system

Abstract

The system according to this embodiment aims to visually reproduce the content of dreams and to share and trade them. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a sharing unit, and a buying / selling unit. The reception unit records the content of dreams. The generation unit analyzes the content of dreams recorded by the reception unit and generates images. The sharing unit shares the images generated by the generation unit. The buying / selling unit buys and sells the images generated by the generation unit.

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 directive sentences 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 conventional technology, there is a problem that it is difficult to visually reproduce, share, and sell the content of a dream.

[0005] The system according to the embodiment aims to visually reproduce, share, and sell the content of a dream.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, a sharing unit, and a trading unit. The reception unit records the content of a dream. The generation unit analyzes the content of the dream recorded by the reception unit and generates an image. The sharing unit shares the image generated by the generation unit. The trading unit trades the image generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can visually reproduce the contents of dreams and allow them to be shared or bought and sold. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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) A dream tracking system according to an embodiment of the present invention is a system for recording, analyzing, sharing, and trading the content of dreams. This dream tracking system records the content of dreams, an AI analyzes that content, and generates images. The generated images can be searched by the user and shared with other users. It is also possible to buy and sell the generated images, and popular dream scenes can be traded as NFTs. This allows users not only to visually enjoy their dreams but also to trade them with other users. For example, a user records the content of their dream. In this case, the user can easily record the content of their dream within the app. Next, the recorded dream content is analyzed by an AI and images are generated. For example, by utilizing the generation AI, dream scenes can be recreated with images generated by the AI. This allows users to visually enjoy their dreams. The generated images can be searched by the user. For example, past dreams can be searched by date, theme, and characters. This makes it easy to search for dreams seen on a specific date, dreams related to a specific theme, or dreams featuring specific characters. The generated images can be shared with other users. For example, dreams can be shared with friends or specific groups. This allows users to share the content of their dreams with others and exchange empathy and opinions. Furthermore, it is possible to form a dream interpretation community. Users can receive interpretations and advice about their dreams from other users, leading to a deeper understanding of their meaning. Generated images can be bought and sold. For example, AI-generated dream artwork can be bought and sold using electronic payments. This allows users not only to enjoy their dreams as works of art but also to trade them with other users. It is also possible to trade popular dream scenes as NFTs. This increases the value of dreams and provides new ways to enjoy them. In this way, the dream tracking system allows users to not only visually enjoy their dreams but also share and trade them with other users.

[0029] The dream tracking system according to this embodiment comprises a reception unit, a generation unit, a sharing unit, and a buying / selling unit. The reception unit records the content of dreams by the user. When the user records the content of dreams, it can be recorded in formats such as text, audio, or video. The reception unit records the content of dreams by, for example, the user entering text within the app. The reception unit can also record the content of dreams by voice. For example, the user records audio using a microphone, and the reception unit analyzes the audio data. Furthermore, the reception unit can also record the content of dreams by video. For example, the user takes video using a camera, and the reception unit analyzes the video data. The generation unit analyzes the content of dreams recorded by the reception unit and generates images. The generation unit analyzes the content of dreams using, for example, a generation AI, and generates images. The generation unit can, for example, have the generation AI receive text data as input and generate images based on that text data. The generation unit can also have the generation AI receive audio data as input and generate images based on that audio data. Furthermore, the generation unit can have the generation AI receive video data as input and generate images based on that video data. The generation unit, for example, receives a prompt such as "Please represent the content of this dream as an image," analyzes the dream content, and generates an image. The sharing unit shares the images generated by the generation unit. The sharing unit allows users to share the content of their dreams with other users. The sharing unit allows users to share the content of their dreams with friends or specific groups. The sharing unit can also form dream interpretation communities. The sharing unit allows users to receive interpretations and advice about their dreams from other users. The trading unit buys and sells the images generated by the generation unit. The trading unit allows users to buy and sell generated images using electronic payment. The trading unit allows users to buy and sell generated images as works of art. The trading unit can also trade generated images as NFTs. The trading unit allows users to trade popular dream scenes as NFTs.As a result, the dream tracking system according to this embodiment not only allows users to visually enjoy their dreams, but also to share and trade them with other users.

[0030] The reception system allows users to record the content of their dreams. Users can record their dreams in various formats, such as text, audio, and video. Specifically, when a user records a dream by typing text within the app, the app automatically saves the entered text for later analysis. The reception system also allows users to record dreams using audio. For example, a user can record audio using a microphone, and the reception system analyzes the audio data. The audio data is converted to text using speech recognition technology and saved as the dream content. Furthermore, the reception system allows users to record dreams using video. For example, a user can film video using a camera, and the reception system analyzes the video data. The video data is analyzed using video analysis technology to extract important scenes and objects, and saved as the dream content. This allows the reception system to record dreams in diverse formats and save them in detail. Additionally, the reception system makes the interface for recording dreams intuitive and easy to use, making it simple for users to record their dreams. For example, with audio input, recording starts simply by pressing a button, and is automatically saved when recording ends. Furthermore, in the case of video input, the user activates the camera and starts recording, and the video data is automatically saved when recording is finished. This allows the reception desk to easily and quickly record the content of dreams, improving the convenience of the dream tracking system.

[0031] The generation unit analyzes the dream content recorded by the reception unit and generates an image. For example, the generation unit uses a generation AI to analyze the dream content and generate an image. Specifically, the generation AI receives text data as input and generates an image based on that text data. The generation AI analyzes the text data using natural language processing technology to understand the dream content. For example, if the text data "flying under a blue sky" is input, the generation AI extracts the keywords "blue sky" and "flying" and generates an image based on them. The generation unit can also have the generation AI receive audio data as input and generate an image based on that audio data. The audio data is converted to text using speech recognition technology, and that text data is input to the generation AI. Furthermore, the generation unit can also have the generation AI receive video data as input and generate an image based on that video data. The video data is analyzed using video analysis technology to extract important scenes and objects, and an image is generated based on them. For example, the generation unit can have the generation AI receive a prompt such as "Please represent the content of this dream as an image," analyze the dream content, and generate an image. The generative AI uses deep learning technology to learn from large amounts of data and generate images with high accuracy. This allows the generator to visually represent the content of the user's dreams, enabling users to enjoy their dreams more concretely. Furthermore, the generator can regularly update the AI's training data and incorporate the latest technologies to improve the quality of the generated images. This ensures that the generator consistently produces high-quality images, increasing user satisfaction.

[0032] The sharing section shares images generated by the generation section. For example, the sharing section allows users to share the content of their dreams with other users. Specifically, users can share generated images with friends or specific groups. The sharing section provides an intuitive interface to make it easy for users to share generated images. For example, users can send images to friends or groups simply by selecting a generated image and pressing the share button. The sharing section can also form dream interpretation communities. For example, users can receive interpretations and advice about their dreams from other users. Dream interpretation communities function as a place where users can exchange opinions about dream content and help each other. The sharing section provides community search and participation functions to make it easy for users to join dream interpretation communities. For example, users can search for communities using themes or keywords of interest and apply to join. This allows the sharing section to enable users to share the content of their dreams with other users and deepen their understanding of dreams. Furthermore, the sharing section provides security features to safely manage shared images and dream content. For example, shared images and text data are protected using encryption technology to prevent unauthorized access. Furthermore, the sharing section provides a function to restrict who users can share with, thus protecting privacy. This allows the sharing section to provide an environment where users can confidently share the contents of their dreams, improving the reliability of the dream tracking system.

[0033] The trading unit buys and sells images generated by the generation unit. For example, users can buy and sell generated images using electronic payment. Specifically, users can buy and sell generated images as works of art. The trading unit provides an intuitive interface to make it easy for users to buy and sell generated images. For example, users can select a generated image, set a price, and add it to their sales list. The trading unit also allows generated images to be traded as NFTs. NFTs (Non-Fungible Tokens) use blockchain technology to prove ownership of digital assets, allowing users to trade generated images as unique digital art. The trading unit provides NFT creation functions and a trading platform to make it easy for users to create and trade NFTs. For example, a user can simply select a generated image and press the button to register it as an NFT, and the image will be registered as an NFT and listed on the trading platform. This allows the trading unit to enable users to buy and sell generated images as works of art or NFTs and earn revenue. Furthermore, the trading unit provides security features to ensure the safety of transactions. For example, transaction data is protected using encryption technology to prevent unauthorized access. Furthermore, to ensure transaction transparency, the trading department will record transaction history on the blockchain, making it accessible to anyone. This will allow the trading department to provide users with a secure trading environment and improve the reliability of the dream tracking system.

[0034] The generation unit can generate images using a generative AI. For example, the generation unit can analyze the content of a dream using a generative AI and generate an image. For example, the generation unit can have the generative AI receive text data as input and generate an image based on that text data. The generation unit can also have the generative AI receive audio data as input and generate an image based on that audio data. Furthermore, the generation unit can have the generative AI receive video data as input and generate an image based on that video data. For example, the generation unit can have the generative AI receive a prompt such as "Please represent the content of this dream as an image," analyze the dream content, and generate an image. This improves the accuracy of image generation by using a generative AI. The generative AI can be implemented using technologies such as GAN (Generative Opposite Network) or VAE (Variational Autoencoder). Some or all of the above-described processes in the generation unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the generation unit can generate an image using a generative AI model that receives the content of a dream as input and outputs an image.

[0035] The sharing section allows users to share the content of their dreams with other users. For example, users can share the content of their dreams with friends or specific groups. For example, users can share the content of their dreams in text format. The sharing section also allows users to share the content of their dreams in image format. Furthermore, the sharing section allows users to share the content of their dreams in audio format. For example, users can send the content of their dreams as a text message. The sharing section also allows users to send the content of their dreams as an image. Furthermore, the sharing section allows users to send the content of their dreams as an audio message. This allows users to share the content of their dreams with other users. Some or all of the above processing in the sharing section may be performed using AI, for example, or without AI. For example, the sharing section can perform the sharing process using an AI model that takes the content of a dream as input and outputs data for sharing.

[0036] The trading unit can trade the generated images as NFTs. The trading unit can, for example, trade the generated images as NFTs using blockchain technology. The trading unit can, for example, trade the generated images on a specific trading platform. The trading unit can, for example, trade the generated images using electronic payment. The trading unit can, for example, sell the generated images at a specific price. The trading unit can also sell the generated images in an auction format. Furthermore, the trading unit can also exchange the generated images. The trading unit can, for example, sell the generated images at a fixed price. The trading unit can also sell the generated images in an auction format. Furthermore, the trading unit can exchange the generated images with other users. This allows the generated images to be traded as NFTs. When trading as NFTs, it is necessary to clarify, for example, the type of blockchain and the trading platform. Some or all of the above processing in the trading unit may be performed using, for example, AI, or not using AI. For example, the trading unit can process transactions using an AI model that takes the generated images as input and outputs data for trading as NFTs.

[0037] The reception desk can analyze the user's past dream recording history and select the optimal recording method. For example, if the user has preferred using text input in the past, the reception desk will prioritize suggesting text input. For example, if the user has frequently used voice input in the past, the reception desk will recommend voice input. For example, if the user has frequently used images in the past, the reception desk will suggest image input. This allows the reception desk to select the optimal recording method based on the user's past recording history. The optimal recording method may be selected in formats such as text, audio, or video. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can select a recording method using an AI model that receives the user's past recording history data as input and outputs data for selecting the optimal recording method.

[0038] The reception unit can filter dreams based on the user's current lifestyle and areas of interest when recording them. For example, if the user is busy with work, the reception unit can suggest a simple recording method. If the user is interested in a hobby, the reception unit can prioritize recording dreams related to that hobby. If the user is traveling, the reception unit can prioritize recording dreams related to travel. This allows the dream records to be filtered based on the user's lifestyle and areas of interest. Lifestyle is filtered by specific details such as occupation, daily activities, and health status. Areas of interest are filtered by specific details such as hobbies and topics of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can perform filtering using an AI model that takes data on the user's lifestyle and areas of interest as input and outputs data for filtering dream records.

[0039] The reception unit can prioritize recording dreams that are highly relevant to the user's geographical location when recording dreams. For example, if the user is traveling, the reception unit will prioritize recording dreams related to the travel destination. For example, if the user is at home, the reception unit will prioritize recording dreams related to home. For example, if the user is at work, the reception unit will prioritize recording dreams related to work. This allows the reception unit to prioritize recording dreams that are highly relevant based on the user's geographical location. Geographical location information is obtained through specific methods such as GPS data or location services. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can determine the priority of recording using an AI model that receives the user's geographical location data as input and outputs data for prioritizing the recording of highly relevant dreams.

[0040] The reception unit can analyze the user's social media activity when recording dreams and record relevant dreams. For example, the reception unit can record dreams related to topics the user spends a lot of time on social media. For example, the reception unit can record dreams related to posts the user has recently "liked". For example, the reception unit can record dreams related to accounts the user follows. This allows the reception unit to record relevant dreams based on the user's social media activity. Social media activity is analyzed using specific analytical methods such as post content, the number of likes, and comments. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can process the recording using an AI model that takes the user's social media activity data as input and outputs data for recording relevant dreams.

[0041] The generation unit can adjust the level of detail of the generated image based on the importance of the dream. For example, the generation unit generates a detailed image for important dreams. For example, it generates a simple image for general dreams. For example, it generates an image by referencing past images for recurring dreams. This allows the level of detail to be adjusted based on the importance of the dream. The level of detail is adjusted using specific adjustment criteria such as resolution and detail level. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can adjust the level of detail using a generation AI model that takes dream importance data as input and outputs data for adjusting the level of detail of the generated image.

[0042] The generation unit can apply different generation algorithms depending on the dream category when generating images. For example, in the case of an adventure dream, the generation unit generates a vividly colored and dynamic image. For example, in the case of a horror dream, the generation unit generates a dark and tense image. For example, in the case of a happy dream, the generation unit generates a bright and calm image. This allows for the application of different generation algorithms depending on the dream category. The generation algorithms can be of specific types, such as GAN, VAE, and deep learning. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or not. For example, the generation unit can apply algorithms using a generative AI model that takes dream category data as input and outputs data for applying different generation algorithms.

[0043] The generation unit can determine the generation priority based on the dream recording date when generating images. For example, the generation unit may prioritize imaging recently recorded dreams. For example, the generation unit may prioritize imaging dreams related to specific events. For example, the generation unit may prioritize imaging dreams that the user frequently experiences. This allows the generation priority to be determined based on the dream recording date. The generation priority is determined by specific criteria such as recording date and importance. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can determine the priority using a generation AI model that takes dream recording date data as input and outputs data for determining the generation priority.

[0044] The generation unit can adjust the order of image generation based on the relevance of the dreams. For example, the generation unit may prioritize image generation of dreams that the user is particularly interested in. For example, the generation unit may prioritize image generation of dreams that the user wants to share with other users. For example, the generation unit may prioritize image generation of dreams that the user frequently experiences. This allows the order of generation to be adjusted based on the relevance of the dreams. The order of generation is adjusted using specific adjustment criteria such as relevance and importance. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can adjust the order using a generation AI model that takes dream relevance data as input and outputs data for adjusting the order of generation.

[0045] The sharing function can adjust the level of detail in sharing based on the content of the dream. For example, in the case of an important dream, the sharing function provides a sharing method that includes detailed information. For example, in the case of a general dream, the sharing function provides a sharing method that includes concise information. For example, in the case of a recurring dream, the sharing function provides information by referring to past sharing information. This allows the level of detail in sharing to be adjusted based on the content of the dream. The level of detail in sharing is adjusted by specific adjustment criteria such as the amount of information and the precision of the display. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can adjust the level of detail using an AI model that takes dream content data as input and outputs data for adjusting the level of detail in sharing.

[0046] The sharing section can apply different sharing algorithms depending on the dream category during sharing. For example, for adventure dreams, the sharing section provides a sharing method with vivid colors and movement. For example, for horror dreams, the sharing section provides a sharing method with dark tones and tension. For example, for happy dreams, the sharing section provides a sharing method with bright tones and calmness. This allows different sharing algorithms to be applied depending on the dream category. The sharing algorithm is applied in specific ways, such as data compression methods and sharing protocols. Some or all of the above processing in the sharing section may be performed using AI, for example, or not. For example, the sharing section can apply algorithms using an AI model that takes dream category data as input and outputs data for applying different sharing algorithms.

[0047] The sharing function can adjust the order of sharing based on when the dreams were recorded. For example, the sharing function may prioritize sharing recently recorded dreams. For example, the sharing function may prioritize sharing dreams related to specific events. For example, the sharing function may prioritize sharing dreams that the user frequently experiences. This allows the order of sharing to be adjusted based on when the dreams were recorded. The order of sharing is adjusted using specific adjustment criteria such as recording date and relevance. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can adjust the order using an AI model that takes dream recording date data as input and outputs data for adjusting the order of sharing.

[0048] The sharing function can adjust the order of sharing based on the relevance of the dreams. For example, the sharing function may prioritize sharing dreams that the user is particularly interested in. For example, the sharing function may prioritize sharing dreams that the user wants to share with other users. For example, the sharing function may prioritize sharing dreams that the user frequently experiences. This allows the order of sharing to be adjusted based on the relevance of the dreams. The order of sharing is adjusted using specific adjustment criteria such as relevance and importance. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can adjust the order using an AI model that takes dream relevance data as input and outputs data for adjusting the order of sharing.

[0049] The trading unit can adjust the level of detail in a trade based on the content of the dream. For example, in the case of an important dream, the trading unit provides a trading method that includes detailed information. For example, in the case of a general dream, the trading unit provides a trading method that includes concise information. For example, in the case of a recurring dream, the trading unit provides information based on past trading data. This allows the level of detail in a trade to be adjusted based on the content of the dream. The level of detail in a trade is adjusted using specific adjustment criteria, such as the amount of transaction information and the precision of the display. Some or all of the above processing in the trading unit may be performed using AI, for example, or without AI. For example, the trading unit can adjust the level of detail using an AI model that takes dream content data as input and outputs data for adjusting the level of detail in a trade.

[0050] The trading unit can apply different trading algorithms depending on the dream category at the time of trading. For example, in the case of an adventure dream, the trading unit provides a trading method with vivid colors and movement. For example, in the case of a horror dream, the trading unit provides a trading method with dark tones and tension. For example, in the case of a happiness dream, the trading unit provides a trading method with bright tones and calmness. This allows different trading algorithms to be applied depending on the dream category. The trading algorithm is applied in specific forms such as pricing algorithms and trading protocols. Some or all of the above processing in the trading unit may be performed using AI, for example, or not using AI. For example, the trading unit can apply algorithms using an AI model that takes dream category data as input and outputs data for applying different trading algorithms.

[0051] The trading unit can adjust the order of transactions based on when the dreams were recorded. For example, the trading unit may prioritize the sale of recently recorded dreams. For example, the trading unit may prioritize the sale of dreams related to specific events. For example, the trading unit may prioritize the sale of dreams that the user frequently experiences. This allows the order of transactions to be adjusted based on when the dreams were recorded. The order of transactions is adjusted using specific adjustment criteria such as recording date and relevance. Some or all of the above processing in the trading unit may be performed using AI, for example, or not. For example, the trading unit can adjust the order using an AI model that takes dream recording date data as input and outputs data for adjusting the order of transactions.

[0052] The trading unit can adjust the order of transactions based on the relevance of dreams. For example, the trading unit may prioritize the sale of dreams that the user is particularly interested in. For example, the trading unit may prioritize the sale of dreams that the user wants to share with other users. For example, the trading unit may prioritize the sale of dreams that the user frequently experiences. This allows the order of transactions to be adjusted based on the relevance of dreams. The order of transactions is adjusted using specific adjustment criteria such as relevance and importance. Some or all of the above processing in the trading unit may be performed using AI, for example, or not. For example, the trading unit can adjust the order using an AI model that takes dream relevance data as input and outputs data for adjusting the order of transactions.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The dream tracking system can also acquire user health data and analyze the relationship between dream content and health status. For example, it can acquire data such as the user's sleep patterns, heart rate, and stress levels, and correlate this data with dream content. This allows users to understand the relationship between their health status and dream content and use it to manage their health. For example, if a user is stressed, it can analyze how that stress is affecting their dreams. It can also analyze how quality sleep is affecting the content of their dreams. Furthermore, it can analyze the user's heart rate data and how heart rate fluctuations are affecting dream content. This allows users to gain a deeper understanding of the relationship between their health status and dream content.

[0055] The dream tracking system can further acquire user lifestyle data and analyze the relationship between dream content and lifestyle habits. For example, it can acquire data on the user's diet, exercise, and sleep, and correlate this data with dream content. This allows users to understand the relationship between their lifestyle and dream content and use this information to improve their lifestyle. For instance, if a user eats a particular meal, the system can analyze how that meal affects the content of their dreams. Similarly, if a user exercises, the system can analyze how that exercise affects the content of their dreams. Furthermore, it can analyze the user's sleep data and how sleep quality affects the content of their dreams. This allows users to gain a deeper understanding of the relationship between their lifestyle and dream content.

[0056] The dream tracking system can also acquire the user's geographical location and analyze the relationship between dream content and geographical location. For example, if the user is traveling, the system can analyze dream content related to their travel destination. This allows the user to understand the relationship between their geographical location and dream content and use this information to plan their trip. For example, if the user is staying in a specific location, the system can analyze dream content related to that location. Similarly, if the user is at home, the system can analyze dream content related to home. Furthermore, if the user is at work, the system can analyze dream content related to work. This allows the user to gain a deeper understanding of the relationship between their geographical location and dream content.

[0057] The dream tracking system can also acquire users' social media activity and analyze the relationship between dream content and social media activity. For example, it can analyze dream content related to topics that users spend a lot of time on social media. This allows users to understand the relationship between their social media activity and dream content and reconsider how they use social media. For example, it can analyze dream content related to posts that users have recently "liked." It can also analyze dream content related to accounts that users follow. Furthermore, it can analyze dream content related to topics that users spend a lot of time on. This allows users to gain a deeper understanding of the relationship between their social media activity and dream content.

[0058] The dream tracking system can further analyze the user's past dream recording history and analyze the relationship between dream content and past records. For example, it can analyze the content of dreams recorded by the user in the past and relate it to the content of current dreams. This allows the user to understand the relationship between their past and present dreams and find dream patterns. For example, it can analyze the content of dreams the user has repeatedly had in the past and relate it to the content of current dreams. It can also relate dreams related to a specific theme in the past to the content of current dreams. Furthermore, if the user has dreams featuring a specific character in the past, it can relate that character to the content of current dreams. This allows the user to gain a deeper understanding of the relationship between their past and present dreams.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk records the content of the dream. Users can record the content of their dreams in various formats, such as text, audio, and video. For example, users can record their dreams by typing text within the app, recording audio using the microphone, or taking video using the camera. Step 2: The generation unit analyzes the dream content recorded by the reception unit and generates an image. The generation unit uses a generation AI to analyze text data, audio data, and video data, and generates an image based on that analysis. For example, it receives a prompt such as "Please represent the content of this dream as an image," analyzes the dream content, and generates an image. Step 3: The sharing section shares the images generated by the generation section. Users can share the content of their dreams with other users, or with friends or specific groups. They can also form dream interpretation communities and receive interpretations and advice from other users. Step 4: The trading unit buys and sells the images generated by the generation unit. Users can buy and sell the generated images using electronic payment or as works of art. They can also trade the generated images as NFTs. For example, users can trade popular dream scenes as NFTs.

[0061] (Example of form 2) A dream tracking system according to an embodiment of the present invention is a system for recording, analyzing, sharing, and trading the content of dreams. This dream tracking system records the content of dreams, an AI analyzes that content, and generates images. The generated images can be searched by the user and shared with other users. It is also possible to buy and sell the generated images, and popular dream scenes can be traded as NFTs. This allows users not only to visually enjoy their dreams but also to trade them with other users. For example, a user records the content of their dream. In this case, the user can easily record the content of their dream within the app. Next, the recorded dream content is analyzed by an AI and images are generated. For example, by utilizing the generation AI, dream scenes can be recreated with images generated by the AI. This allows users to visually enjoy their dreams. The generated images can be searched by the user. For example, past dreams can be searched by date, theme, and characters. This makes it easy to search for dreams seen on a specific date, dreams related to a specific theme, or dreams featuring specific characters. The generated images can be shared with other users. For example, dreams can be shared with friends or specific groups. This allows users to share the content of their dreams with others and exchange empathy and opinions. Furthermore, it is possible to form a dream interpretation community. Users can receive interpretations and advice about their dreams from other users, leading to a deeper understanding of their meaning. Generated images can be bought and sold. For example, AI-generated dream artwork can be bought and sold using electronic payments. This allows users not only to enjoy their dreams as works of art but also to trade them with other users. It is also possible to trade popular dream scenes as NFTs. This increases the value of dreams and provides new ways to enjoy them. In this way, the dream tracking system allows users to not only visually enjoy their dreams but also share and trade them with other users.

[0062] The dream tracking system according to this embodiment comprises a reception unit, a generation unit, a sharing unit, and a buying / selling unit. The reception unit records the content of dreams by the user. When the user records the content of dreams, it can be recorded in formats such as text, audio, or video. The reception unit records the content of dreams by, for example, the user entering text within the app. The reception unit can also record the content of dreams by voice. For example, the user records audio using a microphone, and the reception unit analyzes the audio data. Furthermore, the reception unit can also record the content of dreams by video. For example, the user takes video using a camera, and the reception unit analyzes the video data. The generation unit analyzes the content of dreams recorded by the reception unit and generates images. The generation unit analyzes the content of dreams using, for example, a generation AI, and generates images. The generation unit can, for example, have the generation AI receive text data as input and generate images based on that text data. The generation unit can also have the generation AI receive audio data as input and generate images based on that audio data. Furthermore, the generation unit can have the generation AI receive video data as input and generate images based on that video data. The generation unit, for example, receives a prompt such as "Please represent the content of this dream as an image," analyzes the dream content, and generates an image. The sharing unit shares the images generated by the generation unit. The sharing unit allows users to share the content of their dreams with other users. The sharing unit allows users to share the content of their dreams with friends or specific groups. The sharing unit can also form dream interpretation communities. The sharing unit allows users to receive interpretations and advice about their dreams from other users. The trading unit buys and sells the images generated by the generation unit. The trading unit allows users to buy and sell generated images using electronic payment. The trading unit allows users to buy and sell generated images as works of art. The trading unit can also trade generated images as NFTs. The trading unit allows users to trade popular dream scenes as NFTs.As a result, the dream tracking system according to this embodiment not only allows users to visually enjoy their dreams, but also to share and trade them with other users.

[0063] The reception system allows users to record the content of their dreams. Users can record their dreams in various formats, such as text, audio, and video. Specifically, when a user records a dream by typing text within the app, the app automatically saves the entered text for later analysis. The reception system also allows users to record dreams using audio. For example, a user can record audio using a microphone, and the reception system analyzes the audio data. The audio data is converted to text using speech recognition technology and saved as the dream content. Furthermore, the reception system allows users to record dreams using video. For example, a user can film video using a camera, and the reception system analyzes the video data. The video data is analyzed using video analysis technology to extract important scenes and objects, and saved as the dream content. This allows the reception system to record dreams in diverse formats and save them in detail. Additionally, the reception system makes the interface for recording dreams intuitive and easy to use, making it simple for users to record their dreams. For example, with audio input, recording starts simply by pressing a button, and is automatically saved when recording ends. Furthermore, in the case of video input, the user activates the camera and starts recording, and the video data is automatically saved when recording is finished. This allows the reception desk to easily and quickly record the content of dreams, improving the convenience of the dream tracking system.

[0064] The generation unit analyzes the dream content recorded by the reception unit and generates an image. For example, the generation unit uses a generation AI to analyze the dream content and generate an image. Specifically, the generation AI receives text data as input and generates an image based on that text data. The generation AI analyzes the text data using natural language processing technology to understand the dream content. For example, if the text data "flying under a blue sky" is input, the generation AI extracts the keywords "blue sky" and "flying" and generates an image based on them. The generation unit can also have the generation AI receive audio data as input and generate an image based on that audio data. The audio data is converted to text using speech recognition technology, and that text data is input to the generation AI. Furthermore, the generation unit can also have the generation AI receive video data as input and generate an image based on that video data. The video data is analyzed using video analysis technology to extract important scenes and objects, and an image is generated based on them. For example, the generation unit can have the generation AI receive a prompt such as "Please represent the content of this dream as an image," analyze the dream content, and generate an image. The generative AI uses deep learning technology to learn from large amounts of data and generate images with high accuracy. This allows the generator to visually represent the content of the user's dreams, enabling users to enjoy their dreams more concretely. Furthermore, the generator can regularly update the AI's training data and incorporate the latest technologies to improve the quality of the generated images. This ensures that the generator consistently produces high-quality images, increasing user satisfaction.

[0065] The sharing section shares images generated by the generation section. For example, the sharing section allows users to share the content of their dreams with other users. Specifically, users can share generated images with friends or specific groups. The sharing section provides an intuitive interface to make it easy for users to share generated images. For example, users can send images to friends or groups simply by selecting a generated image and pressing the share button. The sharing section can also form dream interpretation communities. For example, users can receive interpretations and advice about their dreams from other users. Dream interpretation communities function as a place where users can exchange opinions about dream content and help each other. The sharing section provides community search and participation functions to make it easy for users to join dream interpretation communities. For example, users can search for communities using themes or keywords of interest and apply to join. This allows the sharing section to enable users to share the content of their dreams with other users and deepen their understanding of dreams. Furthermore, the sharing section provides security features to safely manage shared images and dream content. For example, shared images and text data are protected using encryption technology to prevent unauthorized access. Furthermore, the sharing section provides a function to restrict who users can share with, thus protecting privacy. This allows the sharing section to provide an environment where users can confidently share the contents of their dreams, improving the reliability of the dream tracking system.

[0066] The trading unit buys and sells images generated by the generation unit. For example, users can buy and sell generated images using electronic payment. Specifically, users can buy and sell generated images as works of art. The trading unit provides an intuitive interface to make it easy for users to buy and sell generated images. For example, users can select a generated image, set a price, and add it to their sales list. The trading unit also allows generated images to be traded as NFTs. NFTs (Non-Fungible Tokens) use blockchain technology to prove ownership of digital assets, allowing users to trade generated images as unique digital art. The trading unit provides NFT creation functions and a trading platform to make it easy for users to create and trade NFTs. For example, a user can simply select a generated image and press the button to register it as an NFT, and the image will be registered as an NFT and listed on the trading platform. This allows the trading unit to enable users to buy and sell generated images as works of art or NFTs and earn revenue. Furthermore, the trading unit provides security features to ensure the safety of transactions. For example, transaction data is protected using encryption technology to prevent unauthorized access. Furthermore, to ensure transaction transparency, the trading department will record transaction history on the blockchain, making it accessible to anyone. This will allow the trading department to provide users with a secure trading environment and improve the reliability of the dream tracking system.

[0067] The generation unit can generate images using a generative AI. For example, the generation unit can analyze the content of a dream using a generative AI and generate an image. For example, the generation unit can have the generative AI receive text data as input and generate an image based on that text data. The generation unit can also have the generative AI receive audio data as input and generate an image based on that audio data. Furthermore, the generation unit can have the generative AI receive video data as input and generate an image based on that video data. For example, the generation unit can have the generative AI receive a prompt such as "Please represent the content of this dream as an image," analyze the dream content, and generate an image. This improves the accuracy of image generation by using a generative AI. The generative AI can be implemented using technologies such as GAN (Generative Opposite Network) or VAE (Variational Autoencoder). Some or all of the above-described processes in the generation unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the generation unit can generate an image using a generative AI model that receives the content of a dream as input and outputs an image.

[0068] The sharing section allows users to share the content of their dreams with other users. For example, users can share the content of their dreams with friends or specific groups. For example, users can share the content of their dreams in text format. The sharing section also allows users to share the content of their dreams in image format. Furthermore, the sharing section allows users to share the content of their dreams in audio format. For example, users can send the content of their dreams as a text message. The sharing section also allows users to send the content of their dreams as an image. Furthermore, the sharing section allows users to send the content of their dreams as an audio message. This allows users to share the content of their dreams with other users. Some or all of the above processing in the sharing section may be performed using AI, for example, or without AI. For example, the sharing section can perform the sharing process using an AI model that takes the content of a dream as input and outputs data for sharing.

[0069] The trading unit can trade the generated images as NFTs. The trading unit can, for example, trade the generated images as NFTs using blockchain technology. The trading unit can, for example, trade the generated images on a specific trading platform. The trading unit can, for example, trade the generated images using electronic payment. The trading unit can, for example, sell the generated images at a specific price. The trading unit can also sell the generated images in an auction format. Furthermore, the trading unit can also exchange the generated images. The trading unit can, for example, sell the generated images at a fixed price. The trading unit can also sell the generated images in an auction format. Furthermore, the trading unit can exchange the generated images with other users. This allows the generated images to be traded as NFTs. When trading as NFTs, it is necessary to clarify, for example, the type of blockchain and the trading platform. Some or all of the above processing in the trading unit may be performed using, for example, AI, or not using AI. For example, the trading unit can process transactions using an AI model that takes the generated images as input and outputs data for trading as NFTs.

[0070] The reception unit can estimate the user's emotions and adjust the timing of dream recording based on the estimated emotions. For example, if the user is relaxed, the reception unit sends a notification prompting them to record their dreams. For example, if the user is stressed, the reception unit suggests postponing dream recording. For example, if the user is excited, the reception unit immediately sends a notification prompting them to record their dreams. This allows the timing of dream recording to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 reception unit may be performed using AI or not. For example, the reception unit can adjust the recording timing using an AI model that receives user emotion data as input and outputs data for adjusting the timing of dream recording.

[0071] The reception desk can analyze the user's past dream recording history and select the optimal recording method. For example, if the user has preferred using text input in the past, the reception desk will prioritize suggesting text input. For example, if the user has frequently used voice input in the past, the reception desk will recommend voice input. For example, if the user has frequently used images in the past, the reception desk will suggest image input. This allows the reception desk to select the optimal recording method based on the user's past recording history. The optimal recording method may be selected in formats such as text, audio, or video. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can select a recording method using an AI model that receives the user's past recording history data as input and outputs data for selecting the optimal recording method.

[0072] The reception unit can filter dreams based on the user's current lifestyle and areas of interest when recording them. For example, if the user is busy with work, the reception unit can suggest a simple recording method. If the user is interested in a hobby, the reception unit can prioritize recording dreams related to that hobby. If the user is traveling, the reception unit can prioritize recording dreams related to travel. This allows the dream records to be filtered based on the user's lifestyle and areas of interest. Lifestyle is filtered by specific details such as occupation, daily activities, and health status. Areas of interest are filtered by specific details such as hobbies and topics of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can perform filtering using an AI model that takes data on the user's lifestyle and areas of interest as input and outputs data for filtering dream records.

[0073] The reception unit can estimate the user's emotions and determine the priority of dreams to record based on the estimated emotions. For example, if the user is sad, the reception unit will prioritize recording positive dreams. If the user is excited, the reception unit will prioritize recording detailed dreams. If the user is tired, the reception unit will prioritize recording concise dreams. This allows the system to determine the priority of dreams to record according to 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can determine the priority using an AI model that takes user emotion data as input and outputs data for determining the priority of dreams to record.

[0074] The reception unit can prioritize recording dreams that are highly relevant to the user's geographical location when recording dreams. For example, if the user is traveling, the reception unit will prioritize recording dreams related to the travel destination. For example, if the user is at home, the reception unit will prioritize recording dreams related to home. For example, if the user is at work, the reception unit will prioritize recording dreams related to work. This allows the reception unit to prioritize recording dreams that are highly relevant based on the user's geographical location. Geographical location information is obtained through specific methods such as GPS data or location services. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can determine the priority of recording using an AI model that receives the user's geographical location data as input and outputs data for prioritizing the recording of highly relevant dreams.

[0075] The reception unit can analyze the user's social media activity when recording dreams and record relevant dreams. For example, the reception unit can record dreams related to topics the user spends a lot of time on social media. For example, the reception unit can record dreams related to posts the user has recently "liked". For example, the reception unit can record dreams related to accounts the user follows. This allows the reception unit to record relevant dreams based on the user's social media activity. Social media activity is analyzed using specific analytical methods such as post content, the number of likes, and comments. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can process the recording using an AI model that takes the user's social media activity data as input and outputs data for recording relevant dreams.

[0076] The generation unit can estimate the user's emotions and adjust the representation of the generated images based on the estimated emotions. For example, if the user is relaxed, the generation unit generates images with soft tones. If the user is excited, the generation unit generates images with vivid tones. If the user is sad, the generation unit generates images with calm tones. This allows the representation of images to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can adjust the representation using a generation AI model that takes user emotion data as input and outputs data for adjusting the representation of the generated images.

[0077] The generation unit can adjust the level of detail of the generated image based on the importance of the dream. For example, the generation unit generates a detailed image for important dreams. For example, it generates a simple image for general dreams. For example, it generates an image by referencing past images for recurring dreams. This allows the level of detail to be adjusted based on the importance of the dream. The level of detail is adjusted using specific adjustment criteria such as resolution and detail level. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can adjust the level of detail using a generation AI model that takes dream importance data as input and outputs data for adjusting the level of detail of the generated image.

[0078] The generation unit can apply different generation algorithms depending on the dream category when generating images. For example, in the case of an adventure dream, the generation unit generates a vividly colored and dynamic image. For example, in the case of a horror dream, the generation unit generates a dark and tense image. For example, in the case of a happy dream, the generation unit generates a bright and calm image. This allows for the application of different generation algorithms depending on the dream category. The generation algorithms can be of specific types, such as GAN, VAE, and deep learning. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or not. For example, the generation unit can apply algorithms using a generative AI model that takes dream category data as input and outputs data for applying different generation algorithms.

[0079] The generation unit can estimate the user's emotions and adjust the length of the generated image based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a longer image. For example, if the user is in a hurry, the generation unit will generate a shorter image. For example, if the user is excited, the generation unit will generate an image of a moderate length. This allows the length of the image to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can adjust the length using a generation AI model that takes user emotion data as input and outputs data for adjusting the length of the generated image.

[0080] The generation unit can determine the generation priority based on the dream recording date when generating images. For example, the generation unit may prioritize imaging recently recorded dreams. For example, the generation unit may prioritize imaging dreams related to specific events. For example, the generation unit may prioritize imaging dreams that the user frequently experiences. This allows the generation priority to be determined based on the dream recording date. The generation priority is determined by specific criteria such as recording date and importance. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can determine the priority using a generation AI model that takes dream recording date data as input and outputs data for determining the generation priority.

[0081] The generation unit can adjust the order of image generation based on the relevance of the dreams. For example, the generation unit may prioritize image generation of dreams that the user is particularly interested in. For example, the generation unit may prioritize image generation of dreams that the user wants to share with other users. For example, the generation unit may prioritize image generation of dreams that the user frequently experiences. This allows the order of generation to be adjusted based on the relevance of the dreams. The order of generation is adjusted using specific adjustment criteria such as relevance and importance. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can adjust the order using a generation AI model that takes dream relevance data as input and outputs data for adjusting the order of generation.

[0082] The sharing section can estimate the user's emotions and adjust how the shares are displayed based on the estimated emotions. For example, if the user is relaxed, the sharing section may provide a display method that includes detailed information. If the user is stressed, the sharing section may provide a simple and highly visible display method. If the user is in a hurry, the sharing section may provide a display method that gets straight to the point. This allows the sharing display method to be adjusted according to 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 sharing section may be performed using AI, for example, or without AI. For example, the sharing section can adjust the display method using an AI model that takes user emotion data as input and outputs data for adjusting how the shares are displayed.

[0083] The sharing function can adjust the level of detail in sharing based on the content of the dream. For example, in the case of an important dream, the sharing function provides a sharing method that includes detailed information. For example, in the case of a general dream, the sharing function provides a sharing method that includes concise information. For example, in the case of a recurring dream, the sharing function provides information by referring to past sharing information. This allows the level of detail in sharing to be adjusted based on the content of the dream. The level of detail in sharing is adjusted by specific adjustment criteria such as the amount of information and the precision of the display. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can adjust the level of detail using an AI model that takes dream content data as input and outputs data for adjusting the level of detail in sharing.

[0084] The sharing section can apply different sharing algorithms depending on the dream category during sharing. For example, for adventure dreams, the sharing section provides a sharing method with vivid colors and movement. For example, for horror dreams, the sharing section provides a sharing method with dark tones and tension. For example, for happy dreams, the sharing section provides a sharing method with bright tones and calmness. This allows different sharing algorithms to be applied depending on the dream category. The sharing algorithm is applied in specific ways, such as data compression methods and sharing protocols. Some or all of the above processing in the sharing section may be performed using AI, for example, or not. For example, the sharing section can apply algorithms using an AI model that takes dream category data as input and outputs data for applying different sharing algorithms.

[0085] The sharing unit can estimate the user's emotions and determine sharing priorities based on the estimated emotions. For example, if the user is excited, the sharing unit might suggest sharing immediately. If the user is relaxed, the sharing unit might suggest sharing with more detailed information. If the user is sad, the sharing unit might suggest prioritizing the sharing of positive dreams. This allows the sharing priority to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine 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 sharing unit may be performed using AI or not. For example, the sharing unit can determine priorities using an AI model that takes user emotion data as input and outputs data for determining sharing priorities.

[0086] The sharing function can adjust the order of sharing based on when the dreams were recorded. For example, the sharing function may prioritize sharing recently recorded dreams. For example, the sharing function may prioritize sharing dreams related to specific events. For example, the sharing function may prioritize sharing dreams that the user frequently experiences. This allows the order of sharing to be adjusted based on when the dreams were recorded. The order of sharing is adjusted using specific adjustment criteria such as recording date and relevance. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can adjust the order using an AI model that takes dream recording date data as input and outputs data for adjusting the order of sharing.

[0087] The sharing function can adjust the order of sharing based on the relevance of the dreams. For example, the sharing function may prioritize sharing dreams that the user is particularly interested in. For example, the sharing function may prioritize sharing dreams that the user wants to share with other users. For example, the sharing function may prioritize sharing dreams that the user frequently experiences. This allows the order of sharing to be adjusted based on the relevance of the dreams. The order of sharing is adjusted using specific adjustment criteria such as relevance and importance. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can adjust the order using an AI model that takes dream relevance data as input and outputs data for adjusting the order of sharing.

[0088] The trading unit can estimate the user's emotions and adjust the trading method based on the estimated emotions. For example, if the user is relaxed, the trading unit provides a trading method that includes detailed information. For example, if the user is tense, the trading unit provides a simple and easy-to-understand trading method. For example, if the user is in a hurry, the trading unit provides a trading method that gets straight to the point. This allows the trading method to be adjusted according to 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the trading unit may be performed using AI, for example, or not using AI. For example, the trading unit can adjust the method using an AI model that takes user emotion data as input and outputs data for adjusting the trading method.

[0089] The trading unit can adjust the level of detail in a trade based on the content of the dream. For example, in the case of an important dream, the trading unit provides a trading method that includes detailed information. For example, in the case of a general dream, the trading unit provides a trading method that includes concise information. For example, in the case of a recurring dream, the trading unit provides information based on past trading data. This allows the level of detail in a trade to be adjusted based on the content of the dream. The level of detail in a trade is adjusted using specific adjustment criteria, such as the amount of transaction information and the precision of the display. Some or all of the above processing in the trading unit may be performed using AI, for example, or without AI. For example, the trading unit can adjust the level of detail using an AI model that takes dream content data as input and outputs data for adjusting the level of detail in a trade.

[0090] The trading unit can apply different trading algorithms depending on the dream category at the time of trading. For example, in the case of an adventure dream, the trading unit provides a trading method with vivid colors and movement. For example, in the case of a horror dream, the trading unit provides a trading method with dark tones and tension. For example, in the case of a happiness dream, the trading unit provides a trading method with bright tones and calmness. This allows different trading algorithms to be applied depending on the dream category. The trading algorithm is applied in specific forms such as pricing algorithms and trading protocols. Some or all of the above processing in the trading unit may be performed using AI, for example, or not using AI. For example, the trading unit can apply algorithms using an AI model that takes dream category data as input and outputs data for applying different trading algorithms.

[0091] The trading unit can estimate the user's emotions and determine trading priorities based on the estimated emotions. For example, if the user is excited, the trading unit may suggest an immediate trade. If the user is relaxed, the trading unit may suggest a trade with detailed information. If the user is sad, the trading unit may suggest prioritizing trades related to positive dreams. This allows trading priorities to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine 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 trading unit may be performed using AI or not. For example, the trading unit can determine priorities using an AI model that takes user emotion data as input and outputs data for determining trading priorities.

[0092] The trading unit can adjust the order of transactions based on when the dreams were recorded. For example, the trading unit may prioritize the sale of recently recorded dreams. For example, the trading unit may prioritize the sale of dreams related to specific events. For example, the trading unit may prioritize the sale of dreams that the user frequently experiences. This allows the order of transactions to be adjusted based on when the dreams were recorded. The order of transactions is adjusted using specific adjustment criteria such as recording date and relevance. Some or all of the above processing in the trading unit may be performed using AI, for example, or not. For example, the trading unit can adjust the order using an AI model that takes dream recording date data as input and outputs data for adjusting the order of transactions.

[0093] The trading unit can adjust the order of transactions based on the relevance of dreams. For example, the trading unit may prioritize the sale of dreams that the user is particularly interested in. For example, the trading unit may prioritize the sale of dreams that the user wants to share with other users. For example, the trading unit may prioritize the sale of dreams that the user frequently experiences. This allows the order of transactions to be adjusted based on the relevance of dreams. The order of transactions is adjusted using specific adjustment criteria such as relevance and importance. Some or all of the above processing in the trading unit may be performed using AI, for example, or not. For example, the trading unit can adjust the order using an AI model that takes dream relevance data as input and outputs data for adjusting the order of transactions.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The dream tracking system can also acquire user health data and analyze the relationship between dream content and health status. For example, it can acquire data such as the user's sleep patterns, heart rate, and stress levels, and correlate this data with dream content. This allows users to understand the relationship between their health status and dream content and use it to manage their health. For example, if a user is stressed, it can analyze how that stress is affecting their dreams. It can also analyze how quality sleep is affecting the content of their dreams. Furthermore, it can analyze the user's heart rate data and how heart rate fluctuations are affecting dream content. This allows users to gain a deeper understanding of the relationship between their health status and dream content.

[0096] The dream tracking system can further estimate the user's emotions and analyze the dream content based on those estimated emotions. For example, when a user records the content of a dream, the system can estimate their emotions at that time and analyze the dream content based on those emotions. This allows users to understand the relationship between their emotions and the content of their dreams, which can be useful for emotional management. For instance, when a user records the content of a dream, the system can use an emotion engine to estimate the user's emotions and analyze the dream content based on those emotions. It can also use the emotion engine to estimate the user's emotions when they record the content of their dreams and classify the dream content based on those emotions. Furthermore, it can use the emotion engine to estimate the user's emotions when they record the content of their dreams and filter the dream content based on those emotions. This allows users to gain a deeper understanding of the relationship between their emotions and the content of their dreams.

[0097] The dream tracking system can further acquire user lifestyle data and analyze the relationship between dream content and lifestyle habits. For example, it can acquire data on the user's diet, exercise, and sleep, and correlate this data with dream content. This allows users to understand the relationship between their lifestyle and dream content and use this information to improve their lifestyle. For instance, if a user eats a particular meal, the system can analyze how that meal affects the content of their dreams. Similarly, if a user exercises, the system can analyze how that exercise affects the content of their dreams. Furthermore, it can analyze the user's sleep data and how sleep quality affects the content of their dreams. This allows users to gain a deeper understanding of the relationship between their lifestyle and dream content.

[0098] The dream tracking system can further estimate the user's emotions and adjust how they share their dreams based on those emotions. For example, when a user shares their dreams, the system can estimate their emotions at the time and adjust the sharing method accordingly. This allows users to choose an appropriate sharing method based on their emotions. For instance, if a user is relaxed, the system can provide a sharing method that includes detailed information. If a user is stressed, it can provide a simple and easy-to-understand sharing method. Furthermore, if a user is in a hurry, it can provide a concise sharing method. This allows users to choose an appropriate sharing method based on their emotions.

[0099] The dream tracking system can also acquire the user's geographical location and analyze the relationship between dream content and geographical location. For example, if the user is traveling, the system can analyze dream content related to their travel destination. This allows the user to understand the relationship between their geographical location and dream content and use this information to plan their trip. For example, if the user is staying in a specific location, the system can analyze dream content related to that location. Similarly, if the user is at home, the system can analyze dream content related to home. Furthermore, if the user is at work, the system can analyze dream content related to work. This allows the user to gain a deeper understanding of the relationship between their geographical location and dream content.

[0100] The dream tracking system can further estimate the user's emotions and adjust the way they buy and sell dream content based on those estimated emotions. For example, when a user buys or sells dream content, the system can estimate their emotions at that time and adjust the buying and selling method accordingly. This allows users to choose an appropriate buying and selling method that suits their emotions. For instance, if a user is relaxed, a detailed buying and selling method can be provided. If a user is stressed, a simple and highly visual buying and selling method can be provided. Furthermore, if a user is in a hurry, a concise buying and selling method can be provided. This allows users to choose an appropriate buying and selling method that suits their emotions.

[0101] The dream tracking system can also acquire users' social media activity and analyze the relationship between dream content and social media activity. For example, it can analyze dream content related to topics that users spend a lot of time on social media. This allows users to understand the relationship between their social media activity and dream content and reconsider how they use social media. For example, it can analyze dream content related to posts that users have recently "liked." It can also analyze dream content related to accounts that users follow. Furthermore, it can analyze dream content related to topics that users spend a lot of time on. This allows users to gain a deeper understanding of the relationship between their social media activity and dream content.

[0102] The dream tracking system can further estimate the user's emotions and filter the dream content based on those emotions. For example, when a user records their dreams, the system can estimate their emotions at the time and filter the dream content based on those emotions. This allows the user to select dream content that is appropriate to their emotions. For instance, if the user is sad, positive dreams can be prioritized for recording. Similarly, if the user is excited, detailed dreams can be prioritized for recording. Furthermore, if the user is tired, concise dreams can be prioritized for recording. This allows the user to select dream content that is appropriate to their emotions.

[0103] The dream tracking system can further analyze the user's past dream recording history and analyze the relationship between dream content and past records. For example, it can analyze the content of dreams recorded by the user in the past and relate it to the content of current dreams. This allows the user to understand the relationship between their past and present dreams and find dream patterns. For example, it can analyze the content of dreams the user has repeatedly had in the past and relate it to the content of current dreams. It can also relate dreams related to a specific theme in the past to the content of current dreams. Furthermore, if the user has dreams featuring a specific character in the past, it can relate that character to the content of current dreams. This allows the user to gain a deeper understanding of the relationship between their past and present dreams.

[0104] The dream tracking system can further estimate the user's emotions and adjust the way it analyzes the dream content based on those estimated emotions. For example, when a user analyzes their dream content, the system can estimate their emotions at that time and adjust the analysis method accordingly. This allows the user to choose an analysis method appropriate to their emotions. For instance, if the user is relaxed, a detailed analysis method can be provided. If the user is stressed, a simple and easy-to-understand analysis method can be provided. Furthermore, if the user is in a hurry, a concise analysis method can be provided. This allows the user to choose an analysis method appropriate to their emotions.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk records the content of the dream. Users can record the content of their dreams in various formats, such as text, audio, and video. For example, users can record their dreams by typing text within the app, recording audio using the microphone, or taking video using the camera. Step 2: The generation unit analyzes the dream content recorded by the reception unit and generates an image. The generation unit uses a generation AI to analyze text data, audio data, and video data, and generates an image based on that analysis. For example, it receives a prompt such as "Please represent the content of this dream as an image," analyzes the dream content, and generates an image. Step 3: The sharing section shares the images generated by the generation section. Users can share the content of their dreams with other users, or with friends or specific groups. They can also form dream interpretation communities and receive interpretations and advice from other users. Step 4: The trading unit buys and sells the images generated by the generation unit. Users can buy and sell the generated images using electronic payment or as works of art. They can also trade the generated images as NFTs. For example, users can trade popular dream scenes as NFTs.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the reception unit, generation unit, sharing unit, and trading unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit records the content of the user's dream using the reception device 38 of the smart device 14. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the content of the dream using a generation AI and generates an image. The sharing unit shares the generated image with other users using, for example, the output device 40 of the smart device 14. The trading unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which buys and sells the generated image using electronic payment. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, generation unit, sharing unit, and trading unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit records the content of the user's dream using the microphone 238 of the smart glasses 214. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which analyzes the content of the dream using a generation AI and generates an image. The sharing unit shares the generated image with other users using, for example, the speaker 240 of the smart glasses 214. The trading unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which buys and sells the generated image using electronic payment. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, generation unit, sharing unit, and trading unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit records the content of the user's dream using the microphone 238 of the headset terminal 314. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the content of the dream using a generation AI and generates an image. The sharing unit shares the generated image with other users using the display 343 of the headset terminal 314. The trading unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which buys and sells the generated image using electronic payment. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[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 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.

[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 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.

[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 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.

[0159] Each of the multiple elements described above, including the reception unit, generation unit, sharing unit, and trading unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit records the content of the user's dream using the microphone 238 of the robot 414. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the content of the dream using a generation AI and generates an image. The sharing unit shares the generated image with other users using, for example, the speaker 240 of the robot 414. The trading unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which buys and sells the generated image using electronic payment. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A reception desk that records the contents of dreams, A generation unit analyzes the content of the dream recorded by the reception unit and generates an image, A sharing unit that shares the images generated by the generation unit, The system comprises a trading unit for buying and selling images generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generate images using a generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned shared portion is, Users share the content of their dreams with other users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned sales department, The generated images are traded as NFTs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of dream recording based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system analyzes the user's past dream recording history and selects the optimal recording method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When recording dreams, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and determines the priority of dreams to record based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When recording dreams, the system prioritizes recording dreams that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When recording dreams, the system analyzes the user's social media activity and records related dreams. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the way images are represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating images, adjust the level of detail based on the importance of the dream. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating images, different generation algorithms are applied depending on the dream category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the length of the generated images based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating images, the generation priority is determined based on the timing of the dream recording. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating images, the generation order is adjusted based on the relevance of the dreams. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned shared portion is, It estimates the user's sentiment and adjusts how shared content is displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned shared portion is, When sharing, adjust the level of detail based on the content of the dream. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned shared portion is, When sharing, different sharing algorithms are applied depending on the dream category. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned shared portion is, It estimates the user's emotions and determines sharing priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned shared portion is, When sharing, adjust the sharing order based on when the dream was recorded. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned shared portion is, When sharing, adjust the sharing order based on the relevance of the dreams. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned sales department, It estimates user sentiment and adjusts trading methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned sales department, When buying or selling, adjust the level of detail in the transaction based on the content of the dream. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned sales department, When buying or selling, different trading algorithms are applied depending on the category of the dream. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned sales department, It estimates user sentiment and determines buying and selling priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned sales department, When buying or selling, adjust the order of transactions based on when the dream was recorded. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned sales department, When buying or selling, adjust the order of transactions based on the relevance of the dreams. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 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 reception desk that records the contents of dreams, A generation unit analyzes the content of the dream recorded by the reception unit and generates an image, A sharing unit that shares the images generated by the generation unit, The system comprises a trading unit for buying and selling images generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generate images using AI. The system according to feature 1.

3. The aforementioned shared portion is, Users share the content of their dreams with other users. The system according to feature 1.

4. The aforementioned sales department, The generated images are traded as NFTs. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of dream recording based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is The system analyzes the user's past dream recording history and selects the optimal recording method. The system according to feature 1.

7. The aforementioned reception unit is When recording dreams, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of dreams to record based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When recording dreams, the system prioritizes recording dreams that are highly relevant based on the user's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When recording dreams, the system analyzes the user's social media activity and records related dreams. The system according to feature 1.

Images