system

Abstract

The system according to this embodiment aims to generate unique digital art using the user's location information and environmental data, and to save, share, and sell it. [Solution] The system according to the embodiment comprises an acquisition unit, a collection unit, an analysis unit, a generation unit, a storage unit, a sharing unit, and a sales unit. The acquisition unit acquires the user's location information. The collection unit collects environmental data based on the location information acquired by the acquisition unit. The analysis unit analyzes the data collected by the collection unit. The generation unit generates digital art based on the data analyzed by the analysis unit. The storage unit, sharing unit, and sales unit store, share, and sell the digital art generated by the generation unit as NFTs.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it has not been fully carried out to utilize the user's location information and environmental data to generate unique digital art and save, share, and sell it.

[0005] The system according to the embodiment aims to utilize the user's location information and environmental data to generate unique digital art and save, share, and sell it.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a collection unit, an analysis unit, a generation unit, a storage unit, a sharing unit, and a sales unit. The acquisition unit acquires the user's location information. The collection unit collects environmental data based on the location information acquired by the acquisition unit. The analysis unit analyzes the data collected by the collection unit. The generation unit generates digital art based on the data analyzed by the analysis unit. The storage unit, sharing unit, and sales unit store, share, and sell the digital art generated by the generation unit as NFTs. [Effects of the Invention]

[0007] The system according to this embodiment can generate unique digital art using the user's location information and environmental data, and can save, share, and sell it. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The digital art generation system according to an embodiment of the present invention is a system that uses AI to analyze environmental data, organisms, landscapes, and cultural properties at a travel destination and generates digital art unique to that moment. The digital art generation system is an application that uses AI to analyze environmental data, organisms, landscapes, and cultural properties at a travel destination and generates digital art unique to that moment. The generated art can be saved, shared, and sold as an NFT. Specifically, it consists of the following steps. First, the digital art generation system acquires the user's location information and inputs photos taken by the user and their impressions into the application. Next, the digital art generation system refers to local organism and architectural information and acquires environmental data such as weather, temperature, and air quality in real time. This data is collected using an API. The collected data is analyzed by an AI art generation engine. The user can select their preferred art style and colors, generate art on the spot, and immediately check and share it. Based on the analysis results, a unique digital art is generated. Complex data integration and creation that would not be possible without the generation AI are performed. The generated art can be saved, shared, and sold as an NFT. The digital art generation system eliminates complex procedures, issuing digital art as NFTs with a single button click. The system automatically handles background processing such as smart contract execution and gas fee calculations, automatically selecting the most environmentally friendly and low-fee blockchain. The system can connect to major cryptocurrency wallets with a single tap from within the app. This digital art generation system can promote tourism by showcasing the natural and cultural attractions of local areas. It can also expand the application range of generation AI and accelerate its implementation in society. Furthermore, it contributes to the spread of blockchain technology and the creation of new digital assets. As a result, the digital art generation system can collect and analyze environmental data based on the user's location, generate digital art, and store, share, and sell it as an NFT.

[0029] The digital art generation system according to the embodiment comprises an acquisition unit, a collection unit, an analysis unit, a generation unit, a storage unit, a sharing unit, and a sales unit. The acquisition unit acquires the user's location information. The acquisition unit can acquire location information using, for example, GPS data, Wi-Fi location information, cell tower data, etc. The collection unit collects environmental data based on the location information acquired by the acquisition unit. The collection unit can collect environmental data such as weather information, temperature, air quality, noise level, etc. The collection unit collects data using an API. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, data mining, machine learning algorithms, statistical analysis, etc. The generation unit generates digital art based on the data analyzed by the analysis unit. The generation unit can generate digital art in, for example, images, videos, 3D models, etc. The generation unit generates digital art using a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. The storage unit, sharing unit, and sales unit store, share, and sell the digital art generated by the generation unit as NFTs. The storage unit can store the digital art using methods such as cloud storage, local storage, and blockchain. The sharing unit can share the digital art using methods such as social media, email, and dedicated apps. The sales unit can sell the digital art using methods such as online marketplaces, auctions, and direct sales. Thus, the digital art generation system according to the embodiment can collect and analyze environmental data based on the user's location information, generate digital art, and store, share, and sell it as an NFT.

[0030] The acquisition unit acquires the user's location information. The acquisition unit can acquire location information using, for example, GPS data, Wi-Fi location information, and cell tower data. Specifically, GPS data uses the GPS module installed in the user's device to acquire latitude and longitude information with high accuracy. Wi-Fi location information is a technology that estimates location based on the signal strength of surrounding Wi-Fi access points, and is particularly accurate indoors and in urban areas. Cell tower data is a method of determining location using communication information with cellular base stations, and is suitable for acquiring location information over a wide area. This location information is acquired in real time and used to accurately determine the user's current location. Furthermore, the acquisition unit can combine multiple location information acquisition methods to improve the accuracy of location information. For example, by combining GPS data and Wi-Fi location information, highly accurate location information can be acquired both outdoors and indoors. The acquisition unit can also save a history of location information and analyze past travel routes. This allows for understanding the user's behavior patterns and providing more personalized services.

[0031] The data collection unit collects environmental data based on location information acquired by the data acquisition unit. The data collection unit can collect environmental data such as weather information, temperature, air quality, and noise levels. Specifically, weather information is obtained using the OpenWeatherMap API to acquire current weather and forecasts. Weather data such as temperature and humidity are also acquired in a similar manner, providing detailed environmental information tailored to the user's location. Air quality data is obtained using the IQAir AirVisual API to acquire concentrations of air pollutants such as PM2.5, PM10, and nitrogen dioxide. Noise levels can be measured using the microphone built into the user's device to assess the surrounding sound environment. Furthermore, the data collection unit can also collect information on flora and fauna present around the user using the iNaturalist API and GBIF API. This allows for the acquisition of detailed data on the natural environment of the user's current location. The data collection unit collects this data in real time and transmits it to a central database. The collected data is managed so that the analysis and generation units can access it and utilize it as information necessary for generating digital art.

[0032] The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using methods such as data mining, machine learning algorithms, and statistical analysis. Specifically, it uses data mining techniques to extract useful patterns and trends from collected environmental data. Machine learning algorithms build models based on the collected data to perform predictions and classifications. For example, it can predict user behavior patterns under specific environmental conditions based on temperature, humidity, and air quality data. Statistical analysis is used to clarify data distribution and correlations and to evaluate data reliability and significance. By combining these techniques, the analysis unit analyzes the collected data from multiple perspectives and provides the insights necessary for generating digital art. Furthermore, the analysis unit can perform more accurate analyses by utilizing historical data and publicly available external data. For example, it can predict future weather fluctuations based on historical weather data and weather forecasts, and reflect these in the themes and styles of digital art. This allows the analysis unit to effectively analyze the collected data and provide the information necessary for generating digital art.

[0033] The generation unit generates digital art based on data analyzed by the analysis unit. The generation unit can generate digital art in various formats, such as images, videos, and 3D models. The generation unit uses generation AI to generate digital art. Generation AI can include text generation AI (e.g., LLM) and multimodal generation AI. Specifically, the generation AI receives data from the analysis unit as input and initiates the digital art generation process. For example, it can generate images in line with a specific theme or style based on weather information, temperature, and air quality data. The generation AI uses pre-trained models to generate high-quality digital art based on the input data. Text generation AI can generate text content such as poems and stories based on data provided by the analysis unit. Multimodal generation AI can combine multiple data formats, such as images, text, and audio, to generate more complex and diverse digital art. The generation unit can provide the generated digital art to the user in real time and improve the generation process based on user feedback. This allows the generation unit to generate and provide personalized digital art to the user based on their location and environmental data.

[0034] The storage, sharing, and sales units store, share, and sell the digital art generated by the generation unit as NFTs. The storage unit can store digital art using methods such as cloud storage, local storage, and blockchain. Specifically, cloud storage stores data via the internet, making it accessible from anywhere. Local storage stores data directly on the user's device, making it accessible even offline. Blockchain is a technology that proves ownership of digital art and stores data in an immutable form. The sharing unit can share digital art using methods such as social media, email, and dedicated apps. Social media is a platform for publishing digital art to a wide range of users and obtaining feedback. Email is a method for directly sending digital art to specific users and is suitable for individual communication. Dedicated apps are dedicated platforms for viewing, sharing, and purchasing digital art. The sales unit can sell digital art using methods such as online marketplaces, auctions, and direct sales. Online marketplaces are platforms for selling digital art, allowing products to be published to many users. Auctions are a method of selling digital art to the highest bidder through bidding. Direct sales involve negotiating directly with users to sell digital art and are suitable for individual transactions. This allows the storage, sharing, and sales departments to effectively store, share, and sell the generated digital art, providing value to users.

[0035] The acquisition unit includes a reception unit for inputting photos and comments taken by the user. The reception unit can accept photos and comments in various formats, such as JPEG images, text comments, and voice memos. The reception unit can accept photos taken by the user in JPEG format. The reception unit can accept comments from the user as text comments. The reception unit can accept comments from the user as voice memos. This allows for the acquisition of more detailed data by inputting photos and comments taken by the user. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input photos and comments entered by the user into an AI and have the AI ​​perform analysis of the photos and comments.

[0036] The data collection unit references local biodiversity and building information to acquire environmental data such as weather, temperature, and air quality in real time. The data collection unit can reference information such as the types of plants and animals, and the history and structure of buildings. The data collection unit can acquire weather information in real time. The data collection unit can acquire temperature in real time. The data collection unit can acquire air quality in real time. By referencing local biodiversity and building information and acquiring environmental data in real time, more accurate data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can input local biodiversity and building information into AI and have the AI ​​perform the analysis of the information.

[0037] The analysis unit analyzes the collected data and includes a selection unit that allows the user to select their preferred art style and colors. The selection unit can select art styles and colors such as abstract painting, realistic painting, warm colors, and cool colors. The selection unit allows the user to select abstract painting. The selection unit allows the user to select realistic painting. The selection unit allows the user to select warm colors. The selection unit allows the user to select cool colors. This allows the user to create more personalized digital art by selecting their preferred art style and colors. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the art style and colors selected by the user into the AI ​​and have the AI ​​perform an analysis of the art style and colors.

[0038] The generation unit generates unique digital art based on the analysis results. The generation unit can generate unique digital art using methods such as a random generation algorithm or user-specific data. The generation unit can generate digital art using a random generation algorithm. The generation unit can generate digital art using user-specific data. The generation unit generates digital art using a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the generation unit to provide original artwork by generating unique digital art based on the analysis results. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input the analysis results into a generation AI and have the generation AI perform the generation of digital art.

[0039] The storage, sharing, and sales departments store, share, and sell the generated digital art as NFTs. The storage department can store digital art using methods such as cloud storage, local storage, and blockchain. The sharing department can share digital art using methods such as social media, email, and dedicated apps. The sales department can sell digital art using methods such as online marketplaces, auctions, and direct sales. The storage department can store digital art using cloud storage. The storage department can store digital art using local storage. The storage department can store digital art using blockchain. The sharing department can share digital art using social media. The sharing department can share digital art using email. The sharing department can share digital art using a dedicated app. The sales department can sell digital art using online marketplaces. The sales department can sell digital art using auctions. The sales department can sell digital art using direct sales. This increases the value of digital art by storing, sharing, and selling the generated digital art as NFTs. Some or all of the above-mentioned processes in the storage, sharing, and sales departments may be performed using AI or not. For example, the storage department can input digital art into the AI ​​and have the AI ​​select the storage method.

[0040] The acquisition unit analyzes the user's past travel history and selects the optimal method for acquiring location information. The acquisition unit can adjust the frequency of location information acquisition based on data of places the user has visited in the past. The acquisition unit can analyze the user's past travel patterns and determine the optimal timing for acquiring location information. The acquisition unit can improve the accuracy of location information acquisition by referring to environmental data of places the user has visited in the past. This allows the acquisition unit to select the optimal method for acquiring location information by analyzing the user's past travel history. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's past travel data into AI and have the AI ​​select the optimal method for acquiring location information.

[0041] The acquisition unit filters location information based on the user's current activities and areas of interest. For example, if the user is sightseeing, the acquisition unit can prioritize acquiring location information around tourist spots. If the user is observing nature, the acquisition unit can prioritize acquiring location information for nature reserves and parks. If the user is interested in cultural properties, the acquisition unit can prioritize acquiring location information for historical buildings and museums. By filtering location information based on the user's current activities and areas of interest, highly relevant information can be obtained. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input data on the user's activities and areas of interest into an AI and have the AI ​​perform the filtering.

[0042] The acquisition unit prioritizes acquiring highly relevant information by considering the user's geographical location when acquiring location information. For example, if the user is in an urban area, the acquisition unit can prioritize acquiring location information for tourist attractions and restaurants. If the user is in a suburban area, the acquisition unit can prioritize acquiring location information for nature reserves and parks. If the user is in a historical site, the acquisition unit can prioritize acquiring location information for cultural properties and museums. In this way, highly relevant information can be prioritized by considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, or it may be performed without using AI. For example, the acquisition unit can input the user's geographical location information into AI and have the AI ​​select highly relevant information.

[0043] The acquisition unit analyzes the user's social media activity when acquiring location information and obtains relevant information. For example, the acquisition unit can prioritize acquiring location information of places shared by the user on social media. The acquisition unit can analyze the content of posts from accounts that the user follows and obtain relevant location information. The acquisition unit can acquire location information based on event information that the user plans to attend. In this way, relevant information can be obtained by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's social media activity data into AI and have the AI ​​select relevant information.

[0044] The data collection unit applies the optimal collection algorithm by referring to past data when collecting environmental data. For example, the data collection unit can determine the optimal collection timing based on past weather data. The data collection unit can adjust the collection frequency by referring to past air quality data. The data collection unit can predict the frequency of appearance of a specific organism based on past biological observation data and collect that data. This allows the optimal collection algorithm to be applied by referring to past data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past data into AI and have the AI ​​select the optimal collection algorithm.

[0045] The data collection unit focuses on specific organisms or buildings when collecting environmental data. For example, if a user is interested in a particular organism, the data collection unit can prioritize collecting environmental data related to that organism. If a user is interested in a particular building, the data collection unit can prioritize collecting environmental data related to that building. If a user is interested in a particular landscape, the data collection unit can prioritize collecting environmental data related to that landscape. This allows for the collection of highly relevant data by focusing on specific organisms or buildings. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data about specific organisms or buildings into an AI and have the AI ​​select the collection method.

[0046] The data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location when collecting environmental data. For example, if the user is in an urban area, the data collection unit can prioritize the collection of air quality and noise level data. If the user is in a nature reserve, the data collection unit can prioritize the collection of biodiversity and temperature data. If the user is in a historical site, the data collection unit can prioritize the collection of cultural property and architectural data. In this way, by considering the user's geographical location, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​select highly relevant data.

[0047] The data collection unit analyzes the user's social media activity and collects relevant data when collecting environmental data. For example, the data collection unit can prioritize collecting environmental data on locations shared by the user on social media. The data collection unit can analyze the content of posts from accounts that the user follows and collect relevant environmental data. The data collection unit can collect environmental data based on information about events the user plans to attend. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into AI and have the AI ​​select relevant data.

[0048] The analysis unit applies the optimal analysis algorithm by referring to past analysis results during data analysis. For example, the analysis unit can select the optimal analysis algorithm based on past analysis results. The analysis unit can improve analysis accuracy by referring to past analysis results. The analysis unit can shorten analysis time based on past analysis results. This allows the optimal analysis algorithm to be applied by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past analysis results into AI and have the AI ​​select the optimal analysis algorithm.

[0049] The analysis unit performs analysis that focuses on specific art styles or colors during data analysis. For example, the analysis unit can perform data analysis based on an art style selected by the user. The analysis unit can perform data analysis based on a color selected by the user. The analysis unit can perform data analysis based on a theme selected by the user. This allows for analysis results tailored to the user's preferences by focusing on specific art styles or colors. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the art style or color selected by the user into an AI and have the AI ​​perform the analysis.

[0050] The analysis unit prioritizes the analysis of highly relevant data, taking into account the user's geographical location during data analysis. For example, if the user is in an urban area, the analysis unit can prioritize the analysis of air quality and noise level data. If the user is in a nature reserve, the analysis unit can prioritize the analysis of biodiversity and temperature data. If the user is in a historical site, the analysis unit can prioritize the analysis of cultural property and architectural data. In this way, by considering the user's geographical location, the analysis unit can prioritize the analysis of highly relevant data. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the user's geographical location information into AI and have the AI ​​select highly relevant data.

[0051] The analysis unit analyzes the user's social media activity and analyzes relevant data during data analysis. For example, the analysis unit can prioritize the analysis of data on locations shared by the user on social media. The analysis unit can analyze the content of posts from accounts that the user follows and analyze relevant data. The analysis unit can analyze data based on information about events the user plans to attend. In this way, relevant data can be analyzed by analyzing the user's social media activity. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's social media activity data into AI and have the AI ​​select relevant data.

[0052] The generation unit applies the optimal generation algorithm by referring to past generation results when generating digital art. For example, the generation unit can select the optimal generation algorithm based on past generation results. The generation unit can improve generation accuracy by referring to past generation results. The generation unit can shorten generation time based on past generation results. This allows the optimal generation algorithm to be applied by referring to past generation results. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input past generation results into a generation AI and have the generation AI select the optimal generation algorithm.

[0053] The generation unit focuses on specific art styles and colors when generating digital art. For example, the generation unit can generate digital art based on an art style selected by the user. The generation unit can generate digital art based on a color selected by the user. The generation unit can generate digital art based on a theme selected by the user. This allows for the generation of digital art tailored to the user's preferences by focusing on specific art styles and colors. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input data on the art style and color selected by the user into a generation AI and have the generation AI perform the generation.

[0054] The generation unit prioritizes generating highly relevant art by considering the user's geographical location information when generating digital art. For example, if the user is in an urban area, the generation unit can generate digital art themed on urban landscapes. If the user is in a nature reserve, the generation unit can generate digital art themed on natural landscapes. If the user is in a historical site, the generation unit can generate digital art themed on cultural properties or historical buildings. In this way, by considering the user's geographical location information, the generation unit can prioritize generating highly relevant art. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI perform the generation of highly relevant art.

[0055] The generation unit analyzes the user's social media activity when generating digital art and generates relevant art. For example, the generation unit can generate digital art themed around landscapes of places shared by the user on social media. The generation unit can analyze the content of posts from accounts the user follows and generate relevant digital art. The generation unit can generate digital art based on information about events the user plans to attend. In this way, relevant art can be generated by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of relevant art.

[0056] The storage, sharing, and sales units apply the optimal storage algorithm when saving NFTs by referring to past saved data. For example, the storage, sharing, and sales units can select the optimal storage algorithm based on past saved data. The storage, sharing, and sales units can improve storage accuracy by referring to past saved data. The storage, sharing, and sales units can shorten storage time based on past saved data. This allows the optimal storage algorithm to be applied by referring to past saved data. Some or all of the above processes in the storage, sharing, and sales units may be performed using AI or not. For example, the storage, sharing, and sales units can input past saved data into AI and have the AI ​​select the optimal storage algorithm.

[0057] The storage, sharing, and sales units focus on specific blockchain technologies when storing NFTs. For example, the storage, sharing, and sales units can store NFTs based on a blockchain technology selected by the user. The storage, sharing, and sales units can provide the optimal storage method considering the characteristics of the blockchain technology selected by the user. The storage, sharing, and sales units can adjust the storage method considering the security characteristics of the blockchain technology selected by the user. This allows for the provision of the optimal storage method by focusing on a specific blockchain technology. Some or all of the above processes in the storage, sharing, and sales units may be performed using AI or not. For example, the storage, sharing, and sales units can input data on the blockchain technology selected by the user into an AI and have the AI ​​select the storage method.

[0058] The storage, sharing, and sales departments prioritize saving highly relevant NFTs when saving NFTs, taking into account the user's geographical location information. For example, if the user is in an urban area, the storage, sharing, and sales departments can save NFTs themed around urban landscapes. If the user is in a nature reserve, the storage, sharing, and sales departments can save NFTs themed around natural landscapes. If the user is in a historical site, the storage, sharing, and sales departments can save NFTs themed around cultural properties or historical buildings. This allows for the priority saving of highly relevant NFTs by considering the user's geographical location information. Some or all of the above processing in the storage, sharing, and sales departments may be performed using AI or not. For example, the storage, sharing, and sales departments can input the user's geographical location information into the AI ​​and have the AI ​​select highly relevant NFTs.

[0059] The storage, sharing, and sales units analyze the user's social media activity when saving NFTs and save relevant NFTs. For example, the storage, sharing, and sales units can save NFTs themed around landscapes of places shared by the user on social media. The storage, sharing, and sales units can analyze the content of posts from accounts the user follows and save relevant NFTs. The storage, sharing, and sales units can save NFTs based on information about events the user plans to attend. In this way, relevant NFTs can be saved by analyzing the user's social media activity. Some or all of the above processes in the storage, sharing, and sales units may be performed using AI or not. For example, the storage, sharing, and sales units can input the user's social media activity data into AI and have the AI ​​select relevant NFTs.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The acquisition unit analyzes the user's past travel history and selects the optimal method for acquiring location information. The acquisition unit can adjust the frequency of location information acquisition based on data of places the user has visited in the past. The acquisition unit can analyze the user's past travel patterns and determine the optimal timing for acquiring location information. The acquisition unit can improve the accuracy of location information acquisition by referring to environmental data of places the user has visited in the past. This allows the acquisition unit to select the optimal method for acquiring location information by analyzing the user's past travel history. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's past travel data into AI and have the AI ​​select the optimal method for acquiring location information.

[0062] The acquisition unit filters location information based on the user's current activities and areas of interest. For example, if the user is sightseeing, the acquisition unit can prioritize acquiring location information around tourist spots. If the user is observing nature, the acquisition unit can prioritize acquiring location information for nature reserves and parks. If the user is interested in cultural properties, the acquisition unit can prioritize acquiring location information for historical buildings and museums. By filtering location information based on the user's current activities and areas of interest, highly relevant information can be obtained. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input data on the user's activities and areas of interest into an AI and have the AI ​​perform the filtering.

[0063] The acquisition unit prioritizes acquiring highly relevant information by considering the user's geographical location when acquiring location information. For example, if the user is in an urban area, the acquisition unit can prioritize acquiring location information for tourist attractions and restaurants. If the user is in a suburban area, the acquisition unit can prioritize acquiring location information for nature reserves and parks. If the user is in a historical site, the acquisition unit can prioritize acquiring location information for cultural properties and museums. In this way, highly relevant information can be prioritized by considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, or it may be performed without using AI. For example, the acquisition unit can input the user's geographical location information into AI and have the AI ​​select highly relevant information.

[0064] The data collection unit applies the optimal collection algorithm by referring to past data when collecting environmental data. For example, the data collection unit can determine the optimal collection timing based on past weather data. The data collection unit can adjust the collection frequency by referring to past air quality data. The data collection unit can predict the frequency of appearance of a specific organism based on past biological observation data and collect that data. This allows the optimal collection algorithm to be applied by referring to past data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past data into AI and have the AI ​​select the optimal collection algorithm.

[0065] The data collection unit focuses on specific organisms or buildings when collecting environmental data. For example, if a user is interested in a particular organism, the data collection unit can prioritize collecting environmental data related to that organism. If a user is interested in a particular building, the data collection unit can prioritize collecting environmental data related to that building. If a user is interested in a particular landscape, the data collection unit can prioritize collecting environmental data related to that landscape. This allows for the collection of highly relevant data by focusing on specific organisms or buildings. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data about specific organisms or buildings into an AI and have the AI ​​select the collection method.

[0066] The data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location when collecting environmental data. For example, if the user is in an urban area, the data collection unit can prioritize the collection of air quality and noise level data. If the user is in a nature reserve, the data collection unit can prioritize the collection of biodiversity and temperature data. If the user is in a historical site, the data collection unit can prioritize the collection of cultural property and architectural data. In this way, by considering the user's geographical location, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​select highly relevant data.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The acquisition unit acquires the user's location information. The acquisition unit can acquire location information using, for example, GPS data, Wi-Fi location information, cell tower data, etc. Step 2: The collection unit collects environmental data based on the location information acquired by the acquisition unit. The collection unit can collect environmental data such as weather information, temperature, air quality, and noise levels. The collection unit collects data using an API. Step 3: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using methods such as data mining, machine learning algorithms, and statistical analysis. Step 4: The generation unit generates digital art based on the data analyzed by the analysis unit. The generation unit can generate digital art in formats such as images, videos, and 3D models. The generation unit generates digital art using a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 5: The storage, sharing, and sales units store, share, and sell the digital art generated by the generation unit as NFTs. The storage unit can store the digital art using methods such as cloud storage, local storage, and blockchain. The sharing unit can share the digital art using methods such as social media, email, and dedicated apps. The sales unit can sell the digital art using methods such as online marketplaces, auctions, and direct sales.

[0069] (Example of form 2) The digital art generation system according to an embodiment of the present invention is a system that uses AI to analyze environmental data, organisms, landscapes, and cultural properties at a travel destination and generates digital art unique to that moment. The digital art generation system is an application that uses AI to analyze environmental data, organisms, landscapes, and cultural properties at a travel destination and generates digital art unique to that moment. The generated art can be saved, shared, and sold as an NFT. Specifically, it consists of the following steps. First, the digital art generation system acquires the user's location information and inputs photos taken by the user and their impressions into the application. Next, the digital art generation system refers to local organism and architectural information and acquires environmental data such as weather, temperature, and air quality in real time. This data is collected using an API. The collected data is analyzed by an AI art generation engine. The user can select their preferred art style and colors, generate art on the spot, and immediately check and share it. Based on the analysis results, a unique digital art is generated. Complex data integration and creation that would not be possible without the generation AI are performed. The generated art can be saved, shared, and sold as an NFT. The digital art generation system eliminates complex procedures, issuing digital art as NFTs with a single button click. The system automatically handles background processing such as smart contract execution and gas fee calculations, automatically selecting the most environmentally friendly and low-fee blockchain. The system can connect to major cryptocurrency wallets with a single tap from within the app. This digital art generation system can promote tourism by showcasing the natural and cultural attractions of local areas. It can also expand the application range of generation AI and accelerate its implementation in society. Furthermore, it contributes to the spread of blockchain technology and the creation of new digital assets. As a result, the digital art generation system can collect and analyze environmental data based on the user's location, generate digital art, and store, share, and sell it as an NFT.

[0070] The digital art generation system according to the embodiment comprises an acquisition unit, a collection unit, an analysis unit, a generation unit, a storage unit, a sharing unit, and a sales unit. The acquisition unit acquires the user's location information. The acquisition unit can acquire location information using, for example, GPS data, Wi-Fi location information, cell tower data, etc. The collection unit collects environmental data based on the location information acquired by the acquisition unit. The collection unit can collect environmental data such as weather information, temperature, air quality, noise level, etc. The collection unit collects data using an API. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, data mining, machine learning algorithms, statistical analysis, etc. The generation unit generates digital art based on the data analyzed by the analysis unit. The generation unit can generate digital art in, for example, images, videos, 3D models, etc. The generation unit generates digital art using a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. The storage unit, sharing unit, and sales unit store, share, and sell the digital art generated by the generation unit as NFTs. The storage unit can store the digital art using methods such as cloud storage, local storage, and blockchain. The sharing unit can share the digital art using methods such as social media, email, and dedicated apps. The sales unit can sell the digital art using methods such as online marketplaces, auctions, and direct sales. Thus, the digital art generation system according to the embodiment can collect and analyze environmental data based on the user's location information, generate digital art, and store, share, and sell it as an NFT.

[0071] The acquisition unit acquires the user's location information. The acquisition unit can acquire location information using, for example, GPS data, Wi-Fi location information, and cell tower data. Specifically, GPS data uses the GPS module installed in the user's device to acquire latitude and longitude information with high accuracy. Wi-Fi location information is a technology that estimates location based on the signal strength of surrounding Wi-Fi access points, and is particularly accurate indoors and in urban areas. Cell tower data is a method of determining location using communication information with cellular base stations, and is suitable for acquiring location information over a wide area. This location information is acquired in real time and used to accurately determine the user's current location. Furthermore, the acquisition unit can combine multiple location information acquisition methods to improve the accuracy of location information. For example, by combining GPS data and Wi-Fi location information, highly accurate location information can be acquired both outdoors and indoors. The acquisition unit can also save a history of location information and analyze past travel routes. This allows for understanding the user's behavior patterns and providing more personalized services.

[0072] The data collection unit collects environmental data based on location information acquired by the data acquisition unit. The data collection unit can collect environmental data such as weather information, temperature, air quality, and noise levels. Specifically, weather information is obtained using the OpenWeatherMap API to acquire current weather and forecasts. Weather data such as temperature and humidity are also acquired in a similar manner, providing detailed environmental information tailored to the user's location. Air quality data is obtained using the IQAir AirVisual API to acquire concentrations of air pollutants such as PM2.5, PM10, and nitrogen dioxide. Noise levels can be measured using the microphone built into the user's device to assess the surrounding sound environment. Furthermore, the data collection unit can also collect information on flora and fauna present around the user using the iNaturalist API and GBIF API. This allows for the acquisition of detailed data on the natural environment of the user's current location. The data collection unit collects this data in real time and transmits it to a central database. The collected data is managed so that the analysis and generation units can access it and utilize it as information necessary for generating digital art.

[0073] The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using methods such as data mining, machine learning algorithms, and statistical analysis. Specifically, it uses data mining techniques to extract useful patterns and trends from collected environmental data. Machine learning algorithms build models based on the collected data to perform predictions and classifications. For example, it can predict user behavior patterns under specific environmental conditions based on temperature, humidity, and air quality data. Statistical analysis is used to clarify data distribution and correlations and to evaluate data reliability and significance. By combining these techniques, the analysis unit analyzes the collected data from multiple perspectives and provides the insights necessary for generating digital art. Furthermore, the analysis unit can perform more accurate analyses by utilizing historical data and publicly available external data. For example, it can predict future weather fluctuations based on historical weather data and weather forecasts, and reflect these in the themes and styles of digital art. This allows the analysis unit to effectively analyze the collected data and provide the information necessary for generating digital art.

[0074] The generation unit generates digital art based on data analyzed by the analysis unit. The generation unit can generate digital art in various formats, such as images, videos, and 3D models. The generation unit uses generation AI to generate digital art. Generation AI can include text generation AI (e.g., LLM) and multimodal generation AI. Specifically, the generation AI receives data from the analysis unit as input and initiates the digital art generation process. For example, it can generate images in line with a specific theme or style based on weather information, temperature, and air quality data. The generation AI uses pre-trained models to generate high-quality digital art based on the input data. Text generation AI can generate text content such as poems and stories based on data provided by the analysis unit. Multimodal generation AI can combine multiple data formats, such as images, text, and audio, to generate more complex and diverse digital art. The generation unit can provide the generated digital art to the user in real time and improve the generation process based on user feedback. This allows the generation unit to generate and provide personalized digital art to the user based on their location and environmental data.

[0075] The storage, sharing, and sales units store, share, and sell the digital art generated by the generation unit as NFTs. The storage unit can store digital art using methods such as cloud storage, local storage, and blockchain. Specifically, cloud storage stores data via the internet, making it accessible from anywhere. Local storage stores data directly on the user's device, making it accessible even offline. Blockchain is a technology that proves ownership of digital art and stores data in an immutable form. The sharing unit can share digital art using methods such as social media, email, and dedicated apps. Social media is a platform for publishing digital art to a wide range of users and obtaining feedback. Email is a method for directly sending digital art to specific users and is suitable for individual communication. Dedicated apps are dedicated platforms for viewing, sharing, and purchasing digital art. The sales unit can sell digital art using methods such as online marketplaces, auctions, and direct sales. Online marketplaces are platforms for selling digital art, allowing products to be published to many users. Auctions are a method of selling digital art to the highest bidder through bidding. Direct sales involve negotiating directly with users to sell digital art and are suitable for individual transactions. This allows the storage, sharing, and sales departments to effectively store, share, and sell the generated digital art, providing value to users.

[0076] The acquisition unit includes a reception unit for inputting photos and comments taken by the user. The reception unit can accept photos and comments in various formats, such as JPEG images, text comments, and voice memos. The reception unit can accept photos taken by the user in JPEG format. The reception unit can accept comments from the user as text comments. The reception unit can accept comments from the user as voice memos. This allows for the acquisition of more detailed data by inputting photos and comments taken by the user. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input photos and comments entered by the user into an AI and have the AI ​​perform analysis of the photos and comments.

[0077] The data collection unit references local biodiversity and building information to acquire environmental data such as weather, temperature, and air quality in real time. The data collection unit can reference information such as the types of plants and animals, and the history and structure of buildings. The data collection unit can acquire weather information in real time. The data collection unit can acquire temperature in real time. The data collection unit can acquire air quality in real time. By referencing local biodiversity and building information and acquiring environmental data in real time, more accurate data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can input local biodiversity and building information into AI and have the AI ​​perform the analysis of the information.

[0078] The analysis unit analyzes the collected data and includes a selection unit that allows the user to select their preferred art style and colors. The selection unit can select art styles and colors such as abstract painting, realistic painting, warm colors, and cool colors. The selection unit allows the user to select abstract painting. The selection unit allows the user to select realistic painting. The selection unit allows the user to select warm colors. The selection unit allows the user to select cool colors. This allows the user to create more personalized digital art by selecting their preferred art style and colors. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input the art style and colors selected by the user into the AI ​​and have the AI ​​perform an analysis of the art style and colors.

[0079] The generation unit generates unique digital art based on the analysis results. The generation unit can generate unique digital art using methods such as a random generation algorithm or user-specific data. The generation unit can generate digital art using a random generation algorithm. The generation unit can generate digital art using user-specific data. The generation unit generates digital art using a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the generation unit to provide original artwork by generating unique digital art based on the analysis results. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input the analysis results into a generation AI and have the generation AI perform the generation of digital art.

[0080] The storage, sharing, and sales departments store, share, and sell the generated digital art as NFTs. The storage department can store digital art using methods such as cloud storage, local storage, and blockchain. The sharing department can share digital art using methods such as social media, email, and dedicated apps. The sales department can sell digital art using methods such as online marketplaces, auctions, and direct sales. The storage department can store digital art using cloud storage. The storage department can store digital art using local storage. The storage department can store digital art using blockchain. The sharing department can share digital art using social media. The sharing department can share digital art using email. The sharing department can share digital art using a dedicated app. The sales department can sell digital art using online marketplaces. The sales department can sell digital art using auctions. The sales department can sell digital art using direct sales. This increases the value of digital art by storing, sharing, and selling the generated digital art as NFTs. Some or all of the above-mentioned processes in the storage, sharing, and sales departments may be performed using AI or not. For example, the storage department can input digital art into the AI ​​and have the AI ​​select the storage method.

[0081] The acquisition unit estimates the user's emotions and adjusts the timing of location information acquisition based on the estimated user emotions. The acquisition unit can estimate the user's emotions using methods such as facial recognition, voice analysis, and text analysis. If the user is excited, the acquisition unit can acquire location information frequently and update it in real time. If the user is relaxed, the acquisition unit can reduce the frequency of location information acquisition to conserve battery power. If the user is in a hurry, the acquisition unit can quickly acquire location information and immediately send it for analysis. This allows for more appropriate timing of location information acquisition by adjusting the timing of location information acquisition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The acquisition unit analyzes the user's past travel history and selects the optimal method for acquiring location information. The acquisition unit can adjust the frequency of location information acquisition based on data of places the user has visited in the past. The acquisition unit can analyze the user's past travel patterns and determine the optimal timing for acquiring location information. The acquisition unit can improve the accuracy of location information acquisition by referring to environmental data of places the user has visited in the past. This allows the acquisition unit to select the optimal method for acquiring location information by analyzing the user's past travel history. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's past travel data into AI and have the AI ​​select the optimal method for acquiring location information.

[0083] The acquisition unit filters location information based on the user's current activities and areas of interest. For example, if the user is sightseeing, the acquisition unit can prioritize acquiring location information around tourist spots. If the user is observing nature, the acquisition unit can prioritize acquiring location information for nature reserves and parks. If the user is interested in cultural properties, the acquisition unit can prioritize acquiring location information for historical buildings and museums. By filtering location information based on the user's current activities and areas of interest, highly relevant information can be obtained. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input data on the user's activities and areas of interest into an AI and have the AI ​​perform the filtering.

[0084] The acquisition unit estimates the user's emotions and determines the priority of location information to acquire based on the estimated emotions. For example, if the user is excited, the acquisition unit can prioritize acquiring location information for tourist attractions and event venues. If the user is relaxed, the acquisition unit can prioritize acquiring location information for parks and nature reserves. If the user is in a hurry, the acquisition unit can prioritize acquiring location information for public transportation and major roads. In this way, by prioritizing location information based on the user's emotions, more important information can be acquired preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The acquisition unit prioritizes acquiring highly relevant information by considering the user's geographical location when acquiring location information. For example, if the user is in an urban area, the acquisition unit can prioritize acquiring location information for tourist attractions and restaurants. If the user is in a suburban area, the acquisition unit can prioritize acquiring location information for nature reserves and parks. If the user is in a historical site, the acquisition unit can prioritize acquiring location information for cultural properties and museums. In this way, highly relevant information can be prioritized by considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, or it may be performed without using AI. For example, the acquisition unit can input the user's geographical location information into AI and have the AI ​​select highly relevant information.

[0086] The acquisition unit analyzes the user's social media activity when acquiring location information and obtains relevant information. For example, the acquisition unit can prioritize acquiring location information of places shared by the user on social media. The acquisition unit can analyze the content of posts from accounts that the user follows and obtain relevant location information. The acquisition unit can acquire location information based on event information that the user plans to attend. In this way, relevant information can be obtained by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's social media activity data into AI and have the AI ​​select relevant information.

[0087] The data collection unit estimates the user's emotions and adjusts the method of collecting environmental data based on the estimated user emotions. For example, if the user is excited, the data collection unit can collect detailed environmental data more frequently. If the user is relaxed, the data collection unit can reduce the frequency of environmental data collection to conserve battery power. If the user is in a hurry, the data collection unit can quickly collect only the important environmental data. This allows for the collection of more appropriate data by adjusting the method of collecting environmental data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The data collection unit applies the optimal collection algorithm by referring to past data when collecting environmental data. For example, the data collection unit can determine the optimal collection timing based on past weather data. The data collection unit can adjust the collection frequency by referring to past air quality data. The data collection unit can predict the frequency of appearance of a specific organism based on past biological observation data and collect that data. This allows the optimal collection algorithm to be applied by referring to past data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past data into AI and have the AI ​​select the optimal collection algorithm.

[0089] The data collection unit focuses on specific organisms or buildings when collecting environmental data. For example, if a user is interested in a particular organism, the data collection unit can prioritize collecting environmental data related to that organism. If a user is interested in a particular building, the data collection unit can prioritize collecting environmental data related to that building. If a user is interested in a particular landscape, the data collection unit can prioritize collecting environmental data related to that landscape. This allows for the collection of highly relevant data by focusing on specific organisms or buildings. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data about specific organisms or buildings into an AI and have the AI ​​select the collection method.

[0090] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit can prioritize collecting detailed environmental data. If the user is relaxed, the data collection unit can prioritize collecting only important environmental data. If the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. This allows for the priority collection of more important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location when collecting environmental data. For example, if the user is in an urban area, the data collection unit can prioritize the collection of air quality and noise level data. If the user is in a nature reserve, the data collection unit can prioritize the collection of biodiversity and temperature data. If the user is in a historical site, the data collection unit can prioritize the collection of cultural property and architectural data. In this way, by considering the user's geographical location, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​select highly relevant data.

[0092] The data collection unit analyzes the user's social media activity and collects relevant data when collecting environmental data. For example, the data collection unit can prioritize collecting environmental data on locations shared by the user on social media. The data collection unit can analyze the content of posts from accounts that the user follows and collect relevant environmental data. The data collection unit can collect environmental data based on information about events the user plans to attend. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into AI and have the AI ​​select relevant data.

[0093] The analysis unit estimates the user's emotions and adjusts the data analysis method based on the estimated user emotions. For example, if the user is excited, the analysis unit can perform a detailed data analysis and provide visually stimulating results. If the user is relaxed, the analysis unit can perform a concise and easy-to-understand data analysis. If the user is in a hurry, the analysis unit can provide analysis results quickly. By adjusting the data analysis method based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The analysis unit applies the optimal analysis algorithm by referring to past analysis results during data analysis. For example, the analysis unit can select the optimal analysis algorithm based on past analysis results. The analysis unit can improve analysis accuracy by referring to past analysis results. The analysis unit can shorten analysis time based on past analysis results. This allows the optimal analysis algorithm to be applied by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past analysis results into AI and have the AI ​​select the optimal analysis algorithm.

[0095] The analysis unit performs analysis that focuses on specific art styles or colors during data analysis. For example, the analysis unit can perform data analysis based on an art style selected by the user. The analysis unit can perform data analysis based on a color selected by the user. The analysis unit can perform data analysis based on a theme selected by the user. This allows for analysis results tailored to the user's preferences by focusing on specific art styles or colors. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the art style or color selected by the user into an AI and have the AI ​​perform the analysis.

[0096] The analysis unit estimates the user's emotions and determines the priority of data to analyze based on the estimated emotions. For example, if the user is excited, the analysis unit can prioritize the analysis of detailed data. If the user is relaxed, the analysis unit can analyze only important data. If the user is in a hurry, the analysis unit can prioritize the analysis of data that can be analyzed quickly. This allows for the prioritization of more important data by determining the data priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The analysis unit prioritizes the analysis of highly relevant data, taking into account the user's geographical location during data analysis. For example, if the user is in an urban area, the analysis unit can prioritize the analysis of air quality and noise level data. If the user is in a nature reserve, the analysis unit can prioritize the analysis of biodiversity and temperature data. If the user is in a historical site, the analysis unit can prioritize the analysis of cultural property and architectural data. In this way, by considering the user's geographical location, the analysis unit can prioritize the analysis of highly relevant data. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the user's geographical location information into AI and have the AI ​​select highly relevant data.

[0098] The analysis unit analyzes the user's social media activity and analyzes relevant data during data analysis. For example, the analysis unit can prioritize the analysis of data on locations shared by the user on social media. The analysis unit can analyze the content of posts from accounts that the user follows and analyze relevant data. The analysis unit can analyze data based on information about events the user plans to attend. In this way, relevant data can be analyzed by analyzing the user's social media activity. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's social media activity data into AI and have the AI ​​select relevant data.

[0099] The generation unit estimates the user's emotions and adjusts the method of generating digital art based on the estimated user emotions. For example, if the user is excited, the generation unit can generate digital art with visually stimulating effects. If the user is relaxed, the generation unit can generate digital art with calming colors. If the user is in a hurry, the generation unit can generate digital art that is simple and can be generated quickly. By adjusting the method of generating digital art based on the user's emotions, more appropriate art can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0100] The generation unit applies the optimal generation algorithm by referring to past generation results when generating digital art. For example, the generation unit can select the optimal generation algorithm based on past generation results. The generation unit can improve generation accuracy by referring to past generation results. The generation unit can shorten generation time based on past generation results. This allows the optimal generation algorithm to be applied by referring to past generation results. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input past generation results into a generation AI and have the generation AI select the optimal generation algorithm.

[0101] The generation unit focuses on specific art styles and colors when generating digital art. For example, the generation unit can generate digital art based on an art style selected by the user. The generation unit can generate digital art based on a color selected by the user. The generation unit can generate digital art based on a theme selected by the user. This allows for the generation of digital art tailored to the user's preferences by focusing on specific art styles and colors. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input data on the art style and color selected by the user into a generation AI and have the generation AI perform the generation.

[0102] The generation unit estimates the user's emotions and determines the priority of the digital art to be generated based on the estimated user emotions. For example, if the user is excited, the generation unit can prioritize generating visually stimulating digital art. If the user is relaxed, the generation unit can prioritize generating digital art with calming tones. If the user is in a hurry, the generation unit can prioritize generating digital art that is simple and can be generated quickly. In this way, by prioritizing digital art based on the user's emotions, more important art can be generated preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0103] The generation unit prioritizes generating highly relevant art by considering the user's geographical location information when generating digital art. For example, if the user is in an urban area, the generation unit can generate digital art themed on urban landscapes. If the user is in a nature reserve, the generation unit can generate digital art themed on natural landscapes. If the user is in a historical site, the generation unit can generate digital art themed on cultural properties or historical buildings. In this way, by considering the user's geographical location information, the generation unit can prioritize generating highly relevant art. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI perform the generation of highly relevant art.

[0104] The generation unit analyzes the user's social media activity when generating digital art and generates relevant art. For example, the generation unit can generate digital art themed around landscapes of places shared by the user on social media. The generation unit can analyze the content of posts from accounts the user follows and generate relevant digital art. The generation unit can generate digital art based on information about events the user plans to attend. In this way, relevant art can be generated by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of relevant art.

[0105] The storage, sharing, and sales units estimate the user's emotions and adjust the NFT storage method based on the estimated user emotions. For example, if the user is excited, the storage, sharing, and sales units can provide a way to quickly save the NFT. If the user is relaxed, the storage, sharing, and sales units can provide detailed saving options. If the user is in a hurry, the storage, sharing, and sales units can provide a simple saving procedure. This allows for a more appropriate saving method by adjusting the NFT storage method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage, sharing, and sales units may be performed using AI or not. For example, the storage, sharing, and sales units can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The storage, sharing, and sales units apply the optimal storage algorithm when saving NFTs by referring to past saved data. For example, the storage, sharing, and sales units can select the optimal storage algorithm based on past saved data. The storage, sharing, and sales units can improve storage accuracy by referring to past saved data. The storage, sharing, and sales units can shorten storage time based on past saved data. This allows the optimal storage algorithm to be applied by referring to past saved data. Some or all of the above processes in the storage, sharing, and sales units may be performed using AI or not. For example, the storage, sharing, and sales units can input past saved data into AI and have the AI ​​select the optimal storage algorithm.

[0107] The storage, sharing, and sales units focus on specific blockchain technologies when storing NFTs. For example, the storage, sharing, and sales units can store NFTs based on a blockchain technology selected by the user. The storage, sharing, and sales units can provide the optimal storage method considering the characteristics of the blockchain technology selected by the user. The storage, sharing, and sales units can adjust the storage method considering the security characteristics of the blockchain technology selected by the user. This allows for the provision of the optimal storage method by focusing on a specific blockchain technology. Some or all of the above processes in the storage, sharing, and sales units may be performed using AI or not. For example, the storage, sharing, and sales units can input data on the blockchain technology selected by the user into an AI and have the AI ​​select the storage method.

[0108] The storage, sharing, and sales units estimate the user's emotions and determine the priority of NFTs to save based on the estimated emotions. For example, if the user is excited, the storage, sharing, and sales units can prioritize saving visually stimulating NFTs. If the user is relaxed, the storage, sharing, and sales units can prioritize saving NFTs with calming colors. If the user is in a hurry, the storage, sharing, and sales units can prioritize saving NFTs that are simple and quick to save. This allows for prioritizing the saving of more important NFTs based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage, sharing, and sales units may be performed using AI or not. For example, the storage, sharing, and sales departments can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0109] The storage, sharing, and sales departments prioritize saving highly relevant NFTs when saving NFTs, taking into account the user's geographical location information. For example, if the user is in an urban area, the storage, sharing, and sales departments can save NFTs themed around urban landscapes. If the user is in a nature reserve, the storage, sharing, and sales departments can save NFTs themed around natural landscapes. If the user is in a historical site, the storage, sharing, and sales departments can save NFTs themed around cultural properties or historical buildings. This allows for the priority saving of highly relevant NFTs by considering the user's geographical location information. Some or all of the above processing in the storage, sharing, and sales departments may be performed using AI or not. For example, the storage, sharing, and sales departments can input the user's geographical location information into the AI ​​and have the AI ​​select highly relevant NFTs.

[0110] The storage, sharing, and sales units analyze the user's social media activity when saving NFTs and save relevant NFTs. For example, the storage, sharing, and sales units can save NFTs themed around landscapes of places shared by the user on social media. The storage, sharing, and sales units can analyze the content of posts from accounts the user follows and save relevant NFTs. The storage, sharing, and sales units can save NFTs based on information about events the user plans to attend. In this way, relevant NFTs can be saved by analyzing the user's social media activity. Some or all of the above processes in the storage, sharing, and sales units may be performed using AI or not. For example, the storage, sharing, and sales units can input the user's social media activity data into AI and have the AI ​​select relevant NFTs.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The acquisition unit estimates the user's emotions and adjusts the timing of location information acquisition based on the estimated user emotions. The acquisition unit can estimate the user's emotions using methods such as facial recognition, voice analysis, and text analysis. If the user is excited, the acquisition unit can acquire location information frequently and update it in real time. If the user is relaxed, the acquisition unit can reduce the frequency of location information acquisition to conserve battery power. If the user is in a hurry, the acquisition unit can quickly acquire location information and immediately send it for analysis. This allows for more appropriate timing of location information acquisition by adjusting the timing of location information acquisition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0113] The acquisition unit analyzes the user's past travel history and selects the optimal method for acquiring location information. The acquisition unit can adjust the frequency of location information acquisition based on data of places the user has visited in the past. The acquisition unit can analyze the user's past travel patterns and determine the optimal timing for acquiring location information. The acquisition unit can improve the accuracy of location information acquisition by referring to environmental data of places the user has visited in the past. This allows the acquisition unit to select the optimal method for acquiring location information by analyzing the user's past travel history. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's past travel data into AI and have the AI ​​select the optimal method for acquiring location information.

[0114] The acquisition unit filters location information based on the user's current activities and areas of interest. For example, if the user is sightseeing, the acquisition unit can prioritize acquiring location information around tourist spots. If the user is observing nature, the acquisition unit can prioritize acquiring location information for nature reserves and parks. If the user is interested in cultural properties, the acquisition unit can prioritize acquiring location information for historical buildings and museums. By filtering location information based on the user's current activities and areas of interest, highly relevant information can be obtained. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input data on the user's activities and areas of interest into an AI and have the AI ​​perform the filtering.

[0115] The acquisition unit estimates the user's emotions and determines the priority of location information to acquire based on the estimated emotions. For example, if the user is excited, the acquisition unit can prioritize acquiring location information for tourist attractions and event venues. If the user is relaxed, the acquisition unit can prioritize acquiring location information for parks and nature reserves. If the user is in a hurry, the acquisition unit can prioritize acquiring location information for public transportation and major roads. In this way, by prioritizing location information based on the user's emotions, more important information can be acquired preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0116] The acquisition unit prioritizes acquiring highly relevant information by considering the user's geographical location when acquiring location information. For example, if the user is in an urban area, the acquisition unit can prioritize acquiring location information for tourist attractions and restaurants. If the user is in a suburban area, the acquisition unit can prioritize acquiring location information for nature reserves and parks. If the user is in a historical site, the acquisition unit can prioritize acquiring location information for cultural properties and museums. In this way, highly relevant information can be prioritized by considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, or it may be performed without using AI. For example, the acquisition unit can input the user's geographical location information into AI and have the AI ​​select highly relevant information.

[0117] The data collection unit estimates the user's emotions and adjusts the method of collecting environmental data based on the estimated user emotions. For example, if the user is excited, the data collection unit can collect detailed environmental data more frequently. If the user is relaxed, the data collection unit can reduce the frequency of environmental data collection to conserve battery power. If the user is in a hurry, the data collection unit can quickly collect only the important environmental data. This allows for the collection of more appropriate data by adjusting the method of collecting environmental data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0118] The data collection unit applies the optimal collection algorithm by referring to past data when collecting environmental data. For example, the data collection unit can determine the optimal collection timing based on past weather data. The data collection unit can adjust the collection frequency by referring to past air quality data. The data collection unit can predict the frequency of appearance of a specific organism based on past biological observation data and collect that data. This allows the optimal collection algorithm to be applied by referring to past data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past data into AI and have the AI ​​select the optimal collection algorithm.

[0119] The data collection unit focuses on specific organisms or buildings when collecting environmental data. For example, if a user is interested in a particular organism, the data collection unit can prioritize collecting environmental data related to that organism. If a user is interested in a particular building, the data collection unit can prioritize collecting environmental data related to that building. If a user is interested in a particular landscape, the data collection unit can prioritize collecting environmental data related to that landscape. This allows for the collection of highly relevant data by focusing on specific organisms or buildings. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data about specific organisms or buildings into an AI and have the AI ​​select the collection method.

[0120] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit can prioritize collecting detailed environmental data. If the user is relaxed, the data collection unit can prioritize collecting only important environmental data. If the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. This allows for the priority collection of more important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0121] The data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location when collecting environmental data. For example, if the user is in an urban area, the data collection unit can prioritize the collection of air quality and noise level data. If the user is in a nature reserve, the data collection unit can prioritize the collection of biodiversity and temperature data. If the user is in a historical site, the data collection unit can prioritize the collection of cultural property and architectural data. In this way, by considering the user's geographical location, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​select highly relevant data.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The acquisition unit acquires the user's location information. The acquisition unit can acquire location information using, for example, GPS data, Wi-Fi location information, cell tower data, etc. Step 2: The collection unit collects environmental data based on the location information acquired by the acquisition unit. The collection unit can collect environmental data such as weather information, temperature, air quality, and noise levels. The collection unit collects data using an API. Step 3: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using methods such as data mining, machine learning algorithms, and statistical analysis. Step 4: The generation unit generates digital art based on the data analyzed by the analysis unit. The generation unit can generate digital art in formats such as images, videos, and 3D models. The generation unit generates digital art using a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 5: The storage, sharing, and sales units store, share, and sell the digital art generated by the generation unit as NFTs. The storage unit can store the digital art using methods such as cloud storage, local storage, and blockchain. The sharing unit can share the digital art using methods such as social media, email, and dedicated apps. The sales unit can sell the digital art using methods such as online marketplaces, auctions, and direct sales.

[0124] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the acquisition unit, collection unit, analysis unit, generation unit, storage unit, sharing unit, and sales unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires location information using GPS data or Wi-Fi location information from the smart device 14. The collection unit collects environmental data via the communication I / F 44 of the smart device 14. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The generation unit generates digital art using the specific processing unit 290 of the data processing unit 12. The storage unit, sharing unit, and sales unit store, share, and sell the digital art generated by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 as NFTs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the acquisition unit, collection unit, analysis unit, generation unit, storage unit, sharing unit, and sales unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires location information using GPS data or Wi-Fi location information from the smart glasses 214. The collection unit collects environmental data via the communication I / F 44 of the smart glasses 214. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The generation unit generates digital art using the specific processing unit 290 of the data processing unit 12. The storage unit, sharing unit, and sales unit store, share, and sell the digital art generated by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12 as NFTs. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the acquisition unit, collection unit, analysis unit, generation unit, storage unit, sharing unit, and sales unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires location information using GPS data or Wi-Fi location information from the headset terminal 314. The collection unit collects environmental data via the communication I / F 44 of the headset terminal 314. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The generation unit generates digital art using the specific processing unit 290 of the data processing unit 12. The storage unit, sharing unit, and sales unit store, share, and sell the digital art generated by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12 as NFTs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the 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.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the acquisition unit, collection unit, analysis unit, generation unit, storage unit, sharing unit, and sales unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires location information using GPS data or Wi-Fi location information from the robot 414. The collection unit collects environmental data via the communication I / F 44 of the robot 414. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12. The generation unit generates digital art using the specific processing unit 290 of the data processing unit 12. The storage unit, sharing unit, and sales unit store, share, and sell the digital art generated by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12 as NFTs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0177] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) A unit that acquires the user's location information, A collection unit collects environmental data based on location information acquired by the acquisition unit, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates digital art based on the data analyzed by the analysis unit, The system includes a storage unit, a sharing unit, and a sales unit for storing, sharing, and selling the digital art generated by the generation unit as NFTs. A system characterized by the following features. (Note 2) The acquisition unit is, It features a reception area where users can input photos they have taken and their impressions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is It references local biodiversity and building information, and acquires environmental data such as weather, temperature, and air quality in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, It analyzes the collected data and includes a selection section where the user can choose their preferred art style and color scheme. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Based on the analysis results, we create a one-of-a-kind digital art piece. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned storage section, shared section, and sales section are, Save, share, and sell the generated digital art as NFTs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of location data acquisition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, The system analyzes the user's past travel history and selects the optimal method for obtaining location information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring location information, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The system estimates the user's emotions and determines the priority of location information to acquire based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring location information, the system prioritizes acquiring highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring location information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is We estimate user sentiment and adjust the method of collecting environmental data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting environmental data, the optimal collection algorithm is applied by referring to past data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When collecting environmental data, focus the collection on specific organisms or buildings. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When collecting environmental data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is When collecting environmental data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During data analysis, the optimal analysis algorithm is applied by referring to past analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, When analyzing data, we perform analyses that focus on specific art styles or color palettes. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and determines the priority of data to analyze based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During data analysis, the system prioritizes analyzing highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During data analysis, we analyze users' social media activity and analyze related data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is It estimates the user's emotions and adjusts the way digital art is generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating digital art, the optimal generation algorithm is applied by referring to past generation results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When generating digital art, focus on specific art styles or color palettes. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is It estimates the user's emotions and determines the priority of the digital art to be generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating digital art, the system prioritizes generating highly relevant art by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is When generating digital art, the system analyzes the user's social media activity and generates relevant art. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned storage section, shared section, and sales section are, It estimates the user's emotions and adjusts how NFTs are stored based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned storage section, shared section, and sales section are, When saving NFTs, the system applies the optimal saving algorithm by referring to previously saved data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned storage section, shared section, and sales section are, When storing NFTs, focus on storing them using specific blockchain technologies. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned storage section, shared section, and sales section are, It estimates the user's emotions and determines the priority of NFTs to save based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned storage section, shared section, and sales section are, When saving NFTs, the system prioritizes saving NFTs that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned storage section, shared section, and sales section are, When saving NFTs, the system analyzes the user's social media activity and saves relevant NFTs. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A unit that acquires the user's location information, A collection unit collects environmental data based on location information acquired by the acquisition unit, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates digital art based on the data analyzed by the analysis unit, The system includes a storage unit, a sharing unit, and a sales unit for storing, sharing, and selling the digital art generated by the generation unit as NFTs. A system characterized by the following features.

2. The acquisition unit is, It features a reception area where users can input photos they have taken and their impressions. The system according to feature 1.

3. The aforementioned collection unit is It references local biodiversity and building information, and acquires environmental data such as weather, temperature, and air quality in real time. The system according to feature 1.

4. The aforementioned analysis unit, It analyzes the collected data and includes a selection section where the user can choose their preferred art style and color scheme. The system according to feature 1.

5. The generating unit is Based on the analysis results, we create a one-of-a-kind digital art piece. The system according to feature 1.

6. The aforementioned storage section, shared section, and sales section are, Save, share, and sell the generated digital art as NFTs. The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of location data acquisition based on those emotions. The system according to feature 1.

8. The acquisition unit is, The system analyzes the user's past travel history and selects the optimal method for obtaining location information. The system according to feature 1.

9. The acquisition unit is, When acquiring location information, filtering is performed based on the user's current activities and areas of interest. The system according to feature 1.

10. The acquisition unit is, The system estimates the user's emotions and determines the priority of location information to acquire based on the estimated emotions. The system according to feature 1.

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