

[0294] Step 11:

[0295] The terminal presents the art ideas sent from the server to the user. The input is a new art idea. Specific operations include rendering the art idea received from the server into HTML and displaying it on the interface. The output is the art idea presented to the user.

[0296] Step 12:

[0297] The user selects an art idea and then begins to create it. The input is the art idea presented to the user. The specific action is to select the one they like best from the multiple art ideas presented. The output is the selected art idea.

[0298] Step 13:

[0299] After creating an artwork, the user feeds the results back to the system via their device. The input is an image file of the artwork and comments. Specifically, the user uploads the image file of the artwork and comments and sends them to the server. The output is the feedback data sent to the server.

[0300] Step 14:

[0301] The emotion engine acquires emotion data in real time while the user is viewing the design and sends it to the server. Inputs include the user's facial expressions, click patterns, and operation logs. Specifically, it captures the user's facial expressions with a camera and sends them to an API for emotion analysis. The output is analyzed emotion data.

[0302] Step 15:

[0303] The server analyzes the emotion data and uses it as feedback for the next generation of art ideas. The input is the emotion data sent from the emotion engine. The specific operation is to log the emotion data and reflect it in the generative AI model. The output is an improved generative algorithm.

[0304] As described above, the server, terminal, user, and emotion engine work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[0305] (Application example 2)

[0306] 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."

[0307] Modern security systems require users to continuously monitor surveillance camera footage for long periods of time, which requires a huge amount of effort and may result in missed alerts. Furthermore, when detecting anomalies, it is difficult to provide appropriate feedback based on the user's emotions and psychological state, making it difficult to properly highlight important or high-risk scenes. Therefore, there is a need for an efficient and effective surveillance system that improves the user experience.

[0308] 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.

[0309] In this invention, the server includes means for collecting feature data, means for analyzing the collected feature data to extract trends, means for generating new designs based on the trends, means for presenting the generated designs, means for receiving input data from a user and providing designs based on the input data, means for analyzing user emotions in real time, and means for highlighting important scenes based on the analyzed emotion data. This makes it possible to improve the user experience and increase the efficiency of monitoring operations by detecting anomalies while taking the user's emotional state into consideration and highlighting important scenes and high-risk scenes.

[0310] "Feature data" refers to basic information that the system uses for analysis and generation, including, for example, image data, text data, and social media posts.

[0311] "Analysis" is the process of processing collected data to extract trends, using natural language processing and image processing algorithms to find important features and patterns.

[0312] "Trends" are specific patterns or themes in the data that are derived through analysis and serve as the basis for generating new designs.

[0313] "Generation" is the process of generating new designs and ideas based on trend data, and is done using generative AI models.

[0314] "Emotional data" refers to psychological and emotional information obtained from users' facial expressions, click patterns, operation logs, etc.

[0315] "User Interface" means the graphical operating screen through which a user can access the system and input their desired style and theme.

[0316] The present invention relates to an advanced security feedback system that analyzes user emotional data in real time and highlights important scenes, thereby enabling security operations to be carried out efficiently and effectively.

[0317] Server Processing

[0318] The server first collects feature data, which includes surveillance footage, click patterns, operation logs, etc. This feature data is collected using video analysis technology such as OpenCV and web scraping technology.

[0319] After feature data is collected, it undergoes a data cleaning process and is converted into a format suitable for analysis. The cleaned data is used for trend analysis and generative AI models are run to detect anomalies and patterns.

[0320] The generated anomaly and pattern information is stored in a database to create a baseline for highlighting important scenes based on the user's emotional data, which can then be used for future analyses.

[0321] Emotion engine processing

[0322] The emotion engine analyzes users' emotions in real time as they view surveillance footage. It collects data to identify the emotions of users through facial expression recognition and click pattern analysis. The collected emotion data is then sent to a server for analysis.

[0323] Terminal handling

[0324] The terminal provides an interface for the security feedback system, allowing users to easily view surveillance footage and see scenes highlighted if anomalies are detected. The terminal includes a field for users to input their desired surveillance style or theme.

[0325] The input data is sent to the server and used for real-time analysis. The device then presents the highlighted scene sent from the server to the user. While the user is viewing the presented scene, the emotion engine analyzes the emotion data in real time and uses it as feedback for the next analysis.

[0326] User Action

[0327] The user first operates the terminal interface and logs into the system. Then, they input their monitoring preferences and requirements. The system then performs analysis based on the user's preferences and optimizes anomaly detection.

[0328] While the user is viewing the video, the emotion engine analyzes the user's emotions, and the data is sent to the server. This allows the server to highlight important or high-risk scenes. The user checks the highlighted scenes presented by the system and provides feedback. This improves the accuracy of analysis from the next time onwards, enabling more efficient monitoring.

[0329] Examples of concrete examples and prompts

[0330] As a concrete example, suppose a user is watching a surveillance camera and suddenly sees a scene with a lot of movement, and expresses surprise. The system stores this scene so that similar anomalies are noticed in future surveillance. An example of a prompt sentence is as follows:

[0331] "Detect any anomalies in this camera feed and highlight important scenes in real time, taking into account the user's emotions."

[0332] In this way, the system of the present invention contributes to improving the efficiency of security analysis and monitoring work based on user emotions through cooperation between the server, terminal, user, and emotion engine.

[0333] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0334] Step 1:

[0335] The server collects feature data from the surveillance camera footage. In this process, it captures the surveillance footage frame by frame and extracts the image data using OpenCV. The input is the surveillance camera video feed, and the output is the feature data (image frames).

[0336] Step 2:

[0337] The server cleans the collected feature data and converts it into a format suitable for analysis. Specifically, it performs noise removal and image standardization. The input is the feature data obtained in step 1, and the output is the cleaned data.

[0338] Step 3:

[0339] The server analyzes the cleaned data to detect anomalies. It uses the AnomalyDetection library to identify specific patterns or anomalies in the frames. The input is the cleaned data, and the output is the frame information containing the anomaly and its location data.

[0340] Step 4:

[0341] The terminal uses the analyzed data to highlight abnormalities in real time on the user interface, notifying the user by, for example, surrounding the area where an abnormality is detected with a red frame. The input is the abnormal data sent from the server, and the output is a user interface with the abnormality highlighted.

[0342] Step 5:

[0343] The emotion engine analyzes the user's facial expressions and operation patterns to collect emotional data in real time. It uses the EmotionRecognition library to collect facial expression data and also analyzes click and operation logs. The input is the user's facial image and operation log, and the output is the user's emotional data.

[0344] Step 6:

[0345] The server re-highlights abnormal scenes based on the emotion data. It analyzes the user's emotion data and prioritizes highlighting scenes that evoke emotions such as "surprise" and "anxiety." The input is emotion data and abnormal data, and the output is scenes that are highlighted based on the emotion feedback.

[0346] Step 7:

[0347] The user reviews the highlighted scenes and provides feedback as needed. The reviewed scenes and emotional feedback are input into the system to help with the next analysis. The input is the user's feedback data, and the output is an updated analysis algorithm.

[0348] In this way, the server, terminal, and emotion engine work together to detect and highlight anomalies based on the user's emotions, thereby realizing an efficient and effective security feedback system.

[0349] 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.

[0350] 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.

[0351] 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.

[0352] [Second embodiment]

[0353] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0354] 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.

[0355] 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).

[0356] 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.

[0357] 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.

[0358] 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).

[0359] 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.

[0360] 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.

[0361] 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.

[0362] 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.

[0363] 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.

[0364] 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."

[0365] The present invention is a system in which a server, terminals, and users work together to analyze contemporary art data and provide artists with new ideas for creating art. The processing for each role will be explained in detail below.

[0366] Server Processing

[0367] The server first collects data about contemporary art from various sources. For example, it uses APIs and web scraping techniques from social media, online art galleries, and specialized magazines to collect posts and articles tagged with "contemporary art." This data includes image data, text data, and more.

[0368] The collected data is stored in a database and then undergoes a data cleaning process. Data cleaning removes unnecessary information that creates noise and converts it into a format suitable for analysis. For example, advertisements and irrelevant content are removed from text data, and image data is converted into a unified format.

[0369] Next, the cleaned data undergoes trend analysis. The server uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data, and image processing algorithms to analyze popular colors, shapes, and compositions. This trend data is then used to run a generative AI to generate ideas for new artworks. These ideas are then stored in a database.

[0370] Terminal handling

[0371] The terminal provides a user interface (GUI) for users to access the server through which they can browse contemporary art trend information and generated ideas. The interface also includes fields for artists to input their desired art style and theme.

[0372] When the user inputs their preferences into the device, the data is sent to the server. The server regenerates the best art ideas based on the user's input data and sends them to the device. The user can then review the provided ideas and select one.

[0373] User Action

[0374] First, users operate the terminal interface to log in to the system. Then, they input their requirements and preferences for their art creation. For example, if a user requests an "abstract style" or "art themed around natural elements," that data is sent to the server.

[0375] The user reviews the ideas sent by the server and selects the one they like best. They then create art based on the selected idea and provide feedback to the system via their device. The feedback evaluates how useful the selected idea was in the actual creation, and the server uses this feedback to improve the performance of the generative AI and the quality of the ideas it provides.

[0376] A specific example is the following flow:

[0377] The user inputs into the interface their desire to create a "nature" themed artwork.

[0378] The terminal transmits this data to the server.

[0379] The server runs a generative AI using trend data related to the "nature" theme to generate ideas for "nature-themed abstract art."

[0380] The generated ideas are sent to the terminal and presented to the user.

[0381] Users select an idea and then work on creating it. After creation, the results are fed back to the user.

[0382] In this way, a system is provided in which servers, terminals, and users work together to help create innovative and meaningful art.

[0383] The processing flow will be explained below.

[0384] Step 1:

[0385] The server collects feature data about art from data sources such as social media, online art galleries, and specialized magazines, including by using APIs to retrieve tagged posts and web scraping techniques to download articles and images.

[0386] Step 2:

[0387] The server stores the collected data in a database. Before storing it, it performs a data cleaning process to remove unnecessary information that creates noise from the text and images. For example, it removes advertisements and irrelevant comments from the text data, and converts image data into a unified format (JPEG, PNG, etc.).

[0388] Step 3:

[0389] The server analyzes the cleaned data and uses natural language processing (NLP) techniques to extract frequent keywords and themes, as well as image processing algorithms to analyze popular colors, shapes, and compositions, thereby identifying trends in contemporary art.

[0390] Step 4:

[0391] The server runs a generative AI based on the analyzed trend data to generate ideas for new artworks. The generative AI uses the learned model to propose designs based on the trends.

[0392] Step 5:

[0393] The server stores the generated art ideas in a database and makes them accessible to users through a user interface.

[0394] Step 6:

[0395] The terminal provides a graphical user interface (GUI) for users to access, through which they can view contemporary art trend information and generated ideas.

[0396] Step 7:

[0397] Users input their desired art style and theme into the interface via their device, such as "abstract style" or "art with a natural element theme."

[0398] Step 8:

[0399] The device sends the user's input data to the server, which analyzes the data and regenerates the optimal art idea based on the user's wishes.

[0400] Step 9:

[0401] The server sends the generated ideas to the terminal, which displays them on the user interface. The user can review the ideas provided and select the one they like best.

[0402] Step 10:

[0403] Users create art based on the selected idea, and after completing the work, they input feedback on the finished product into the system via their terminal.

[0404] Step 11:

[0405] The server receives user feedback and analyzes it to improve the performance of the generative AI and the quality of the ideas it provides. This feedback cycle improves the accuracy of the system and helps with future idea generation.

[0406] Through these steps, a system is realized in which the server, terminals, and users work together to analyze trends in contemporary art and help create innovative and meaningful art.

[0407] Example 1

[0408] 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."

[0409] In the creation of contemporary art, it is important for artists to gain new ideas and inspiration, but currently this requires a lot of time and effort. It is also difficult to integrate and analyze information obtained from different data sources. For this reason, a system that allows artists to gain new art ideas effectively and quickly is needed.

[0410] 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.

[0411] In this invention, the server includes means for collecting data related to contemporary art, means for storing the collected data in a database and cleaning the data to remove noise, trend analysis means using natural language processing technology to analyze the cleaned data and extract frequently occurring keywords and themes, trend analysis means using image processing technology to analyze image data and extract colors and shapes, means for operating a generative AI model that generates ideas for new artworks based on the trend data, means for storing the generated art ideas in a database and displaying them through a user interface, and means for receiving input data from a user and re-operating the generative AI model based on the input data to provide optimal art ideas, thereby enabling artists to efficiently and quickly obtain new art ideas.

[0412] "Data collection" refers to the acquisition of data related to contemporary art from multiple sources on the Internet.

[0413] A "database" is a repository where collected data is stored so that it can be accessed and analyzed later.

[0414] "Data cleaning" refers to the process of removing unnecessary noise and irrelevant information from collected data and converting it into a form suitable for analysis.

[0415] "Natural Language Processing (NLP)" is a technique used to extract frequent keywords and themes from text data.

[0416] "Image processing" is a technology that analyzes image data and extracts features such as color and shape.

[0417] "Trend analysis" is an analytical method that uses clean data to detect frequently occurring keywords, themes, colors, shapes, etc. in contemporary art.

[0418] A "generative AI model" is an artificial intelligence model that generates new ideas and works based on given data and prompts.

[0419] A "user interface (GUI)" is a visual interface that allows a user to access a system, input data, and view results.

[0420] "Input data" refers to the information that a user inputs into the system, such as the desired art style and theme.

[0421] "Feedback" means that the user uses the generated ideas to create art and then returns the results and evaluation to the system.

[0422] The present invention is a system in which a server, terminals, and users work together to analyze contemporary art data and provide artists with new ideas for creating art. The processing for each role will be explained in detail below.

[0423] Server Processing

[0424] The server first collects data about contemporary art from multiple sources on the Internet. For example, the server uses social media APIs (e.g., Instagram API) to retrieve posts tagged with "contemporary art." It also uses web scraping technologies (e.g., BeautifulSoup and Scrapy) to collect data from websites of online art galleries and specialized magazines. The collected data includes image data and text data.

[0425] The collected data is first stored in a database. It then undergoes a data cleaning process. During data cleaning, noise information is removed from the collected data and it is converted into a format suitable for analysis. For example, advertisements and irrelevant information are removed from text data using regular expressions, and image data is standardized into JPEG format using PIL (Python Imaging Library) and converted to 800x800 pixels.

[0426] The server then performs trend analysis using the cleaned data. Here, natural language processing (NLP) techniques (e.g., SpaCy and NLTK) are used to extract frequently occurring keywords and themes from the text data. Image processing techniques (e.g., OpenCV) are also used to analyze popular colors and shapes from the image data. Specifically, frequently occurring keywords such as "abstract," "natural," and "modern" are extracted from the text data, and the image data confirms that "blue" and "circular composition" are common.

[0427] Based on the trend data, the server runs a generative AI model (e.g., GPT-3 or DALL-E) to generate ideas for new artworks. The prompts are in the form of "Generate ideas for abstract, blue artwork incorporating natural elements." The generated ideas are stored in a database and displayed through a user interface.

[0428] Terminal handling

[0429] The terminal provides a user interface (GUI) for users to access the system. The GUI has fields for displaying collected contemporary art trend information and generated art ideas. Through this interface, users can input their desired art style and theme to the server.

[0430] When a user inputs the desired art style or theme into the device interface, the data is sent by the device to the server. Here, the data can be sent asynchronously using Ajax technology. For example, if a user wants art ideas with the theme "abstract and natural," the data is sent to the server.

[0431] The server then runs the generative AI model again based on the user's input data, generates optimal art ideas, and sends them to the device. These generated art ideas are displayed on the device's interface, allowing the user to select the best one.

[0432] User Action

[0433] Users log in to the system through a terminal interface. When logging in, the user's authentication information is transmitted encrypted for security reasons. Next, the user enters their requests and wishes regarding their art creation. For example, if they want art in an "abstract style" or with a "natural element" theme, they enter that into the interface.

[0434] The user reviews the art ideas sent by the server and selects the one they like best. The user then creates the art based on the selected idea. After completing the creation, the results are fed back to the system. This feedback evaluates how useful the selected idea was in the actual creation. The server uses this feedback to improve the performance of the generative AI model and the quality of the ideas it provides.

[0435] As described above, a system is realized in which the server, terminals, and users work together to efficiently and quickly generate new art ideas.

[0436] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0437] Step 1:

[0438] The server collects data about contemporary art from social media, online art galleries, and specialized magazine websites using tools such as the Instagram API, BeautifulSoup, and Scrapy. Specifically, the server calls the Instagram API to retrieve the 10,000 most recent posts tagged "contemporaryart." It also uses Scrapy to retrieve information about new exhibitions from specific online art gallery pages.

[0439] Input: Contemporary art data collected via API and web scraping

[0440] Output: Collected text and image data

[0441] Step 2:

[0442] The server stores the collected data in a database and then performs a data cleaning process. Specifically, it uses regular expressions to remove advertisements and irrelevant information from the collected text data, and then uses PIL (Python Imaging Library) to standardize the image data into JPEG format and convert it to 800x800 pixels.

[0443] Input: Collected text and image data

[0444] Output: Clean text and image data

[0445] Step 3:

[0446] The server uses the cleaned data to perform trend analysis. Specifically, it uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data. SpaCy and NLTK are used for NLP. It also uses OpenCV to analyze popular colors and shapes from image data. For example, it extracts frequently occurring keywords such as "abstract," "nature," and "modern" from the text data, and determines that "blue" and "circular composition" are common in the image data.

[0447] Input: Clean text and image data

[0448] Output: Extracted frequent keywords, themes, colors, shapes, and other trend data

[0449] Step 4:

[0450] The server runs a generative AI model based on the trend data to generate ideas for new artworks. Specifically, the generative AI is run using GPT-3 and DALL-E, and generates ideas by entering the prompt "Generate ideas for abstract blue artwork incorporating natural elements." The generated ideas are stored in a database.

[0451] Input: Trend data and prompt statement

[0452] Output: Generated art ideas

[0453] Step 5:

[0454] The terminal receives and displays the generated art ideas from the server through a user interface provided to the user, which also includes a field for the artist to input their desired art style and theme.

[0455] Input: Generated art ideas

[0456] Output: The idea displayed in the user interface

[0457] Step 6:

[0458] The user inputs the desired art style or theme into the device interface, and the data is sent to the server. For example, if the user wants an "abstract and natural" themed art idea, the data is sent to the server.

[0459] Input: User-entered art style and theme

[0460] Output: Desired data to be sent to the server

[0461] Step 7:

[0462] The server re-runs the generative AI model based on the user's input data, generates optimal art ideas, and sends them to the device. For example, the AI ​​can generate ideas using new prompts that match the user's wishes.

[0463] Input: User's desired data and prompt

[0464] Output: Best art ideas

[0465] Step 8:

[0466] The terminal displays the best art ideas sent from the server on the user interface, and the user can review the provided ideas and select the one they like best.

[0467] Input: The best art ideas sent by the server

[0468] Output: The best idea displayed in the user interface

[0469] Step 9:

[0470] The user creates an artwork based on the selected idea and provides feedback to the system via the terminal about the results. The feedback includes an evaluation of how the selected idea was useful in the actual creation.

[0471] Input: Results and feedback on the selected ideas

[0472] Output: Feedback data sent to the server

[0473] Step 10:

[0474] The server analyzes user feedback and uses it to improve the performance of the generative AI model and the quality of the ideas it provides, thereby increasing the accuracy and usefulness of the entire system.

[0475] Input: Feedback data from users

[0476] Output: Improved generative AI models and the quality of generated ideas

[0477] (Application example 1)

[0478] 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."

[0479] In the creation of modern art, artists need to collect and analyze vast amounts of data to come up with new ideas. It is also difficult to quickly grasp the latest trends. Furthermore, there are few ways for artists to obtain specific ideas that fit their own style or theme. As a result, there is a need for an efficient system to enhance creativity.

[0480] 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.

[0481] In this invention, the server includes means for collecting feature data, means for analyzing the collected feature data to extract trends, means for generating new designs based on the trends, means for presenting the generated designs, means for receiving input data from a user and providing designs based on the input data, and means for operating a generative AI model based on a theme and style specified by the user to generate art ideas. This allows users to easily obtain new art ideas that meet their individual needs and enhance their creativity. It also allows users to efficiently grasp trends in contemporary art and utilize them in their creations.

[0482] "Feature data" refers to information collected for analysis, such as text data, image data, and social media posts related to the contemporary art in question.

[0483] "Analysis" refers to the process of extracting trends from collected feature data and converting it into meaningful information.

[0484] "Trends" are common themes or patterns that emerge from the analyzed data.

[0485] "Designs" refer to artworks and concepts created using generative AI models.

[0486] A "generative AI model" is a program that uses machine learning algorithms to automatically generate new design and art ideas.

[0487] "Presenting" means displaying the generated designs and ideas to the user.

[0488] A "user interface" is an interactive system that has screens and input fields that allow a user to operate the system.

[0489] "Feedback" refers to information that allows a user to evaluate the generated design and its proposals and provide their opinions and suggestions for improvement to the system.

[0490] "Generative means" refers to the functions and techniques for generating new designs and ideas based on the analyzed trends.

[0491] "Trend data" refers to information about distinctive themes or patterns that emerge as a result of analysis.

[0492] "Behavior" refers to the process by which the generative AI model generates art ideas based on themes and styles specified by the user.

[0493] This system allows the server, terminals, and users to cooperate in collecting and analyzing data on contemporary art, and provides ideas for new art creation. This system has the function of collecting and analyzing characteristic data, extracting trends, generating designs based on that data, and presenting them to users.

[0494] The server collects data about contemporary art from various sources, including social media, online art galleries, and specialized magazines, using APIs and web scraping techniques. The collected data is then stored in a database and undergoes data cleaning, during which unnecessary information is removed and the data is converted into a format suitable for analysis.

[0495] The server then analyzes the cleaned data for trends. This analysis uses natural language processing (NLP) techniques and image processing algorithms to extract frequently occurring keywords, themes, popular colors, shapes, and compositions. The results of this analysis are fed into a generative AI model to generate new art ideas. These ideas are then stored in a database.

[0496] The terminal provides a user interface (UI) for users to access the system. The UI allows users to view trend information and generated ideas. It also includes a field for artists to input their desired art style and theme. For example, if a user inputs their desire to create a "nature"-themed artwork, that data is sent to the server.

[0497] Based on the user's input, the server regenerates optimal art ideas and sends them to the device. The user can browse these ideas and select one. They then create art based on the selected idea and provide feedback on the results. The feedback is an evaluation of how useful the idea was in actual production, and the server uses this feedback to improve the performance of the generation AI and the quality of the ideas it provides.

[0498] Hardware and software used

[0499] On the server side, Amazon Web Services (AWS) is used for data storage and analysis, Google Cloud Natural Language API is used for natural language processing, OpenCV is used for image processing, and OpenAI's GPT-4 model is used for generative AI.

[0500] Specific examples

[0501] If a user wishes to create a piece of art with a "nature" theme, they input their request through the device's UI. Based on the input, the server inputs the following prompt into the generative AI model:

[0502] "Prompt for generative AI model: Generate ideas for nature-themed abstract art."

[0503] Based on this prompt, the generative AI model generates multiple art ideas, including specific suggestions for color, composition, and theme. The generated ideas are displayed on the device's UI, allowing the user to use them as reference for creating their own artwork.

[0504] As a result, the entire process from collecting and analyzing feature data to generating new art ideas and presenting them to users is automated, realizing a system that supports artists' creative activities.

[0505] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0506] Step 1:

[0507] The server collects data related to contemporary art. Specifically, it uses APIs and web scraping technologies from social media, online art galleries, and specialized magazines to collect text and image data. The input is data from API calls and web scraping, and the output is the collected raw data.

[0508] Step 2:

[0509] The server stores the collected data in a database and performs a data cleaning process. Specifically, it removes unnecessary information that creates noise and converts it into a format suitable for analysis. For example, it removes advertisements and irrelevant content and converts image data into a unified format. The input is the raw data obtained in step 1, and the output is clean data.

[0510] Step 3:

[0511] The server then uses the cleaned data to analyze trends. It uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data, and image processing algorithms to analyze popular colors, shapes, compositions, etc. The input is the cleaned data generated in step 2, and the output is trend data resulting from the analysis.

[0512] Step 4:

[0513] The server runs a generative AI model based on the analyzed trend data to generate new art ideas. Specifically, the trend data is input into the generative AI as prompts to generate ideas. The generated ideas are stored in a database. The input is trend data, and the output is the generated art idea.

[0514] Step 5:

[0515] The terminal provides a user interface, allowing users to access the system. The UI displays generated art ideas and trend information, and includes a field where artists can input their desired style and theme. The input is data based on the user's preferences and interests, and the output is new art ideas based on that data.

[0516] Step 6:

[0517] The user operates the terminal interface and inputs the desired style or theme, for example, "nature-themed artwork." The input is the theme or style the user inputs, and the output is the corresponding prompt.

[0518] Step 7:

[0519] The server regenerates optimal art ideas based on the user's input data and sends them to the device. The server creates prompts using the user's input theme and runs the generative AI model again. The input is the user's theme and style data, and the output is updated art ideas.

[0520] Step 8:

[0521] The user reviews the provided ideas and selects the one they like best. They then create art based on the selected idea and provide feedback to the system. The input is the selected art idea, and the output is the resulting feedback data.

[0522] Step 9:

[0523] The server analyzes user feedback to improve the performance of the generative AI and the quality of the ideas it provides. Specifically, it analyzes user evaluations and uses them as training data for the generative AI. The input is user feedback data, and the output is an updated generative AI model.

[0524] The above process provides a support system that allows servers, terminals, and users to work together to create innovative and meaningful art.

[0525] 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.

[0526] This invention is a system in which a server, a terminal, a user, and an emotion engine work together to analyze contemporary art data, provide artists with new ideas for creating art, and recognize the user's emotions and reflect them in the design. The processing for each role is explained in detail below.

[0527] Server Processing

[0528] The server first collects feature data about contemporary art from various sources, such as social media, online art galleries, and specialized magazines, using APIs and web scraping techniques to collect posts and articles tagged with "contemporary art." This data includes image data and text data.

[0529] The collected data is stored in a database and then undergoes a data cleaning process. Data cleaning removes unnecessary information that creates noise and converts it into a format suitable for analysis. For example, advertisements and irrelevant content are removed from text data, and image data is converted into a unified format.

[0530] Next, the cleaned data undergoes trend analysis. The server uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data, and image processing algorithms to analyze popular colors, shapes, and compositions. This trend data is then used to run a generative AI to generate ideas for new artworks. These ideas are then stored in a database.

[0531] Emotion engine processing

[0532] The emotion engine analyzes facial expressions, click patterns, operation logs, etc. as users browse designs, recognizing emotions in real time. Emotional data is acquired using a camera and microphone built into the user interface.

[0533] The recognized emotion data is sent to a server and used as feedback for the design the user has viewed, allowing the generative AI to provide designs that take the user's emotions into consideration when generating designs from then on.

[0534] Terminal handling

[0535] The terminal provides a graphical user interface (GUI) for users to access the server and browse contemporary art trend information and generated ideas. The interface also includes fields for artists to input their desired art style and theme.

[0536] When the user inputs their preferences into the device, the data is sent to the server. The server regenerates the best art ideas based on the user's input data and sends them to the device. The user can then review the provided ideas and select one.

[0537] User Action

[0538] First, users operate the terminal interface to log in to the system. Then, they input their requirements and preferences for their art creation. For example, if a user requests an "abstract style" or "art themed around natural elements," that data is sent to the server.

[0539] The user reviews the ideas sent from the server and selects the one they like best. They then create art based on the selected idea and feed the results back to the system via their device. At this time, the user's emotional data, as recognized by the emotion engine, is also fed back.

[0540] A specific example is the following flow:

[0541] The user inputs into the interface their desire to create an artwork with the theme of "inspirational landscapes."

[0542] The terminal transmits this data to the server.

[0543] The server runs a generative AI using trend data related to the theme of "inspirational landscapes" to generate ideas for "abstract art with the theme of inspiring landscapes."

[0544] The generated ideas are sent to the terminal and presented to the user.

[0545] While the user is browsing the presented ideas, the emotion engine recognizes the user's emotions in real time and transmits this information to the server.

[0546] Users select an idea and then work on creating it. After creation, the results are given along with emotional feedback.

[0547] In this way, a system is provided in which the server, terminal, user and emotion engine work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[0548] The processing flow will be explained below.

[0549] Step 1:

[0550] The server collects feature data from data sources such as social media, online art galleries, and specialty magazines, using APIs to retrieve tagged art posts and web scraping techniques to download articles and images.

[0551] Step 2:

[0552] The server stores the collected feature data in a database. Before storing, the data is cleaned to remove unnecessary information from the text and images. For example, advertisements and irrelevant comments are removed from the text data, and image data is converted into a unified format.

[0553] Step 3:

[0554] The server analyzes the cleaned data and uses natural language processing (NLP) techniques to extract frequent keywords and themes, as well as image processing algorithms to analyze popular colors, shapes, and compositions, thereby identifying trends in contemporary art.

[0555] Step 4:

[0556] The server runs a generative AI based on the analyzed trend data to generate ideas for new artworks. The generative AI uses the learned model to propose designs based on the trends.

[0557] Step 5:

[0558] The server stores the generated art ideas in a database and makes them accessible to users through a user interface.

[0559] Step 6:

[0560] The terminal provides a graphical user interface (GUI) for users to access, through which they can view contemporary art trend information and generated ideas.

[0561] Step 7:

[0562] The user interface is equipped with an emotion engine that recognizes emotions in real time by analyzing facial expressions, click patterns, and operation logs as users browse designs.

[0563] Step 8:

[0564] Users input their desired art style and theme into the interface via their device, such as "abstract style" or "art with a natural element theme."

[0565] Step 9:

[0566] The device sends the user's input data to the server, which then combines and analyzes this data with the emotional data recognized by the emotion engine to regenerate optimal art ideas based on the user's wishes.

[0567] Step 10:

[0568] The server sends the generated ideas to the terminal, which displays them on the user interface. The user can review the ideas provided and select the one they like best.

[0569] Step 11:

[0570] The user creates art based on the selected idea. After completing the work, the user inputs feedback on the finished product into the system via a terminal. At this time, the user's emotional data recognized by the emotion engine is also fed back.

[0571] Step 12:

[0572] The server receives user feedback and emotional data, analyzes it, and improves the performance of the AI ​​and the quality of the ideas it provides. This feedback cycle improves the system's accuracy and helps with future idea generation.

[0573] In this way, a system is provided in which the server, terminal, user and emotion engine work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[0574] Example 2

[0575] 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."

[0576] In the creation of contemporary art, it is extremely difficult for artists to find new ideas and create designs that match the user's emotions. Furthermore, generating personalized designs based on individual users' emotions requires advanced analytical technology and real-time emotion recognition, which conventional systems often cannot adequately address. Therefore, there is a need to analyze contemporary art data and provide new artwork ideas that take the user's emotions into consideration.

[0577] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0578] In this invention, the server includes means for collecting feature data, means for analyzing the collected feature data to extract trends, means for operating a generative AI model to generate new designs based on the trends, means for analyzing user emotion data in real time and using the data as feedback, means for providing a user interface for the user to input a desired style or theme, means for presenting the generated designs to the user, and means for receiving feedback from the user, analyzing the feedback, and using the feedback to improve the generation means. This makes it possible to efficiently analyze a variety of contemporary art data and provide personalized designs that match the user's emotions.

[0579] "Feature data" refers to information such as image data and text data related to contemporary art, including data collected from social media and online galleries.

[0580] A "generative AI model" refers to an algorithm that uses large-scale language models and deep learning algorithms to generate ideas for new designs and artworks.

[0581] An "emotion engine" refers to a system that analyzes a user's facial expressions, click patterns, operation logs, etc. to recognize emotions in real time and analyzes that data.

[0582] "User Interface" refers to a graphical interface that allows a user to input a desired style or theme and view the generated design.

[0583] "Trend data" refers to information such as frequently occurring keywords, themes, colors, shapes, and compositions extracted by analyzing collected feature data.

[0584] "Feedback" refers to the opinions and reactions users provide to the generated designs, including emotional data obtained through the emotion engine.

[0585] This invention is a system in which a server, a terminal, a user, and an emotion engine work together to analyze contemporary art data, provide artists with new ideas for creating art, and recognize the user's emotions and reflect them in the design. The processing for each role is explained in detail below.

[0586] Server Processing

[0587] The server first collects feature data about contemporary art from social media, online art galleries, and specialized magazines using APIs and web scraping. The APIs of Instagram and online galleries are used as the primary data source. For example, the server uses the Instagram API to collect images tagged with "contemporary art" and their captions.

[0588] The collected data is temporarily stored in a database such as MySQL or MongoDB, after which it undergoes a data cleaning process: regular expressions in Python are used to remove advertisements and irrelevant content from the text data, and image data is converted into a unified format (e.g., JPEG).

[0589] Based on the clean data, the server uses natural language processing (NLP) techniques to extract frequent keywords and themes from the text data, and Python libraries (NLTK and OpenCV) to analyze popular colors, shapes, and compositions from image data.

[0590] Based on the analysis results, the server runs a generative AI model to generate ideas for new artworks. For example, the generative AI model uses a large-scale language model such as GPT-4. The following prompt is input to the model:

[0591] Please generate ideas for artwork with the theme of "inspirational landscapes." Please also suggest specific colors, shapes, and compositions.

[0592] The generated ideas are then stored in the database.

[0593] Emotion engine processing

[0594] The emotion engine analyzes facial expressions, click patterns, operation logs, and other data as users browse designs to recognize emotions in real time. This is achieved using a camera and microphone built into the interface. For example, the camera captures the user's facial expressions, and the microphone acquires audio data. This data is sent to an API for emotion analysis (e.g., Microsoft Azure's Emotion API) and analyzed in real time.

[0595] The recognized emotion data is sent to the server and used as feedback data for the design the user viewed. This data will serve as the basis for providing designs that take the user's emotions into consideration when generating designs in the future.

[0596] Terminal handling

[0597] The terminal provides a graphical user interface (GUI) for users to access. This interface is likely to be implemented using React.js or Vue.js. Through this interface, users can access the server and view contemporary art trend information and generated art ideas. There is also a field for users to input their desired art style and theme.

[0598] Users input their desired style and theme into the interface, and the data is sent to the server via an AJAX request. The server generates new art ideas based on this data and sends the results to the device, where the user can view and select from the presented ideas.

[0599] User Action

[0600] Users first log in to the system through a terminal interface and enter their requirements and preferences for their art creation, such as specific preferences such as "abstract style" or "art themed around natural elements."

[0601] The user reviews the ideas sent from the server and selects the one they like best. After making their selection, they then create the actual artwork, and the results are fed back to the system via their device. At this time, the user's emotional data, as recognized by the emotion engine, is also fed back.

[0602] In this way, the system of the present invention enables the server, terminal, user, and emotion engine to work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[0603] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0604] Step 1:

[0605] The server collects feature data. As input, it obtains data about contemporary art from social media APIs and web scraping tools. This includes social media (e.g., posts tagged "contemporary art" using the Instagram API), online art galleries, and specialized magazines. Specifically, it sends API requests and receives the retrieved data in JSON format. The output is the collected image data and text data.

[0606] Step 2:

[0607] The server stores the collected data in a database. The input is the collected feature data. For example, the data is temporarily stored in a database such as MongoDB. Specifically, it executes an insert query on the database to save image data and text data. The output is the uncleaned data stored in the database.

[0608] Step 3:

[0609] The server performs data cleaning. The input is the raw feature data stored in the database. It uses Python regular expressions to remove advertisements and irrelevant content from the text data and converts the image data into a unified format (e.g., JPEG). Specific operations include filtering the text data and converting the image data format. The output is clean data converted into a format suitable for analysis.

[0610] Step 4:

[0611] The server performs trend analysis using cleaned data. The input is cleaned feature data. Natural language processing (NLP) techniques are used to extract frequently occurring keywords and themes from the text data, and Python libraries (NLTK and OpenCV) are used to analyze popular colors, shapes, and compositions from image data. Specifically, it performs text analysis using keyword extraction algorithms and image processing algorithms. The output is trend data extracted as frequently occurring keywords, themes, colors, shapes, compositions, etc.

[0612] Step 5:

[0613] The server runs a generative AI model based on the trend data to generate ideas for new artworks. The input is the trend data and a generative AI model (e.g., GPT-4 model). Specifically, the server gives the generative AI model the following prompt:

[0614] Please generate ideas for artwork with the theme of "inspirational landscapes." Please also suggest specific colors, shapes, and compositions.

[0615] The output is the generated new art idea.

[0616] Step 6:

[0617] The server saves the generated art ideas in a database. The input is the generated art ideas. The specific operation is to execute an insert query on the database to save the art ideas. The output is the art ideas saved in the database.

[0618] Step 7:

[0619] The terminal provides a user interface. The input is the art idea sent from the server and the user's input data. Through an interface built with React.js or Vue.js, it provides fields for the user to enter their desired art style and theme. Specific operations include rendering a form and accepting user input. The output is the user's input data.

[0620] Step 8:

[0621] A user operates the terminal interface to log in to the system. The input is the user's authentication information. Specifically, the user enters a username and password into the login form and sends an authentication request to the server. The output is the authenticated user's session information.

[0622] Step 9:

[0623] The user inputs their requirements and desires for their art creation. The input is the user's desired style or theme. Specific actions include entering "abstract style" or "art themed around natural elements" into a text box on the interface. The output is the user's input data, which is sent to the server.

[0624] Step 10:

[0625] The server generates new art ideas based on the user's wishes and sends them to the device. The inputs are the user's input data and the generative AI model. Specifically, the server provides the user's wishes as prompts to the generative AI model, which then generates new art ideas. The output is the new art ideas sent to the device.

[0626] Step 11:

[0627] The terminal presents the art ideas sent from the server to the user. The input is a new art idea. Specific operations include rendering the art idea received from the server into HTML and displaying it on the interface. The output is the art idea presented to the user.

[0628] Step 12:

[0629] The user selects an art idea and then begins to create it. The input is the art idea presented to the user. The specific action is to select the one they like best from the multiple art ideas presented. The output is the selected art idea.

[0630] Step 13:

[0631] After creating an artwork, the user feeds the results back to the system via their device. The input is an image file of the artwork and comments. Specifically, the user uploads the image file of the artwork and comments and sends them to the server. The output is the feedback data sent to the server.

[0632] Step 14:

[0633] The emotion engine acquires emotion data in real time while the user is viewing the design and sends it to the server. Inputs include the user's facial expressions, click patterns, and operation logs. Specifically, it captures the user's facial expressions with a camera and sends them to an API for emotion analysis. The output is analyzed emotion data.

[0634] Step 15:

[0635] The server analyzes the emotion data and uses it as feedback for the next generation of art ideas. The input is the emotion data sent from the emotion engine. The specific operation is to log the emotion data and reflect it in the generative AI model. The output is an improved generative algorithm.

[0636] As described above, the server, terminal, user, and emotion engine work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[0637] (Application example 2)

[0638] 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."

[0639] Modern security systems require users to continuously monitor surveillance camera footage for long periods of time, which requires a huge amount of effort and can lead to missed events. Furthermore, when detecting anomalies, it is difficult to provide appropriate feedback based on the user's emotions and psychological state, making it difficult to properly highlight important or high-risk scenes. Therefore, there is a need for an efficient and effective surveillance system that improves the user experience.

[0640] 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.

[0641] In this invention, the server includes means for collecting feature data, means for analyzing the collected feature data to extract trends, means for generating new designs based on the trends, means for presenting the generated designs, means for receiving input data from a user and providing designs based on the input data, means for analyzing user emotions in real time, and means for highlighting important scenes based on the analyzed emotion data. This makes it possible to improve the user experience and increase the efficiency of monitoring operations by detecting anomalies while taking the user's emotional state into consideration and highlighting important scenes and high-risk scenes.

[0642] "Feature data" refers to basic information that the system uses for analysis and generation, including, for example, image data, text data, and social media posts.

[0643] "Analysis" is the process of processing collected data to extract trends, using natural language processing and image processing algorithms to find important features and patterns.

[0644] "Trends" are specific patterns or themes in the data that are derived through analysis and serve as the basis for generating new designs.

[0645] "Generation" is the process of generating new designs and ideas based on trend data, and is done using generative AI models.

[0646] "Emotional data" refers to psychological and emotional information obtained from users' facial expressions, click patterns, operation logs, etc.

[0647] "User Interface" means the graphical operating screen through which a user can access the system and input their desired style and theme.

[0648] The present invention relates to an advanced security feedback system that analyzes user emotional data in real time and highlights important scenes, thereby enabling security operations to be carried out efficiently and effectively.

[0649] Server Processing

[0650] The server first collects feature data, which includes surveillance footage, click patterns, operation logs, etc. This feature data is collected using video analysis technology such as OpenCV and web scraping technology.

[0651] After feature data is collected, it undergoes a data cleaning process and is converted into a format suitable for analysis. The cleaned data is used for trend analysis and generative AI models are run to detect anomalies and patterns.

[0652] The generated anomaly and pattern information is stored in a database to create a baseline for highlighting important scenes based on the user's emotional data, which can then be used for future analyses.

[0653] Emotion engine processing

[0654] The emotion engine analyzes users' emotions in real time as they view surveillance footage. It collects data to identify the emotions of users through facial expression recognition and click pattern analysis. The collected emotion data is then sent to a server for analysis.

[0655] Terminal handling

[0656] The terminal provides an interface for the security feedback system, allowing users to easily view surveillance footage and see scenes highlighted if anomalies are detected. The terminal includes a field for users to input their desired surveillance style or theme.

[0657] The input data is sent to the server and used for real-time analysis. The device then presents the highlighted scene sent from the server to the user. While the user is viewing the presented scene, the emotion engine analyzes the emotion data in real time and uses it as feedback for the next analysis.

[0658] User Action

[0659] The user first operates the terminal interface and logs into the system. Then, they input their monitoring preferences and requirements. The system then performs analysis based on the user's preferences and optimizes anomaly detection.

[0660] While the user is viewing the video, the emotion engine analyzes the user's emotions, and the data is sent to the server. This allows the server to highlight important or high-risk scenes. The user checks the highlighted scenes presented by the system and provides feedback. This improves the accuracy of analysis from the next time onwards, enabling more efficient monitoring.

[0661] Examples of concrete examples and prompts

[0662] As a concrete example, suppose a user is watching a surveillance camera and suddenly sees a scene with a lot of movement, and expresses surprise. The system stores this scene so that similar anomalies are noticed in future surveillance. An example of a prompt sentence is as follows:

[0663] "Detect any anomalies in this camera feed and highlight important scenes in real time, taking into account the user's emotions."

[0664] In this way, the system of the present invention contributes to improving the efficiency of security analysis and monitoring work based on user emotions through cooperation between the server, terminal, user, and emotion engine.

[0665] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0666] Step 1:

[0667] The server collects feature data from the surveillance camera footage. In this process, it captures the surveillance footage frame by frame and extracts the image data using OpenCV. The input is the surveillance camera video feed, and the output is the feature data (image frames).

[0668] Step 2:

[0669] The server cleans the collected feature data and converts it into a format suitable for analysis. Specifically, it performs noise removal and image standardization. The input is the feature data obtained in step 1, and the output is the cleaned data.

[0670] Step 3:

[0671] The server analyzes the cleaned data to detect anomalies. It uses the AnomalyDetection library to identify specific patterns or anomalies in the frames. The input is the cleaned data, and the output is the frame information containing the anomaly and its location data.

[0672] Step 4:

[0673] The terminal uses the analyzed data to highlight abnormalities in real time on the user interface, notifying the user by, for example, surrounding the area where an abnormality is detected with a red frame. The input is the abnormal data sent from the server, and the output is a user interface with the abnormality highlighted.

[0674] Step 5:

[0675] The emotion engine analyzes the user's facial expressions and operation patterns to collect emotional data in real time. It uses the EmotionRecognition library to collect facial expression data and also analyzes click and operation logs. The input is the user's facial image and operation log, and the output is the user's emotional data.

[0676] Step 6:

[0677] The server re-highlights abnormal scenes based on the emotion data. It analyzes the user's emotion data and prioritizes highlighting scenes that evoke emotions such as "surprise" and "anxiety." The input is emotion data and abnormal data, and the output is scenes that are highlighted based on the emotion feedback.

[0678] Step 7:

[0679] The user reviews the highlighted scenes and provides feedback as needed. The reviewed scenes and emotional feedback are input into the system to help with the next analysis. The input is the user's feedback data, and the output is an updated analysis algorithm.

[0680] In this way, the server, terminal, and emotion engine work together to detect and highlight anomalies based on the user's emotions, thereby realizing an efficient and effective security feedback system.

[0681] 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.

[0682] 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.

[0683] 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.

[0684] [Third embodiment]

[0685] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0686] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0687] 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).

[0688] 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.

[0689] 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.

[0690] 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).

[0691] 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.

[0692] 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.

[0693] 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.

[0694] 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.

[0695] 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.

[0696] 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."

[0697] The present invention is a system in which a server, terminals, and users work together to analyze contemporary art data and provide artists with new ideas for creating art. The processing for each role will be explained in detail below.

[0698] Server Processing

[0699] The server first collects data about contemporary art from various sources. For example, it uses APIs and web scraping techniques from social media, online art galleries, and specialized magazines to collect posts and articles tagged with "contemporary art." This data includes image data, text data, and more.

[0700] The collected data is stored in a database and then undergoes a data cleaning process. Data cleaning removes unnecessary information that creates noise and converts it into a format suitable for analysis. For example, advertisements and irrelevant content are removed from text data, and image data is converted into a unified format.

[0701] Next, the cleaned data undergoes trend analysis. The server uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data, and image processing algorithms to analyze popular colors, shapes, and compositions. This trend data is then used to run a generative AI to generate ideas for new artworks. These ideas are then stored in a database.

[0702] Terminal handling

[0703] The terminal provides a user interface (GUI) for users to access the server through which they can browse contemporary art trend information and generated ideas. The interface also includes fields for artists to input their desired art style and theme.

[0704] When the user inputs their preferences into the device, the data is sent to the server. The server regenerates the best art ideas based on the user's input data and sends them to the device. The user can then review the provided ideas and select one.

[0705] User Action

[0706] First, users operate the terminal interface to log in to the system. Then, they input their requirements and preferences for their art creation. For example, if a user requests an "abstract style" or "art themed around natural elements," that data is sent to the server.

[0707] The user reviews the ideas sent by the server and selects the one they like best. They then create art based on the selected idea and provide feedback to the system via their device. The feedback evaluates how useful the selected idea was in the actual creation, and the server uses this feedback to improve the performance of the generative AI and the quality of the ideas it provides.

[0708] A specific example is the following flow:

[0709] The user inputs into the interface their desire to create a "nature" themed artwork.

[0710] The terminal transmits this data to the server.

[0711] The server runs a generative AI using trend data related to the "nature" theme to generate ideas for "nature-themed abstract art."

[0712] The generated ideas are sent to the terminal and presented to the user.

[0713] Users select an idea and then work on creating it. After creation, the results are fed back to the user.

[0714] In this way, a system is provided in which servers, terminals, and users work together to help create innovative and meaningful art.

[0715] The processing flow will be explained below.

[0716] Step 1:

[0717] The server collects feature data about art from data sources such as social media, online art galleries, and specialized magazines, including by using APIs to retrieve tagged posts and web scraping techniques to download articles and images.

[0718] Step 2:

[0719] The server stores the collected data in a database. Before storing it, it performs a data cleaning process to remove unnecessary information that creates noise from the text and images. For example, it removes advertisements and irrelevant comments from the text data, and converts image data into a unified format (JPEG, PNG, etc.).

[0720] Step 3:

[0721] The server analyzes the cleaned data and uses natural language processing (NLP) techniques to extract frequent keywords and themes, as well as image processing algorithms to analyze popular colors, shapes, and compositions, thereby identifying trends in contemporary art.

[0722] Step 4:

[0723] The server runs a generative AI based on the analyzed trend data to generate ideas for new artworks. The generative AI uses the learned model to propose designs based on the trends.

[0724] Step 5:

[0725] The server stores the generated art ideas in a database and makes them accessible to users through a user interface.

[0726] Step 6:

[0727] The terminal provides a graphical user interface (GUI) for users to access, through which they can view contemporary art trend information and generated ideas.

[0728] Step 7:

[0729] Users input their desired art style and theme into the interface via their device, such as "abstract style" or "art with a natural element theme."

[0730] Step 8:

[0731] The device sends the user's input data to the server, which analyzes the data and regenerates the optimal art idea based on the user's wishes.

[0732] Step 9:

[0733] The server sends the generated ideas to the terminal, which displays them on the user interface. The user can review the ideas provided and select the one they like best.

[0734] Step 10:

[0735] Users create art based on the selected idea, and after completing the work, they input feedback on the finished product into the system via their terminal.

[0736] Step 11:

[0737] The server receives user feedback and analyzes it to improve the performance of the generative AI and the quality of the ideas it provides. This feedback cycle improves the accuracy of the system and helps with future idea generation.

[0738] Through these steps, a system is realized in which the server, terminals, and users work together to analyze trends in contemporary art and help create innovative and meaningful art.

[0739] Example 1

[0740] 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."

[0741] In the creation of contemporary art, it is important for artists to gain new ideas and inspiration, but currently this requires a lot of time and effort. It is also difficult to integrate and analyze information obtained from different data sources. For this reason, a system that allows artists to gain new art ideas effectively and quickly is needed.

[0742] 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.

[0743] In this invention, the server includes means for collecting data related to contemporary art, means for storing the collected data in a database and cleaning the data to remove noise, trend analysis means using natural language processing technology to analyze the cleaned data and extract frequently occurring keywords and themes, trend analysis means using image processing technology to analyze image data and extract colors and shapes, means for operating a generative AI model that generates ideas for new artworks based on the trend data, means for storing the generated art ideas in a database and displaying them through a user interface, and means for receiving input data from a user and re-operating the generative AI model based on the input data to provide optimal art ideas, thereby enabling artists to efficiently and quickly obtain new art ideas.

[0744] "Data collection" refers to the acquisition of data related to contemporary art from multiple sources on the Internet.

[0745] A "database" is a repository where collected data is stored so that it can be accessed and analyzed later.

[0746] "Data cleaning" refers to the process of removing unnecessary noise and irrelevant information from collected data and converting it into a form suitable for analysis.

[0747] "Natural Language Processing (NLP)" is a technique used to extract frequent keywords and themes from text data.

[0748] "Image processing" is a technology that analyzes image data and extracts features such as color and shape.

[0749] "Trend analysis" is an analytical method that uses clean data to detect frequently occurring keywords, themes, colors, shapes, etc. in contemporary art.

[0750] A "generative AI model" is an artificial intelligence model that generates new ideas and works based on given data and prompts.

[0751] A "user interface (GUI)" is a visual interface that allows a user to access a system, input data, and view results.

[0752] "Input data" refers to the information that a user inputs into the system, such as the desired art style and theme.

[0753] "Feedback" means that the user uses the generated ideas to create art and then returns the results and evaluation to the system.

[0754] The present invention is a system in which a server, terminals, and users work together to analyze contemporary art data and provide artists with new ideas for creating art. The processing for each role will be explained in detail below.

[0755] Server Processing

[0756] The server first collects data about contemporary art from multiple sources on the Internet. For example, the server uses social media APIs (e.g., Instagram API) to retrieve posts tagged with "contemporary art." It also uses web scraping technologies (e.g., BeautifulSoup and Scrapy) to collect data from websites of online art galleries and specialized magazines. The collected data includes image data and text data.

[0757] The collected data is first stored in a database. It then undergoes a data cleaning process. During data cleaning, noise information is removed from the collected data and it is converted into a format suitable for analysis. For example, advertisements and irrelevant information are removed from text data using regular expressions, and image data is standardized into JPEG format using PIL (Python Imaging Library) and converted to 800x800 pixels.

[0758] The server then performs trend analysis using the cleaned data. Here, natural language processing (NLP) techniques (e.g., SpaCy and NLTK) are used to extract frequently occurring keywords and themes from the text data. Image processing techniques (e.g., OpenCV) are also used to analyze popular colors and shapes from the image data. Specifically, frequently occurring keywords such as "abstract," "natural," and "modern" are extracted from the text data, and the image data confirms that "blue" and "circular composition" are common.

[0759] Based on the trend data, the server runs a generative AI model (e.g., GPT-3 or DALL-E) to generate ideas for new artworks. The prompts are in the form of "Generate ideas for abstract, blue artwork incorporating natural elements." The generated ideas are stored in a database and displayed through a user interface.

[0760] Terminal handling

[0761] The terminal provides a user interface (GUI) for users to access the system. The GUI has fields for displaying collected contemporary art trend information and generated art ideas. Through this interface, users can input their desired art style and theme to the server.

[0762] When a user inputs the desired art style or theme into the device interface, the data is sent by the device to the server. Here, the data can be sent asynchronously using Ajax technology. For example, if a user wants art ideas with the theme "abstract and natural," the data is sent to the server.

[0763] The server then runs the generative AI model again based on the user's input data, generates optimal art ideas, and sends them to the device. These generated art ideas are displayed on the device's interface, allowing the user to select the best one.

[0764] User Action

[0765] Users log in to the system through a terminal interface. When logging in, the user's authentication information is transmitted encrypted for security reasons. Next, the user enters their requests and wishes regarding their art creation. For example, if they want art in an "abstract style" or with a "natural element" theme, they enter that into the interface.

[0766] The user reviews the art ideas sent by the server and selects the one they like best. The user then creates the art based on the selected idea. After completing the creation, the results are fed back to the system. This feedback evaluates how useful the selected idea was in the actual creation. The server uses this feedback to improve the performance of the generative AI model and the quality of the ideas it provides.

[0767] As described above, a system is realized in which the server, terminals, and users work together to efficiently and quickly generate new art ideas.

[0768] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0769] Step 1:

[0770] The server collects data about contemporary art from social media, online art galleries, and specialized magazine websites using tools such as the Instagram API, BeautifulSoup, and Scrapy. Specifically, the server calls the Instagram API to retrieve the 10,000 most recent posts tagged "contemporaryart." It also uses Scrapy to retrieve information about new exhibitions from specific online art gallery pages.

[0771] Input: Contemporary art data collected via API and web scraping

[0772] Output: Collected text and image data

[0773] Step 2:

[0774] The server stores the collected data in a database and then performs a data cleaning process. Specifically, it uses regular expressions to remove advertisements and irrelevant information from the collected text data, and then uses PIL (Python Imaging Library) to standardize the image data into JPEG format and convert it to 800x800 pixels.

[0775] Input: Collected text and image data

[0776] Output: Clean text and image data

[0777] Step 3:

[0778] The server uses the cleaned data to perform trend analysis. Specifically, it uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data. SpaCy and NLTK are used for NLP. It also uses OpenCV to analyze popular colors and shapes from image data. For example, it extracts frequently occurring keywords such as "abstract," "nature," and "modern" from the text data, and determines that "blue" and "circular composition" are common in the image data.

[0779] Input: Clean text and image data

[0780] Output: Extracted frequent keywords, themes, colors, shapes, and other trend data

[0781] Step 4:

[0782] The server runs a generative AI model based on the trend data to generate ideas for new artworks. Specifically, the generative AI is run using GPT-3 and DALL-E, and generates ideas by entering the prompt "Generate ideas for abstract blue artwork incorporating natural elements." The generated ideas are stored in a database.

[0783] Input: Trend data and prompt statement

[0784] Output: Generated art ideas

[0785] Step 5:

[0786] The terminal receives and displays the generated art ideas from the server through a user interface provided to the user, which also includes a field for the artist to input their desired art style and theme.

[0787] Input: Generated art ideas

[0788] Output: The idea displayed in the user interface

[0789] Step 6:

[0790] The user inputs the desired art style or theme into the device interface, and the data is sent to the server. For example, if the user wants an "abstract and natural" themed art idea, the data is sent to the server.

[0791] Input: User-entered art style and theme

[0792] Output: Desired data to be sent to the server

[0793] Step 7:

[0794] The server re-runs the generative AI model based on the user's input data, generates optimal art ideas, and sends them to the device. For example, the AI ​​can generate ideas using new prompts that match the user's wishes.

[0795] Input: User's desired data and prompt

[0796] Output: Best art ideas

[0797] Step 8:

[0798] The terminal displays the best art ideas sent from the server on the user interface, and the user can review the provided ideas and select the one they like best.

[0799] Input: The best art ideas sent by the server

[0800] Output: The best idea displayed in the user interface

[0801] Step 9:

[0802] The user creates an artwork based on the selected idea and provides feedback to the system via the terminal about the results. The feedback includes an evaluation of how the selected idea was useful in the actual creation.

[0803] Input: Results and feedback on the selected ideas

[0804] Output: Feedback data sent to the server

[0805] Step 10:

[0806] The server analyzes user feedback and uses it to improve the performance of the generative AI model and the quality of the ideas it provides, thereby increasing the accuracy and usefulness of the entire system.

[0807] Input: Feedback data from users

[0808] Output: Improved generative AI models and the quality of generated ideas

[0809] (Application example 1)

[0810] 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."

[0811] In the creation of modern art, artists need to collect and analyze vast amounts of data to come up with new ideas. It is also difficult to quickly grasp the latest trends. Furthermore, there are few ways for artists to obtain specific ideas that fit their own style or theme. As a result, there is a need for an efficient system to enhance creativity.

[0812] 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.

[0813] In this invention, the server includes means for collecting feature data, means for analyzing the collected feature data to extract trends, means for generating new designs based on the trends, means for presenting the generated designs, means for receiving input data from a user and providing designs based on the input data, and means for operating a generative AI model based on a theme and style specified by the user to generate art ideas. This allows users to easily obtain new art ideas that meet their individual needs and enhance their creativity. It also allows users to efficiently grasp trends in contemporary art and utilize them in their creations.

[0814] "Feature data" refers to information collected for analysis, such as text data, image data, and social media posts related to the contemporary art in question.

[0815] "Analysis" refers to the process of extracting trends from collected feature data and converting it into meaningful information.

[0816] "Trends" are common themes or patterns that emerge from the analyzed data.

[0817] "Designs" refer to artworks and concepts created using generative AI models.

[0818] A "generative AI model" is a program that uses machine learning algorithms to automatically generate new design and art ideas.

[0819] "Presenting" means displaying the generated designs and ideas to the user.

[0820] A "user interface" is an interactive system that has screens and input fields that allow a user to operate the system.

[0821] "Feedback" refers to information that allows a user to evaluate the generated design and its proposals and provide their opinions and suggestions for improvement to the system.

[0822] "Generative means" refers to the functions and techniques for generating new designs and ideas based on the analyzed trends.

[0823] "Trend data" refers to information about distinctive themes or patterns that emerge as a result of analysis.

[0824] "Behavior" refers to the process by which the generative AI model generates art ideas based on themes and styles specified by the user.

[0825] This system allows the server, terminals, and users to cooperate in collecting and analyzing data on contemporary art, and provides ideas for new art creation. This system has the function of collecting and analyzing characteristic data, extracting trends, generating designs based on that data, and presenting them to users.

[0826] The server collects data about contemporary art from various sources, including social media, online art galleries, and specialized magazines, using APIs and web scraping techniques. The collected data is then stored in a database and undergoes data cleaning, during which unnecessary information is removed and the data is converted into a format suitable for analysis.

[0827] The server then analyzes the cleaned data for trends. This analysis uses natural language processing (NLP) techniques and image processing algorithms to extract frequently occurring keywords, themes, popular colors, shapes, and compositions. The results of this analysis are fed into a generative AI model to generate new art ideas. These ideas are then stored in a database.

[0828] The terminal provides a user interface (UI) for users to access the system. The UI allows users to view trend information and generated ideas. It also includes a field for artists to input their desired art style and theme. For example, if a user inputs their desire to create a "nature"-themed artwork, that data is sent to the server.

[0829] Based on the user's input, the server regenerates optimal art ideas and sends them to the device. The user can browse these ideas and select one. They then create art based on the selected idea and provide feedback on the results. The feedback is an evaluation of how useful the idea was in actual production, and the server uses this feedback to improve the performance of the generation AI and the quality of the ideas it provides.

[0830] Hardware and software used

[0831] On the server side, Amazon Web Services (AWS) is used for data storage and analysis, Google Cloud Natural Language API is used for natural language processing, OpenCV is used for image processing, and OpenAI's GPT-4 model is used for generative AI.

[0832] Specific examples

[0833] If a user wishes to create a piece of art with a "nature" theme, they input their request through the device's UI. Based on the input, the server inputs the following prompt into the generative AI model:

[0834] "Prompt for generative AI model: Generate ideas for nature-themed abstract art."

[0835] Based on this prompt, the generative AI model generates multiple art ideas, including specific suggestions for color, composition, and theme. The generated ideas are displayed on the device's UI, allowing the user to use them as reference for creating their own artwork.

[0836] As a result, the entire process from collecting and analyzing feature data to generating new art ideas and presenting them to users is automated, realizing a system that supports artists' creative activities.

[0837] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0838] Step 1:

[0839] The server collects data related to contemporary art. Specifically, it uses APIs and web scraping technologies from social media, online art galleries, and specialized magazines to collect text and image data. The input is data from API calls and web scraping, and the output is the collected raw data.

[0840] Step 2:

[0841] The server stores the collected data in a database and performs a data cleaning process. Specifically, it removes unnecessary information that creates noise and converts it into a format suitable for analysis. For example, it removes advertisements and irrelevant content and converts image data into a unified format. The input is the raw data obtained in step 1, and the output is clean data.

[0842] Step 3:

[0843] The server then uses the cleaned data to analyze trends. It uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data, and image processing algorithms to analyze popular colors, shapes, compositions, etc. The input is the cleaned data generated in step 2, and the output is trend data resulting from the analysis.

[0844] Step 4:

[0845] The server runs a generative AI model based on the analyzed trend data to generate new art ideas. Specifically, the trend data is input into the generative AI as prompts to generate ideas. The generated ideas are stored in a database. The input is trend data, and the output is the generated art idea.

[0846] Step 5:

[0847] The terminal provides a user interface, allowing users to access the system. The UI displays generated art ideas and trend information, and includes a field where artists can input their desired style and theme. The input is data based on the user's preferences and interests, and the output is new art ideas based on that data.

[0848] Step 6:

[0849] The user operates the terminal interface and inputs the desired style or theme, for example, "nature-themed artwork." The input is the theme or style the user inputs, and the output is the corresponding prompt.

[0850] Step 7:

[0851] The server regenerates optimal art ideas based on the user's input data and sends them to the device. The server creates prompts using the user's input theme and runs the generative AI model again. The input is the user's theme and style data, and the output is updated art ideas.

[0852] Step 8:

[0853] The user reviews the provided ideas and selects the one they like best. They then create art based on the selected idea and provide feedback to the system. The input is the selected art idea, and the output is the resulting feedback data.

[0854] Step 9:

[0855] The server analyzes user feedback to improve the performance of the generative AI and the quality of the ideas it provides. Specifically, it analyzes user evaluations and uses them as training data for the generative AI. The input is user feedback data, and the output is an updated generative AI model.

[0856] The above process provides a support system that allows servers, terminals, and users to work together to create innovative and meaningful art.

[0857] 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.

[0858] This invention is a system in which a server, a terminal, a user, and an emotion engine work together to analyze contemporary art data, provide artists with new ideas for creating art, and recognize the user's emotions and reflect them in the design. The processing for each role is explained in detail below.

[0859] Server Processing

[0860] The server first collects feature data about contemporary art from various sources, such as social media, online art galleries, and specialized magazines, using APIs and web scraping techniques to collect posts and articles tagged with "contemporary art." This data includes image data and text data.

[0861] The collected data is stored in a database and then undergoes a data cleaning process. Data cleaning removes unnecessary information that creates noise and converts it into a format suitable for analysis. For example, advertisements and irrelevant content are removed from text data, and image data is converted into a unified format.

[0862] Next, the cleaned data undergoes trend analysis. The server uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data, and image processing algorithms to analyze popular colors, shapes, and compositions. This trend data is then used to run a generative AI to generate ideas for new artworks. These ideas are then stored in a database.

[0863] Emotion engine processing

[0864] The emotion engine analyzes facial expressions, click patterns, operation logs, etc. as users browse designs, recognizing emotions in real time. Emotional data is acquired using a camera and microphone built into the user interface.

[0865] The recognized emotion data is sent to a server and used as feedback for the design the user has viewed, allowing the generative AI to provide designs that take the user's emotions into consideration when generating designs from then on.

[0866] Terminal handling

[0867] The terminal provides a graphical user interface (GUI) for users to access the server and browse contemporary art trend information and generated ideas. The interface also includes fields for artists to input their desired art style and theme.

[0868] When the user inputs their preferences into the device, the data is sent to the server. The server regenerates the best art ideas based on the user's input data and sends them to the device. The user can then review the provided ideas and select one.

[0869] User Action

[0870] First, users operate the terminal interface to log in to the system. Then, they input their requirements and preferences for their art creation. For example, if a user requests an "abstract style" or "art themed around natural elements," that data is sent to the server.

[0871] The user reviews the ideas sent from the server and selects the one they like best. They then create art based on the selected idea and feed the results back to the system via their device. At this time, the user's emotional data, as recognized by the emotion engine, is also fed back.

[0872] A specific example is the following flow:

[0873] The user inputs into the interface their desire to create an artwork with the theme of "inspirational landscapes."

[0874] The terminal transmits this data to the server.

[0875] The server runs a generative AI using trend data related to the theme of "inspirational landscapes" to generate ideas for "abstract art with the theme of inspiring landscapes."

[0876] The generated ideas are sent to the terminal and presented to the user.

[0877] While the user is browsing the presented ideas, the emotion engine recognizes the user's emotions in real time and transmits this information to the server.

[0878] Users select an idea and then work on creating it. After creation, the results are given along with emotional feedback.

[0879] In this way, a system is provided in which the server, terminal, user and emotion engine work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[0880] The processing flow will be explained below.

[0881] Step 1:

[0882] The server collects feature data from data sources such as social media, online art galleries, and specialty magazines, using APIs to retrieve tagged art posts and web scraping techniques to download articles and images.

[0883] Step 2:

[0884] The server stores the collected feature data in a database. Before storing, the data is cleaned to remove unnecessary information from the text and images. For example, advertisements and irrelevant comments are removed from the text data, and image data is converted into a unified format.

[0885] Step 3:

[0886] The server analyzes the cleaned data and uses natural language processing (NLP) techniques to extract frequent keywords and themes, as well as image processing algorithms to analyze popular colors, shapes, and compositions, thereby identifying trends in contemporary art.

[0887] Step 4:

[0888] The server runs a generative AI based on the analyzed trend data to generate ideas for new artworks. The generative AI uses the learned model to propose designs based on the trends.

[0889] Step 5:

[0890] The server stores the generated art ideas in a database and makes them accessible to users through a user interface.

[0891] Step 6:

[0892] The terminal provides a graphical user interface (GUI) for users to access, through which they can view contemporary art trend information and generated ideas.

[0893] Step 7:

[0894] The user interface is equipped with an emotion engine that recognizes emotions in real time by analyzing facial expressions, click patterns, and operation logs as users browse designs.

[0895] Step 8:

[0896] Users input their desired art style and theme into the interface via their device, such as "abstract style" or "art with a natural element theme."

[0897] Step 9:

[0898] The device sends the user's input data to the server, which then combines and analyzes this data with the emotional data recognized by the emotion engine to regenerate optimal art ideas based on the user's wishes.

[0899] Step 10:

[0900] The server sends the generated ideas to the terminal, which displays them on the user interface. The user can review the ideas provided and select the one they like best.

[0901] Step 11:

[0902] The user creates art based on the selected idea. After completing the work, the user inputs feedback on the finished product into the system via a terminal. At this time, the user's emotional data recognized by the emotion engine is also fed back.

[0903] Step 12:

[0904] The server receives user feedback and emotional data, analyzes it, and improves the performance of the AI ​​and the quality of the ideas it provides. This feedback cycle improves the system's accuracy and helps with future idea generation.

[0905] In this way, a system is provided in which the server, terminal, user and emotion engine work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[0906] Example 2

[0907] 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."

[0908] In the creation of contemporary art, it is extremely difficult for artists to find new ideas and create designs that match the user's emotions. Furthermore, generating personalized designs based on individual users' emotions requires advanced analytical technology and real-time emotion recognition, which conventional systems often cannot adequately address. Therefore, there is a need to analyze contemporary art data and provide new artwork ideas that take the user's emotions into consideration.

[0909] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0910] In this invention, the server includes means for collecting feature data, means for analyzing the collected feature data to extract trends, means for operating a generative AI model to generate new designs based on the trends, means for analyzing user emotion data in real time and using the data as feedback, means for providing a user interface for the user to input a desired style or theme, means for presenting the generated designs to the user, and means for receiving feedback from the user, analyzing the feedback, and using the feedback to improve the generation means. This makes it possible to efficiently analyze a variety of contemporary art data and provide personalized designs that match the user's emotions.

[0911] "Feature data" refers to information such as image data and text data related to contemporary art, including data collected from social media and online galleries.

[0912] A "generative AI model" refers to an algorithm that uses large-scale language models and deep learning algorithms to generate ideas for new designs and artworks.

[0913] An "emotion engine" refers to a system that analyzes a user's facial expressions, click patterns, operation logs, etc. to recognize emotions in real time and analyzes that data.

[0914] "User Interface" refers to a graphical interface that allows a user to input a desired style or theme and view the generated design.

[0915] "Trend data" refers to information such as frequently occurring keywords, themes, colors, shapes, and compositions extracted by analyzing collected feature data.

[0916] "Feedback" refers to the opinions and reactions users provide to the generated designs, including emotional data obtained through the emotion engine.

[0917] This invention is a system in which a server, a terminal, a user, and an emotion engine work together to analyze contemporary art data, provide artists with new ideas for creating art, and recognize the user's emotions and reflect them in the design. The processing for each role is explained in detail below.

[0918] Server Processing

[0919] The server first collects feature data about contemporary art from social media, online art galleries, and specialized magazines using APIs and web scraping. The APIs of Instagram and online galleries are used as the primary data source. For example, the server uses the Instagram API to collect images tagged with "contemporary art" and their captions.

[0920] The collected data is temporarily stored in a database such as MySQL or MongoDB, after which it undergoes a data cleaning process: regular expressions in Python are used to remove advertisements and irrelevant content from the text data, and image data is converted into a unified format (e.g., JPEG).

[0921] Based on the clean data, the server uses natural language processing (NLP) techniques to extract frequent keywords and themes from the text data, and Python libraries (NLTK and OpenCV) to analyze popular colors, shapes, and compositions from image data.

[0922] Based on the analysis results, the server runs a generative AI model to generate ideas for new artworks. For example, the generative AI model uses a large-scale language model such as GPT-4. The following prompt is input to the model:

[0923] Please generate ideas for artwork with the theme of "inspirational landscapes." Please also suggest specific colors, shapes, and compositions.

[0924] The generated ideas are then stored in the database.

[0925] Emotion engine processing

[0926] The emotion engine analyzes facial expressions, click patterns, operation logs, and other data as users browse designs to recognize emotions in real time. This is achieved using a camera and microphone built into the interface. For example, the camera captures the user's facial expressions, and the microphone acquires audio data. This data is sent to an API for emotion analysis (e.g., Microsoft Azure's Emotion API) and analyzed in real time.

[0927] The recognized emotion data is sent to the server and used as feedback data for the design the user viewed. This data will serve as the basis for providing designs that take the user's emotions into consideration when generating designs in the future.

[0928] Terminal handling

[0929] The terminal provides a graphical user interface (GUI) for users to access. This interface is likely to be implemented using React.js or Vue.js. Through this interface, users can access the server and view contemporary art trend information and generated art ideas. There is also a field for users to input their desired art style and theme.

[0930] Users input their desired style and theme into the interface, and the data is sent to the server via an AJAX request. The server generates new art ideas based on this data and sends the results to the device, where the user can view and select from the presented ideas.

[0931] User Action

[0932] Users first log in to the system through a terminal interface and enter their requirements and preferences for their art creation, such as specific preferences such as "abstract style" or "art themed around natural elements."

[0933] The user reviews the ideas sent from the server and selects the one they like best. After making their selection, they then create the actual artwork, and the results are fed back to the system via their device. At this time, the user's emotional data, as recognized by the emotion engine, is also fed back.

[0934] In this way, the system of the present invention enables the server, terminal, user, and emotion engine to work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[0935] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0936] Step 1:

[0937] The server collects feature data. As input, it obtains data about contemporary art from social media APIs and web scraping tools. This includes social media (e.g., posts tagged "contemporary art" using the Instagram API), online art galleries, and specialized magazines. Specifically, it sends API requests and receives the retrieved data in JSON format. The output is the collected image data and text data.

[0938] Step 2:

[0939] The server stores the collected data in a database. The input is the collected feature data. For example, the data is temporarily stored in a database such as MongoDB. Specifically, it executes an insert query on the database to save image data and text data. The output is the uncleaned data stored in the database.

[0940] Step 3:

[0941] The server performs data cleaning. The input is the raw feature data stored in the database. It uses Python regular expressions to remove advertisements and irrelevant content from the text data and converts the image data into a unified format (e.g., JPEG). Specific operations include filtering the text data and converting the image data format. The output is clean data converted into a format suitable for analysis.

[0942] Step 4:

[0943] The server performs trend analysis using cleaned data. The input is cleaned feature data. Natural language processing (NLP) techniques are used to extract frequently occurring keywords and themes from the text data, and Python libraries (NLTK and OpenCV) are used to analyze popular colors, shapes, and compositions from image data. Specifically, it performs text analysis using keyword extraction algorithms and image processing algorithms. The output is trend data extracted as frequently occurring keywords, themes, colors, shapes, compositions, etc.

[0944] Step 5:

[0945] The server runs a generative AI model based on the trend data to generate ideas for new artworks. The input is the trend data and a generative AI model (e.g., GPT-4 model). Specifically, the server gives the generative AI model the following prompt:

[0946] Please generate ideas for artwork with the theme of "inspirational landscapes." Please also suggest specific colors, shapes, and compositions.

[0947] The output is the generated new art idea.

[0948] Step 6:

[0949] The server saves the generated art ideas in a database. The input is the generated art ideas. The specific operation is to execute an insert query on the database to save the art ideas. The output is the art ideas saved in the database.

[0950] Step 7:

[0951] The terminal provides a user interface. The input is the art idea sent from the server and the user's input data. Through an interface built with React.js or Vue.js, it provides fields for the user to enter their desired art style and theme. Specific operations include rendering a form and accepting user input. The output is the user's input data.

[0952] Step 8:

[0953] A user operates the terminal interface to log in to the system. The input is the user's authentication information. Specifically, the user enters a username and password into the login form and sends an authentication request to the server. The output is the authenticated user's session information.

[0954] Step 9:

[0955] The user inputs their requirements and desires for their art creation. The input is the user's desired style or theme. Specific actions include entering "abstract style" or "art themed around natural elements" into a text box on the interface. The output is the user's input data, which is sent to the server.

[0956] Step 10:

[0957] The server generates new art ideas based on the user's wishes and sends them to the device. The inputs are the user's input data and the generative AI model. Specifically, the server provides the user's wishes as prompts to the generative AI model, which then generates new art ideas. The output is the new art ideas sent to the device.

[0958] Step 11:

[0959] The terminal presents the art ideas sent from the server to the user. The input is a new art idea. Specific operations include rendering the art idea received from the server into HTML and displaying it on the interface. The output is the art idea presented to the user.

[0960] Step 12:

[0961] The user selects an art idea and then begins to create it. The input is the art idea presented to the user. The specific action is to select the one they like best from the multiple art ideas presented. The output is the selected art idea.

[0962] Step 13:

[0963] After creating an artwork, the user feeds the results back to the system via their device. The input is an image file of the artwork and comments. Specifically, the user uploads the image file of the artwork and comments and sends them to the server. The output is the feedback data sent to the server.

[0964] Step 14:

[0965] The emotion engine acquires emotion data in real time while the user is viewing the design and sends it to the server. Inputs include the user's facial expressions, click patterns, and operation logs. Specifically, it captures the user's facial expressions with a camera and sends them to an API for emotion analysis. The output is analyzed emotion data.

[0966] Step 15:

[0967] The server analyzes the emotion data and uses it as feedback for the next generation of art ideas. The input is the emotion data sent from the emotion engine. The specific operation is to log the emotion data and reflect it in the generative AI model. The output is an improved generative algorithm.

[0968] As described above, the server, terminal, user, and emotion engine work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[0969] (Application example 2)

[0970] 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."

[0971] Modern security systems require users to continuously monitor surveillance camera footage for long periods of time, which requires a huge amount of effort and can lead to missed events. Furthermore, when detecting anomalies, it is difficult to provide appropriate feedback based on the user's emotions and psychological state, making it difficult to properly highlight important or high-risk scenes. Therefore, there is a need for an efficient and effective surveillance system that improves the user experience.

[0972] 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.

[0973] In this invention, the server includes means for collecting feature data, means for analyzing the collected feature data to extract trends, means for generating new designs based on the trends, means for presenting the generated designs, means for receiving input data from a user and providing designs based on the input data, means for analyzing user emotions in real time, and means for highlighting important scenes based on the analyzed emotion data. This makes it possible to improve the user experience and increase the efficiency of monitoring operations by detecting anomalies while taking the user's emotional state into consideration and highlighting important scenes and high-risk scenes.

[0974] "Feature data" refers to basic information that the system uses for analysis and generation, including, for example, image data, text data, and social media posts.

[0975] "Analysis" is the process of processing collected data to extract trends, using natural language processing and image processing algorithms to find important features and patterns.

[0976] "Trends" are specific patterns or themes in the data that are derived through analysis and serve as the basis for generating new designs.

[0977] "Generation" is the process of generating new designs and ideas based on trend data, and is done using generative AI models.

[0978] "Emotional data" refers to psychological and emotional information obtained from users' facial expressions, click patterns, operation logs, etc.

[0979] "User Interface" means the graphical operating screen through which a user can access the system and input their desired style and theme.

[0980] The present invention relates to an advanced security feedback system that analyzes user emotional data in real time and highlights important scenes, thereby enabling security operations to be carried out efficiently and effectively.

[0981] Server Processing

[0982] The server first collects feature data, which includes surveillance footage, click patterns, operation logs, etc. This feature data is collected using video analysis technology such as OpenCV and web scraping technology.

[0983] After feature data is collected, it undergoes a data cleaning process and is converted into a format suitable for analysis. The cleaned data is used for trend analysis and generative AI models are run to detect anomalies and patterns.

[0984] The generated anomaly and pattern information is stored in a database to create a baseline for highlighting important scenes based on the user's emotional data, which can then be used for future analyses.

[0985] Emotion engine processing

[0986] The emotion engine analyzes users' emotions in real time as they view surveillance footage. It collects data to identify the emotions of users through facial expression recognition and click pattern analysis. The collected emotion data is then sent to a server for analysis.

[0987] Terminal handling

[0988] The terminal provides an interface for the security feedback system, allowing users to easily view surveillance footage and see scenes highlighted if anomalies are detected. The terminal includes a field for users to input their desired surveillance style or theme.

[0989] The input data is sent to the server and used for real-time analysis. The device then presents the highlighted scene sent from the server to the user. While the user is viewing the presented scene, the emotion engine analyzes the emotion data in real time and uses it as feedback for the next analysis.

[0990] User Action

[0991] The user first operates the terminal interface and logs into the system. Then, they input their monitoring preferences and requirements. The system then performs analysis based on the user's preferences and optimizes anomaly detection.

[0992] While the user is viewing the video, the emotion engine analyzes the user's emotions, and the data is sent to the server. This allows the server to highlight important or high-risk scenes. The user checks the highlighted scenes presented by the system and provides feedback. This improves the accuracy of analysis from the next time onwards, enabling more efficient monitoring.

[0993] Examples of concrete examples and prompts

[0994] As a concrete example, suppose a user is watching a surveillance camera and suddenly sees a scene with a lot of movement, and expresses surprise. The system stores this scene so that similar anomalies are noticed in future surveillance. An example of a prompt sentence is as follows:

[0995] "Detect any anomalies in this camera feed and highlight important scenes in real time, taking into account the user's emotions."

[0996] In this way, the system of the present invention contributes to improving the efficiency of security analysis and monitoring work based on user emotions through cooperation between the server, terminal, user, and emotion engine.

[0997] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0998] Step 1:

[0999] The server collects feature data from the surveillance camera footage. In this process, it captures the surveillance footage frame by frame and extracts the image data using OpenCV. The input is the surveillance camera video feed, and the output is the feature data (image frames).

[1000] Step 2:

[1001] The server cleans the collected feature data and converts it into a format suitable for analysis. Specifically, it performs noise removal and image standardization. The input is the feature data obtained in step 1, and the output is the cleaned data.

[1002] Step 3:

[1003] The server analyzes the cleaned data to detect anomalies. It uses the AnomalyDetection library to identify specific patterns or anomalies in the frames. The input is the cleaned data, and the output is the frame information containing the anomaly and its location data.

[1004] Step 4:

[1005] The terminal uses the analyzed data to highlight abnormalities in real time on the user interface, notifying the user by, for example, surrounding the area where an abnormality is detected with a red frame. The input is the abnormal data sent from the server, and the output is a user interface with the abnormality highlighted.

[1006] Step 5:

[1007] The emotion engine analyzes the user's facial expressions and operation patterns to collect emotional data in real time. It uses the EmotionRecognition library to collect facial expression data and also analyzes click and operation logs. The input is the user's facial image and operation log, and the output is the user's emotional data.

[1008] Step 6:

[1009] The server re-highlights abnormal scenes based on the emotion data. It analyzes the user's emotion data and prioritizes highlighting scenes that evoke emotions such as "surprise" and "anxiety." The input is emotion data and abnormal data, and the output is scenes that are highlighted based on the emotion feedback.

[1010] Step 7:

[1011] The user reviews the highlighted scenes and provides feedback as needed. The reviewed scenes and emotional feedback are input into the system to help with the next analysis. The input is the user's feedback data, and the output is an updated analysis algorithm.

[1012] In this way, the server, terminal, and emotion engine work together to detect and highlight anomalies based on the user's emotions, thereby realizing an efficient and effective security feedback system.

[1013] 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.

[1014] 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.

[1015] 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.

[1016] [Fourth embodiment]

[1017] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1018] 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.

[1019] 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).

[1020] 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.

[1021] 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.

[1022] 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).

[1023] 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.

[1024] 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.

[1025] 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.

[1026] 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.

[1027] 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.

[1028] 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.

[1029] 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."

[1030] The present invention is a system in which a server, terminals, and users work together to analyze contemporary art data and provide artists with new ideas for creating art. The processing for each role will be explained in detail below.

[1031] Server Processing

[1032] The server first collects data about contemporary art from various sources. For example, it uses APIs and web scraping techniques from social media, online art galleries, and specialized magazines to collect posts and articles tagged with "contemporary art." This data includes image data, text data, and more.

[1033] The collected data is stored in a database and then undergoes a data cleaning process. Data cleaning removes unnecessary information that creates noise and converts it into a format suitable for analysis. For example, advertisements and irrelevant content are removed from text data, and image data is converted into a unified format.

[1034] Next, the cleaned data undergoes trend analysis. The server uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data, and image processing algorithms to analyze popular colors, shapes, and compositions. This trend data is then used to run a generative AI to generate ideas for new artworks. These ideas are then stored in a database.

[1035] Terminal handling

[1036] The terminal provides a user interface (GUI) for users to access the server through which they can browse contemporary art trend information and generated ideas. The interface also includes fields for artists to input their desired art style and theme.

[1037] When the user inputs their preferences into the device, the data is sent to the server. The server regenerates the best art ideas based on the user's input data and sends them to the device. The user can then review the provided ideas and select one.

[1038] User Action

[1039] First, users operate the terminal interface to log in to the system. Then, they input their requirements and preferences for their art creation. For example, if a user requests an "abstract style" or "art themed around natural elements," that data is sent to the server.

[1040] The user reviews the ideas sent by the server and selects the one they like best. They then create art based on the selected idea and provide feedback to the system via their device. The feedback evaluates how useful the selected idea was in the actual creation, and the server uses this feedback to improve the performance of the generative AI and the quality of the ideas it provides.

[1041] A specific example is the following flow:

[1042] The user inputs into the interface their desire to create a "nature" themed artwork.

[1043] The terminal transmits this data to the server.

[1044] The server runs a generative AI using trend data related to the "nature" theme to generate ideas for "nature-themed abstract art."

[1045] The generated ideas are sent to the terminal and presented to the user.

[1046] Users select an idea and then work on creating it. After creation, the results are fed back to the user.

[1047] In this way, a system is provided in which servers, terminals, and users work together to help create innovative and meaningful art.

[1048] The processing flow will be explained below.

[1049] Step 1:

[1050] The server collects feature data about art from data sources such as social media, online art galleries, and specialized magazines, including by using APIs to retrieve tagged posts and web scraping techniques to download articles and images.

[1051] Step 2:

[1052] The server stores the collected data in a database. Before storing it, it performs a data cleaning process to remove unnecessary information that creates noise from the text and images. For example, it removes advertisements and irrelevant comments from the text data, and converts image data into a unified format (JPEG, PNG, etc.).

[1053] Step 3:

[1054] The server analyzes the cleaned data and uses natural language processing (NLP) techniques to extract frequent keywords and themes, as well as image processing algorithms to analyze popular colors, shapes, and compositions, thereby identifying trends in contemporary art.

[1055] Step 4:

[1056] The server runs a generative AI based on the analyzed trend data to generate ideas for new artworks. The generative AI uses the learned model to propose designs based on the trends.

[1057] Step 5:

[1058] The server stores the generated art ideas in a database and makes them accessible to users through a user interface.

[1059] Step 6:

[1060] The terminal provides a graphical user interface (GUI) for users to access, through which they can view contemporary art trend information and generated ideas.

[1061] Step 7:

[1062] Users input their desired art style and theme into the interface via their device, such as "abstract style" or "art with a natural element theme."

[1063] Step 8:

[1064] The device sends the user's input data to the server, which analyzes the data and regenerates the optimal art idea based on the user's wishes.

[1065] Step 9:

[1066] The server sends the generated ideas to the terminal, which displays them on the user interface. The user can review the ideas provided and select the one they like best.

[1067] Step 10:

[1068] Users create art based on the selected idea, and after completing the work, they input feedback on the finished product into the system via their terminal.

[1069] Step 11:

[1070] The server receives user feedback and analyzes it to improve the performance of the generative AI and the quality of the ideas it provides. This feedback cycle improves the accuracy of the system and helps with future idea generation.

[1071] Through these steps, a system is realized in which the server, terminals, and users work together to analyze trends in contemporary art and help create innovative and meaningful art.

[1072] Example 1

[1073] 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."

[1074] In the creation of contemporary art, it is important for artists to gain new ideas and inspiration, but currently this requires a lot of time and effort. It is also difficult to integrate and analyze information obtained from different data sources. For this reason, a system that allows artists to gain new art ideas effectively and quickly is needed.

[1075] 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.

[1076] In this invention, the server includes means for collecting data related to contemporary art, means for storing the collected data in a database and cleaning the data to remove noise, trend analysis means using natural language processing technology to analyze the cleaned data and extract frequently occurring keywords and themes, trend analysis means using image processing technology to analyze image data and extract colors and shapes, means for operating a generative AI model that generates ideas for new artworks based on the trend data, means for storing the generated art ideas in a database and displaying them through a user interface, and means for receiving input data from a user and re-operating the generative AI model based on the input data to provide optimal art ideas, thereby enabling artists to efficiently and quickly obtain new art ideas.

[1077] "Data collection" refers to the acquisition of data related to contemporary art from multiple sources on the Internet.

[1078] A "database" is a repository where collected data is stored so that it can be accessed and analyzed later.

[1079] "Data cleaning" refers to the process of removing unnecessary noise and irrelevant information from collected data and converting it into a form suitable for analysis.

[1080] "Natural Language Processing (NLP)" is a technique used to extract frequent keywords and themes from text data.

[1081] "Image processing" is a technology that analyzes image data and extracts features such as color and shape.

[1082] "Trend analysis" is an analytical method that uses clean data to detect frequently occurring keywords, themes, colors, shapes, etc. in contemporary art.

[1083] A "generative AI model" is an artificial intelligence model that generates new ideas and works based on given data and prompts.

[1084] A "user interface (GUI)" is a visual interface that allows a user to access a system, input data, and view results.

[1085] "Input data" refers to the information that a user inputs into the system, such as the desired art style and theme.

[1086] "Feedback" means that the user uses the generated ideas to create art and then returns the results and evaluation to the system.

[1087] The present invention is a system in which a server, terminals, and users work together to analyze contemporary art data and provide artists with new ideas for creating art. The processing for each role will be explained in detail below.

[1088] Server Processing

[1089] The server first collects data about contemporary art from multiple sources on the Internet. For example, the server uses social media APIs (e.g., Instagram API) to retrieve posts tagged with "contemporary art." It also uses web scraping technologies (e.g., BeautifulSoup and Scrapy) to collect data from websites of online art galleries and specialized magazines. The collected data includes image data and text data.

[1090] The collected data is first stored in a database. It then undergoes a data cleaning process. During data cleaning, noise information is removed from the collected data and it is converted into a format suitable for analysis. For example, advertisements and irrelevant information are removed from text data using regular expressions, and image data is standardized into JPEG format using PIL (Python Imaging Library) and converted to 800x800 pixels.

[1091] The server then performs trend analysis using the cleaned data. Here, natural language processing (NLP) techniques (e.g., SpaCy and NLTK) are used to extract frequently occurring keywords and themes from the text data. Image processing techniques (e.g., OpenCV) are also used to analyze popular colors and shapes from the image data. Specifically, frequently occurring keywords such as "abstract," "natural," and "modern" are extracted from the text data, and the image data confirms that "blue" and "circular composition" are common.

[1092] Based on the trend data, the server runs a generative AI model (e.g., GPT-3 or DALL-E) to generate ideas for new artworks. The prompts are in the form of "Generate ideas for abstract, blue artwork incorporating natural elements." The generated ideas are stored in a database and displayed through a user interface.

[1093] Terminal handling

[1094] The terminal provides a user interface (GUI) for users to access the system. The GUI has fields for displaying collected contemporary art trend information and generated art ideas. Through this interface, users can input their desired art style and theme to the server.

[1095] When a user inputs the desired art style or theme into the device interface, the data is sent by the device to the server. Here, the data can be sent asynchronously using Ajax technology. For example, if a user wants art ideas with the theme "abstract and natural," the data is sent to the server.

[1096] The server then runs the generative AI model again based on the user's input data, generates optimal art ideas, and sends them to the device. These generated art ideas are displayed on the device's interface, allowing the user to select the best one.

[1097] User Action

[1098] Users log in to the system through a terminal interface. When logging in, the user's authentication information is transmitted encrypted for security reasons. Next, the user enters their requests and wishes regarding their art creation. For example, if they want art in an "abstract style" or with a "natural element" theme, they enter that into the interface.

[1099] The user reviews the art ideas sent by the server and selects the one they like best. The user then creates the art based on the selected idea. After completing the creation, the results are fed back to the system. This feedback evaluates how useful the selected idea was in the actual creation. The server uses this feedback to improve the performance of the generative AI model and the quality of the ideas it provides.

[1100] As described above, a system is realized in which the server, terminals, and users work together to efficiently and quickly generate new art ideas.

[1101] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1102] Step 1:

[1103] The server collects data about contemporary art from social media, online art galleries, and specialized magazine websites using tools such as the Instagram API, BeautifulSoup, and Scrapy. Specifically, the server calls the Instagram API to retrieve the 10,000 most recent posts tagged "contemporaryart." It also uses Scrapy to retrieve information about new exhibitions from specific online art gallery pages.

[1104] Input: Contemporary art data collected via API and web scraping

[1105] Output: Collected text and image data

[1106] Step 2:

[1107] The server stores the collected data in a database and then performs a data cleaning process. Specifically, it uses regular expressions to remove advertisements and irrelevant information from the collected text data, and then uses PIL (Python Imaging Library) to standardize the image data into JPEG format and convert it to 800x800 pixels.

[1108] Input: Collected text and image data

[1109] Output: Clean text and image data

[1110] Step 3:

[1111] The server uses the cleaned data to perform trend analysis. Specifically, it uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data. SpaCy and NLTK are used for NLP. It also uses OpenCV to analyze popular colors and shapes from image data. For example, it extracts frequently occurring keywords such as "abstract," "nature," and "modern" from the text data, and determines that "blue" and "circular composition" are common in the image data.

[1112] Input: Clean text and image data

[1113] Output: Extracted frequent keywords, themes, colors, shapes, and other trend data

[1114] Step 4:

[1115] The server runs a generative AI model based on the trend data to generate ideas for new artworks. Specifically, the generative AI is run using GPT-3 and DALL-E, and generates ideas by entering the prompt "Generate ideas for abstract blue artwork incorporating natural elements." The generated ideas are stored in a database.

[1116] Input: Trend data and prompt statement

[1117] Output: Generated art ideas

[1118] Step 5:

[1119] The terminal receives and displays the generated art ideas from the server through a user interface provided to the user, which also includes a field for the artist to input their desired art style and theme.

[1120] Input: Generated art ideas

[1121] Output: The idea displayed in the user interface

[1122] Step 6:

[1123] The user inputs the desired art style or theme into the device interface, and the data is sent to the server. For example, if the user wants an "abstract and natural" themed art idea, the data is sent to the server.

[1124] Input: User-entered art style and theme

[1125] Output: Desired data to be sent to the server

[1126] Step 7:

[1127] The server re-runs the generative AI model based on the user's input data, generates optimal art ideas, and sends them to the device. For example, the AI ​​can generate ideas using new prompts that match the user's wishes.

[1128] Input: User's desired data and prompt

[1129] Output: Best art ideas

[1130] Step 8:

[1131] The terminal displays the best art ideas sent from the server on the user interface, and the user can review the provided ideas and select the one they like best.

[1132] Input: The best art ideas sent by the server

[1133] Output: The best idea displayed in the user interface

[1134] Step 9:

[1135] The user creates an artwork based on the selected idea and provides feedback to the system via the terminal about the results. The feedback includes an evaluation of how the selected idea was useful in the actual creation.

[1136] Input: Results and feedback on the selected ideas

[1137] Output: Feedback data sent to the server

[1138] Step 10:

[1139] The server analyzes user feedback and uses it to improve the performance of the generative AI model and the quality of the ideas it provides, thereby increasing the accuracy and usefulness of the entire system.

[1140] Input: Feedback data from users

[1141] Output: Improved generative AI models and the quality of generated ideas

[1142] (Application example 1)

[1143] 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."

[1144] In the creation of modern art, artists need to collect and analyze vast amounts of data to come up with new ideas. It is also difficult to quickly grasp the latest trends. Furthermore, there are few ways for artists to obtain specific ideas that fit their own style or theme. As a result, there is a need for an efficient system to enhance creativity.

[1145] 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.

[1146] In this invention, the server includes means for collecting feature data, means for analyzing the collected feature data to extract trends, means for generating new designs based on the trends, means for presenting the generated designs, means for receiving input data from a user and providing designs based on the input data, and means for operating a generative AI model based on a theme and style specified by the user to generate art ideas. This allows users to easily obtain new art ideas that meet their individual needs and enhance their creativity. It also allows users to efficiently grasp trends in contemporary art and utilize them in their creations.

[1147] "Feature data" refers to information collected for analysis, such as text data, image data, and social media posts related to the contemporary art in question.

[1148] "Analysis" refers to the process of extracting trends from collected feature data and converting it into meaningful information.

[1149] "Trends" are common themes or patterns that emerge from the analyzed data.

[1150] "Designs" refer to artworks and concepts created using generative AI models.

[1151] A "generative AI model" is a program that uses machine learning algorithms to automatically generate new design and art ideas.

[1152] "Presenting" means displaying the generated designs and ideas to the user.

[1153] A "user interface" is an interactive system that has screens and input fields that allow a user to operate the system.

[1154] "Feedback" refers to information that allows a user to evaluate the generated design and its proposals and provide their opinions and suggestions for improvement to the system.

[1155] "Generative means" refers to the functions and techniques for generating new designs and ideas based on the analyzed trends.

[1156] "Trend data" refers to information about distinctive themes or patterns that emerge as a result of analysis.

[1157] "Behavior" refers to the process by which the generative AI model generates art ideas based on themes and styles specified by the user.

[1158] This system allows the server, terminals, and users to cooperate in collecting and analyzing data on contemporary art, and provides ideas for new art creation. This system has the function of collecting and analyzing characteristic data, extracting trends, generating designs based on that data, and presenting them to users.

[1159] The server collects data about contemporary art from various sources, including social media, online art galleries, and specialized magazines, using APIs and web scraping techniques. The collected data is then stored in a database and undergoes data cleaning, during which unnecessary information is removed and the data is converted into a format suitable for analysis.

[1160] The server then analyzes the cleaned data for trends. This analysis uses natural language processing (NLP) techniques and image processing algorithms to extract frequently occurring keywords, themes, popular colors, shapes, and compositions. The results of this analysis are fed into a generative AI model to generate new art ideas. These ideas are then stored in a database.

[1161] The terminal provides a user interface (UI) for users to access the system. The UI allows users to view trend information and generated ideas. It also includes a field for artists to input their desired art style and theme. For example, if a user inputs their desire to create a "nature"-themed artwork, that data is sent to the server.

[1162] Based on the user's input, the server regenerates optimal art ideas and sends them to the device. The user can browse these ideas and select one. They then create art based on the selected idea and provide feedback on the results. The feedback is an evaluation of how useful the idea was in actual production, and the server uses this feedback to improve the performance of the generation AI and the quality of the ideas it provides.

[1163] Hardware and software used

[1164] On the server side, Amazon Web Services (AWS) is used for data storage and analysis, Google Cloud Natural Language API is used for natural language processing, OpenCV is used for image processing, and OpenAI's GPT-4 model is used for generative AI.

[1165] Specific examples

[1166] If a user wishes to create a piece of art with a "nature" theme, they input their request through the device's UI. Based on the input, the server inputs the following prompt into the generative AI model:

[1167] "Prompt for generative AI model: Generate ideas for nature-themed abstract art."

[1168] Based on this prompt, the generative AI model generates multiple art ideas, including specific suggestions for color, composition, and theme. The generated ideas are displayed on the device's UI, allowing the user to use them as reference for creating their own artwork.

[1169] As a result, the entire process from collecting and analyzing feature data to generating new art ideas and presenting them to users is automated, realizing a system that supports artists' creative activities.

[1170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1171] Step 1:

[1172] The server collects data related to contemporary art. Specifically, it uses APIs and web scraping technologies from social media, online art galleries, and specialized magazines to collect text and image data. The input is data from API calls and web scraping, and the output is the collected raw data.

[1173] Step 2:

[1174] The server stores the collected data in a database and performs a data cleaning process. Specifically, it removes unnecessary information that creates noise and converts it into a format suitable for analysis. For example, it removes advertisements and irrelevant content and converts image data into a unified format. The input is the raw data obtained in step 1, and the output is clean data.

[1175] Step 3:

[1176] The server then uses the cleaned data to analyze trends. It uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data, and image processing algorithms to analyze popular colors, shapes, compositions, etc. The input is the cleaned data generated in step 2, and the output is trend data resulting from the analysis.

[1177] Step 4:

[1178] The server runs a generative AI model based on the analyzed trend data to generate new art ideas. Specifically, the trend data is input into the generative AI as prompts to generate ideas. The generated ideas are stored in a database. The input is trend data, and the output is the generated art idea.

[1179] Step 5:

[1180] The terminal provides a user interface, allowing users to access the system. The UI displays generated art ideas and trend information, and includes a field where artists can input their desired style and theme. The input is data based on the user's preferences and interests, and the output is new art ideas based on that data.

[1181] Step 6:

[1182] The user operates the terminal interface and inputs the desired style or theme, for example, "nature-themed artwork." The input is the theme or style the user inputs, and the output is the corresponding prompt.

[1183] Step 7:

[1184] The server regenerates optimal art ideas based on the user's input data and sends them to the device. The server creates prompts using the user's input theme and runs the generative AI model again. The input is the user's theme and style data, and the output is updated art ideas.

[1185] Step 8:

[1186] The user reviews the provided ideas and selects the one they like best. They then create art based on the selected idea and provide feedback to the system. The input is the selected art idea, and the output is the resulting feedback data.

[1187] Step 9:

[1188] The server analyzes user feedback to improve the performance of the generative AI and the quality of the ideas it provides. Specifically, it analyzes user evaluations and uses them as training data for the generative AI. The input is user feedback data, and the output is an updated generative AI model.

[1189] The above process provides a support system that allows servers, terminals, and users to work together to create innovative and meaningful art.

[1190] 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.

[1191] This invention is a system in which a server, a terminal, a user, and an emotion engine work together to analyze contemporary art data, provide artists with new ideas for creating art, and recognize the user's emotions and reflect them in the design. The processing for each role is explained in detail below.

[1192] Server Processing

[1193] The server first collects feature data about contemporary art from various sources, such as social media, online art galleries, and specialized magazines, using APIs and web scraping techniques to collect posts and articles tagged with "contemporary art." This data includes image data and text data.

[1194] The collected data is stored in a database and then undergoes a data cleaning process. Data cleaning removes unnecessary information that creates noise and converts it into a format suitable for analysis. For example, advertisements and irrelevant content are removed from text data, and image data is converted into a unified format.

[1195] Next, the cleaned data undergoes trend analysis. The server uses natural language processing (NLP) techniques to extract frequently occurring keywords and themes from the text data, and image processing algorithms to analyze popular colors, shapes, and compositions. This trend data is then used to run a generative AI to generate ideas for new artworks. These ideas are then stored in a database.

[1196] Emotion engine processing

[1197] The emotion engine analyzes facial expressions, click patterns, operation logs, etc. as users browse designs, recognizing emotions in real time. Emotional data is acquired using a camera and microphone built into the user interface.

[1198] The recognized emotion data is sent to a server and used as feedback for the design the user has viewed, allowing the generative AI to provide designs that take the user's emotions into consideration when generating designs from then on.

[1199] Terminal handling

[1200] The terminal provides a graphical user interface (GUI) for users to access the server and browse contemporary art trend information and generated ideas. The interface also includes fields for artists to input their desired art style and theme.

[1201] When the user inputs their preferences into the device, the data is sent to the server. The server regenerates the best art ideas based on the user's input data and sends them to the device. The user can then review the provided ideas and select one.

[1202] User Action

[1203] First, users operate the terminal interface to log in to the system. Then, they input their requirements and preferences for their art creation. For example, if a user requests an "abstract style" or "art themed around natural elements," that data is sent to the server.

[1204] The user reviews the ideas sent from the server and selects the one they like best. They then create art based on the selected idea and feed the results back to the system via their device. At this time, the user's emotional data, as recognized by the emotion engine, is also fed back.

[1205] A specific example is the following flow:

[1206] The user inputs into the interface their desire to create an artwork with the theme of "inspirational landscapes."

[1207] The terminal transmits this data to the server.

[1208] The server runs a generative AI using trend data related to the theme of "inspirational landscapes" to generate ideas for "abstract art with the theme of inspiring landscapes."

[1209] The generated ideas are sent to the terminal and presented to the user.

[1210] While the user is browsing the presented ideas, the emotion engine recognizes the user's emotions in real time and transmits this information to the server.

[1211] Users select an idea and then work on creating it. After creation, the results are given along with emotional feedback.

[1212] In this way, a system is provided in which the server, terminal, user and emotion engine work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[1213] The processing flow will be explained below.

[1214] Step 1:

[1215] The server collects feature data from data sources such as social media, online art galleries, and specialty magazines, using APIs to retrieve tagged art posts and web scraping techniques to download articles and images.

[1216] Step 2:

[1217] The server stores the collected feature data in a database. Before storing, the data is cleaned to remove unnecessary information from the text and images. For example, advertisements and irrelevant comments are removed from the text data, and image data is converted into a unified format.

[1218] Step 3:

[1219] The server analyzes the cleaned data and uses natural language processing (NLP) techniques to extract frequent keywords and themes, as well as image processing algorithms to analyze popular colors, shapes, and compositions, thereby identifying trends in contemporary art.

[1220] Step 4:

[1221] The server runs a generative AI based on the analyzed trend data to generate ideas for new artworks. The generative AI uses the learned model to propose designs based on the trends.

[1222] Step 5:

[1223] The server stores the generated art ideas in a database and makes them accessible to users through a user interface.

[1224] Step 6:

[1225] The terminal provides a graphical user interface (GUI) for users to access, through which they can view contemporary art trend information and generated ideas.

[1226] Step 7:

[1227] The user interface is equipped with an emotion engine that recognizes emotions in real time by analyzing facial expressions, click patterns, and operation logs as users browse designs.

[1228] Step 8:

[1229] Users input their desired art style and theme into the interface via their device, such as "abstract style" or "art with a natural element theme."

[1230] Step 9:

[1231] The device sends the user's input data to the server, which then combines and analyzes this data with the emotional data recognized by the emotion engine to regenerate optimal art ideas based on the user's wishes.

[1232] Step 10:

[1233] The server sends the generated ideas to the terminal, which displays them on the user interface. The user can review the ideas provided and select the one they like best.

[1234] Step 11:

[1235] The user creates art based on the selected idea. After completing the work, the user inputs feedback on the finished product into the system via a terminal. At this time, the user's emotional data recognized by the emotion engine is also fed back.

[1236] Step 12:

[1237] The server receives user feedback and emotional data, analyzes it, and improves the performance of the AI ​​and the quality of the ideas it provides. This feedback cycle improves the system's accuracy and helps with future idea generation.

[1238] In this way, a system is provided in which the server, terminal, user and emotion engine work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[1239] Example 2

[1240] 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."

[1241] In the creation of contemporary art, it is extremely difficult for artists to find new ideas and create designs that match the user's emotions. Furthermore, generating personalized designs based on individual users' emotions requires advanced analytical technology and real-time emotion recognition, which conventional systems often cannot adequately address. Therefore, there is a need to analyze contemporary art data and provide new artwork ideas that take the user's emotions into consideration.

[1242] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1243] In this invention, the server includes means for collecting feature data, means for analyzing the collected feature data to extract trends, means for operating a generative AI model to generate new designs based on the trends, means for analyzing user emotion data in real time and using the data as feedback, means for providing a user interface for the user to input a desired style or theme, means for presenting the generated designs to the user, and means for receiving feedback from the user, analyzing the feedback, and using the feedback to improve the generation means. This makes it possible to efficiently analyze a variety of contemporary art data and provide personalized designs that match the user's emotions.

[1244] "Feature data" refers to information such as image data and text data related to contemporary art, including data collected from social media and online galleries.

[1245] A "generative AI model" refers to an algorithm that uses large-scale language models and deep learning algorithms to generate ideas for new designs and artworks.

[1246] An "emotion engine" refers to a system that analyzes a user's facial expressions, click patterns, operation logs, etc. to recognize emotions in real time and analyzes that data.

[1247] "User Interface" refers to a graphical interface that allows a user to input a desired style or theme and view the generated design.

[1248] "Trend data" refers to information such as frequently occurring keywords, themes, colors, shapes, and compositions extracted by analyzing collected feature data.

[1249] "Feedback" refers to the opinions and reactions users provide to the generated designs, including emotional data obtained through the emotion engine.

[1250] This invention is a system in which a server, a terminal, a user, and an emotion engine work together to analyze contemporary art data, provide artists with new ideas for creating art, and recognize the user's emotions and reflect them in the design. The processing for each role is explained in detail below.

[1251] Server Processing

[1252] The server first collects feature data about contemporary art from social media, online art galleries, and specialized magazines using APIs and web scraping. The APIs of Instagram and online galleries are used as the primary data source. For example, the server uses the Instagram API to collect images tagged with "contemporary art" and their captions.

[1253] The collected data is temporarily stored in a database such as MySQL or MongoDB, after which it undergoes a data cleaning process: regular expressions in Python are used to remove advertisements and irrelevant content from the text data, and image data is converted into a unified format (e.g., JPEG).

[1254] Based on the clean data, the server uses natural language processing (NLP) techniques to extract frequent keywords and themes from the text data, and Python libraries (NLTK and OpenCV) to analyze popular colors, shapes, and compositions from image data.

[1255] Based on the analysis results, the server runs a generative AI model to generate ideas for new artworks. For example, the generative AI model uses a large-scale language model such as GPT-4. The following prompt is input to the model:

[1256] Please generate ideas for artwork with the theme of "inspirational landscapes." Please also suggest specific colors, shapes, and compositions.

[1257] The generated ideas are then stored in the database.

[1258] Emotion engine processing

[1259] The emotion engine analyzes facial expressions, click patterns, operation logs, and other data as users browse designs to recognize emotions in real time. This is achieved using a camera and microphone built into the interface. For example, the camera captures the user's facial expressions, and the microphone acquires audio data. This data is sent to an API for emotion analysis (e.g., Microsoft Azure's Emotion API) and analyzed in real time.

[1260] The recognized emotion data is sent to the server and used as feedback data for the design the user viewed. This data will serve as the basis for providing designs that take the user's emotions into consideration when generating designs in the future.

[1261] Terminal handling

[1262] The terminal provides a graphical user interface (GUI) for users to access. This interface is likely to be implemented using React.js or Vue.js. Through this interface, users can access the server and view contemporary art trend information and generated art ideas. There is also a field for users to input their desired art style and theme.

[1263] Users input their desired style and theme into the interface, and the data is sent to the server via an AJAX request. The server generates new art ideas based on this data and sends the results to the device, where the user can view and select from the presented ideas.

[1264] User Action

[1265] Users first log in to the system through a terminal interface and enter their requirements and preferences for their art creation, such as specific preferences such as "abstract style" or "art themed around natural elements."

[1266] The user reviews the ideas sent from the server and selects the one they like best. After making their selection, they then create the actual artwork, and the results are fed back to the system via their device. At this time, the user's emotional data, as recognized by the emotion engine, is also fed back.

[1267] In this way, the system of the present invention enables the server, terminal, user, and emotion engine to work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[1268] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1269] Step 1:

[1270] The server collects feature data. As input, it obtains data about contemporary art from social media APIs and web scraping tools. This includes social media (e.g., posts tagged "contemporary art" using the Instagram API), online art galleries, and specialized magazines. Specifically, it sends API requests and receives the retrieved data in JSON format. The output is the collected image data and text data.

[1271] Step 2:

[1272] The server stores the collected data in a database. The input is the collected feature data. For example, the data is temporarily stored in a database such as MongoDB. Specifically, it executes an insert query on the database to save image data and text data. The output is the uncleaned data stored in the database.

[1273] Step 3:

[1274] The server performs data cleaning. The input is the raw feature data stored in the database. It uses Python regular expressions to remove advertisements and irrelevant content from the text data and converts the image data into a unified format (e.g., JPEG). Specific operations include filtering the text data and converting the image data format. The output is clean data converted into a format suitable for analysis.

[1275] Step 4:

[1276] The server performs trend analysis using cleaned data. The input is cleaned feature data. Natural language processing (NLP) techniques are used to extract frequently occurring keywords and themes from the text data, and Python libraries (NLTK and OpenCV) are used to analyze popular colors, shapes, and compositions from image data. Specifically, it performs text analysis using keyword extraction algorithms and image processing algorithms. The output is trend data extracted as frequently occurring keywords, themes, colors, shapes, compositions, etc.

[1277] Step 5:

[1278] The server runs a generative AI model based on the trend data to generate ideas for new artworks. The input is the trend data and a generative AI model (e.g., GPT-4 model). Specifically, the server gives the generative AI model the following prompt:

[1279] Please generate ideas for artwork with the theme of "inspirational landscapes." Please also suggest specific colors, shapes, and compositions.

[1280] The output is the generated new art idea.

[1281] Step 6:

[1282] The server saves the generated art ideas in a database. The input is the generated art ideas. The specific operation is to execute an insert query on the database to save the art ideas. The output is the art ideas saved in the database.

[1283] Step 7:

[1284] The terminal provides a user interface. The input is the art idea sent from the server and the user's input data. Through an interface built with React.js or Vue.js, it provides fields for the user to enter their desired art style and theme. Specific operations include rendering a form and accepting user input. The output is the user's input data.

[1285] Step 8:

[1286] A user operates the terminal interface to log in to the system. The input is the user's authentication information. Specifically, the user enters a username and password into the login form and sends an authentication request to the server. The output is the authenticated user's session information.

[1287] Step 9:

[1288] The user inputs their requirements and desires for their art creation. The input is the user's desired style or theme. Specific actions include entering "abstract style" or "art themed around natural elements" into a text box on the interface. The output is the user's input data, which is sent to the server.

[1289] Step 10:

[1290] The server generates new art ideas based on the user's wishes and sends them to the device. The inputs are the user's input data and the generative AI model. Specifically, the server provides the user's wishes as prompts to the generative AI model, which then generates new art ideas. The output is the new art ideas sent to the device.

[1291] Step 11:

[1292] The terminal presents the art ideas sent from the server to the user. The input is a new art idea. Specific operations include rendering the art idea received from the server into HTML and displaying it on the interface. The output is the art idea presented to the user.

[1293] Step 12:

[1294] The user selects an art idea and then begins to create it. The input is the art idea presented to the user. The specific action is to select the one they like best from the multiple art ideas presented. The output is the selected art idea.

[1295] Step 13:

[1296] After creating an artwork, the user feeds the results back to the system via their device. The input is an image file of the artwork and comments. Specifically, the user uploads the image file of the artwork and comments and sends them to the server. The output is the feedback data sent to the server.

[1297] Step 14:

[1298] The emotion engine acquires emotion data in real time while the user is viewing the design and sends it to the server. Inputs include the user's facial expressions, click patterns, and operation logs. Specifically, it captures the user's facial expressions with a camera and sends them to an API for emotion analysis. The output is analyzed emotion data.

[1299] Step 15:

[1300] The server analyzes the emotion data and uses it as feedback for the next generation of art ideas. The input is the emotion data sent from the emotion engine. The specific operation is to log the emotion data and reflect it in the generative AI model. The output is an improved generative algorithm.

[1301] As described above, the server, terminal, user, and emotion engine work together to analyze trends in contemporary art and help create innovative and meaningful art based on the user's emotions.

[1302] (Application example 2)

[1303] 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."

[1304] Modern security systems require users to continuously monitor surveillance camera footage for long periods of time, which requires a huge amount of effort and can lead to missed events. Furthermore, when detecting anomalies, it is difficult to provide appropriate feedback based on the user's emotions and psychological state, making it difficult to properly highlight important or high-risk scenes. Therefore, there is a need for an efficient and effective surveillance system that improves the user experience.

[1305] 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.

[1306] In this invention, the server includes means for collecting feature data, means for analyzing the collected feature data to extract trends, means for generating new designs based on the trends, means for presenting the generated designs, means for receiving input data from a user and providing designs based on the input data, means for analyzing user emotions in real time, and means for highlighting important scenes based on the analyzed emotion data. This makes it possible to improve the user experience and increase the efficiency of monitoring operations by detecting anomalies while taking the user's emotional state into consideration and highlighting important scenes and high-risk scenes.

[1307] "Feature data" refers to basic information that the system uses for analysis and generation, including, for example, image data, text data, and social media posts.

[1308] "Analysis" is the process of processing collected data to extract trends, using natural language processing and image processing algorithms to find important features and patterns.

[1309] "Trends" are specific patterns or themes in the data that are derived through analysis and serve as the basis for generating new designs.

[1310] "Generation" is the process of generating new designs and ideas based on trend data, and is done using generative AI models.

[1311] "Emotional data" refers to psychological and emotional information obtained from users' facial expressions, click patterns, operation logs, etc.

[1312] "User Interface" means the graphical operating screen through which a user can access the system and input their desired style and theme.

[1313] The present invention relates to an advanced security feedback system that analyzes user emotional data in real time and highlights important scenes, thereby enabling security operations to be carried out efficiently and effectively.

[1314] Server Processing

[1315] The server first collects feature data, which includes surveillance footage, click patterns, operation logs, etc. This feature data is collected using video analysis technology such as OpenCV and web scraping technology.

[1316] After feature data is collected, it undergoes a data cleaning process and is converted into a format suitable for analysis. The cleaned data is used for trend analysis and generative AI models are run to detect anomalies and patterns.

[1317] The generated anomaly and pattern information is stored in a database to create a baseline for highlighting important scenes based on the user's emotional data, which can then be used for future analyses.

[1318] Emotion engine processing

[1319] The emotion engine analyzes users' emotions in real time as they view surveillance footage. It collects data to identify the emotions of users through facial expression recognition and click pattern analysis. The collected emotion data is then sent to a server for analysis.

[1320] Terminal handling

[1321] The terminal provides an interface for the security feedback system, allowing users to easily view surveillance footage and see scenes highlighted if anomalies are detected. The terminal includes a field for users to input their desired surveillance style or theme.

[1322] The input data is sent to the server and used for real-time analysis. The device then presents the highlighted scene sent from the server to the user. While the user is viewing the presented scene, the emotion engine analyzes the emotion data in real time and uses it as feedback for the next analysis.

[1323] User Action

[1324] The user first operates the terminal interface and logs into the system. Then, they input their monitoring preferences and requirements. The system then performs analysis based on the user's preferences and optimizes anomaly detection.

[1325] While the user is viewing the video, the emotion engine analyzes the user's emotions, and the data is sent to the server. This allows the server to highlight important or high-risk scenes. The user checks the highlighted scenes presented by the system and provides feedback. This improves the accuracy of analysis from the next time onwards, enabling more efficient monitoring.

[1326] Examples of concrete examples and prompts

[1327] As a concrete example, suppose a user is watching a surveillance camera and suddenly sees a scene with a lot of movement, and expresses surprise. The system stores this scene so that similar anomalies are noticed in future surveillance. An example of a prompt sentence is as follows:

[1328] "Detect any anomalies in this camera feed and highlight important scenes in real time, taking into account the user's emotions."

[1329] In this way, the system of the present invention contributes to improving the efficiency of security analysis and monitoring work based on user emotions through cooperation between the server, terminal, user, and emotion engine.

[1330] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1331] Step 1:

[1332] The server collects feature data from the surveillance camera footage. In this process, it captures the surveillance footage frame by frame and extracts the image data using OpenCV. The input is the surveillance camera video feed, and the output is the feature data (image frames).

[1333] Step 2:

[1334] The server cleans the collected feature data and converts it into a format suitable for analysis. Specifically, it performs noise removal and image standardization. The input is the feature data obtained in step 1, and the output is the cleaned data.

[1335] Step 3:

[1336] The server analyzes the cleaned data to detect anomalies. It uses the AnomalyDetection library to identify specific patterns or anomalies in the frames. The input is the cleaned data, and the output is the frame information containing the anomaly and its location data.

[1337] Step 4:

[1338] The terminal uses the analyzed data to highlight abnormalities in real time on the user interface, notifying the user by, for example, surrounding the area where an abnormality is detected with a red frame. The input is the abnormal data sent from the server, and the output is a user interface with the abnormality highlighted.

[1339] Step 5:

[1340] The emotion engine analyzes the user's facial expressions and operation patterns to collect emotional data in real time. It uses the EmotionRecognition library to collect facial expression data and also analyzes click and operation logs. The input is the user's facial image and operation log, and the output is the user's emotional data.

[1341] Step 6:

[1342] The server re-highlights abnormal scenes based on the emotion data. It analyzes the user's emotion data and prioritizes highlighting scenes that evoke emotions such as "surprise" and "anxiety." The input is emotion data and abnormal data, and the output is scenes that are highlighted based on the emotion feedback.

[1343] Step 7:

[1344] The user reviews the highlighted scenes and provides feedback as needed. The reviewed scenes and emotional feedback are input into the system to help with the next analysis. The input is the user's feedback data, and the output is an updated analysis algorithm.

[1345] In this way, the server, terminal, and emotion engine work together to detect and highlight anomalies based on the user's emotions, thereby realizing an efficient and effective security feedback system.

[1346] 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.

[1347] 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.

[1348] 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 robot 414.

[1349] 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.

[1350] 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.

[1351] 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.

[1352] 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).

[1353] 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.

[1354] 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."

[1355] 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.

[1356] 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).

[1357] 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.

[1358] 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.

[1359] 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.

[1360] 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.

[1361] 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.

[1362] 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.

[1363] 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.

[1364] 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.

[1365] 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.

[1366] 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.

[1367] The following is further disclosed regarding the above embodiment.

[1368] (Claim 1)

[1369] means for collecting feature data;

[1370] A means for analyzing the collected feature data to extract trends;

[1371] a means for generating new designs based on trends;

[1372] a means for presenting the generated design;

[1373] A system including means for receiving input data from a user and providing a design based on the input data.

[1374] (Claim 2)

[1375] 10. The system of claim 1, further comprising means for providing a user interface to allow a user to input a desired style or theme.

[1376] (Claim 3)

[1377] 10. The system of claim 1, further comprising means for receiving feedback from a user and analyzing the feedback to aid in improving the generating means.

[1378] "Example 1"

[1379] (Claim 1)

[1380] A means of collecting data on contemporary art;

[1381] A means for storing the collected data in a database and cleaning the data to remove noise;

[1382] A trend analysis method that uses natural language processing technology to analyze clean data and extract frequently occurring keywords and themes;

[1383] A trend analysis means that uses image processing technology to analyze image data and extract colors and shapes;

[1384] means for operating a generative AI model that generates new artwork ideas based on the trend data;

[1385] means for storing the generated art ideas in a database and displaying them through a user interface;

[1386] A system that includes a means for receiving input data from a user and re-running the generative AI model based on the input data to provide optimal art ideas.

[1387] (Claim 2)

[1388] 10. The system of claim 1, further comprising means for providing a user interface to allow a user to input a desired art style or theme.

[1389] (Claim 3)

[1390] 10. The system of claim 1, further comprising means for receiving feedback from a user and analyzing the feedback to help improve the generative AI model.

[1391] "Application Example 1"

[1392] (Claim 1)

[1393] means for collecting feature data;

[1394] A means for analyzing the collected feature data to extract trends;

[1395] a means for generating new designs based on trends;

[1396] a means for presenting the generated design;

[1397] means for receiving input data from a user and providing a design based on the input data;

[1398] A system that includes a means for generating art ideas by running a generative AI model based on a theme and style specified by the user.

[1399] (Claim 2)

[1400] 10. The system of claim 1, further comprising means for providing a user interface to allow a user to input a desired style or theme.

[1401] (Claim 3)

[1402] 10. The system of claim 1, further comprising: means for receiving feedback from users and analyzing the feedback to aid in improving the generating means; and means for displaying trend data of generated ideas.

[1403] "Example 2: Combining Emotion Engines"

[1404] (Claim 1)

[1405] means for collecting feature data;

[1406] A means for analyzing the collected feature data to extract trends;

[1407] means for operating the generative AI model to generate new designs based on the trends;

[1408] A means for analyzing user emotion data in real time and using it as feedback;

[1409] means for providing a user interface for inputting a user's desired style or theme;

[1410] means for presenting the generated design to a user;

[1411] a means for receiving feedback from users and analyzing the feedback to help improve the generating means;

[1412] A system including:

[1413] (Claim 2)

[1414] 10. The system of claim 1, further comprising means for providing a graphical user interface for user access, allowing the user to input a desired design and view ideas generated based on the design.

[1415] (Claim 3)

[1416] 10. The system of claim 1, further comprising means for using an emotion engine to recognize a user's emotions in real time while the user is viewing the generated design, and transmitting the information to a server, thereby enabling subsequent design generation to provide designs that take the user's emotions into consideration.

[1417] "Application example 2 when combining emotion engines"

[1418] (Claim 1)

[1419] means for collecting feature data;

[1420] A means for analyzing the collected feature data to extract trends;

[1421] a means for generating new designs based on trends;

[1422] a means for presenting the generated design;

[1423] means for receiving input data from a user and providing a design based on the input data;

[1424] A means for analyzing user emotions in real time;

[1425] A means of highlighting important scenes based on analyzed emotion data

[1426] A system including:

[1427] (Claim 2)

[1428] 10. The system of claim 1, further comprising means for providing a user interface to allow a user to input a desired style or theme.

[1429] (Claim 3)

[1430] 10. The system of claim 1, further comprising means for receiving feedback from a user and analyzing the feedback to aid in improving the generating means. [Explanation of symbols]

[1431] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting feature data; A means for analyzing the collected feature data to extract trends; a means for generating new designs based on trends; a means for presenting the generated design; A system including means for receiving input data from a user and providing a design based on the input data.

2. 10. The system of claim 1, further comprising means for providing a user interface to allow a user to input a desired style or theme.

3. The system of claim 1 further comprising means for receiving feedback from a user and analyzing the feedback to aid in improving the generating means.

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