system

Abstract

An object of the system according to the embodiment is to allow a user to easily create a custom stamp that expresses his / her idea or feeling and sell the custom stamp as a digital asset having uniqueness and rarity.SOLUTION: A system according to an embodiment includes a stamp generation unit, an NFT registration unit, and a marketplace provision unit. The stamp generation unit analyzes the user's text or sketch and generates a stamp. The NFT registration unit registers the stamp generated by the stamp generation unit on the blockchain as the NFT. The marketplace providing unit sells the stamp registered by the NFT registration unit on the marketplace.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made it difficult for users to easily create custom stamps that express their ideas and emotions and sell them as unique and rare digital assets.

[0005] The system according to the embodiment aims to enable users to easily create custom stamps that express their own ideas and emotions and sell them as unique and rare digital assets. [Means for solving the problem]

[0006] A system according to an embodiment includes a stamp generation unit, an NFT registration unit, and a marketplace providing unit. The stamp generation unit analyzes a user's text or sketch and generates a stamp. The NFT registration unit registers the stamp generated by the stamp generation unit as an NFT on a blockchain. The marketplace providing unit sells the stamp registered by the NFT registration unit on the marketplace. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily create custom stamps that express their ideas and emotions and sell them as unique and rare digital assets. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The custom sticker generation system according to an embodiment of the present invention allows users to easily create custom stickers that express their ideas and emotions, register them as NFTs, and sell them on the marketplace. This allows the custom sticker generation system to add new value to users' creativity and create new revenue streams for the LINE platform.

[0029] A custom stamp generation system according to an embodiment includes a stamp generation unit, an NFT registration unit, and a marketplace provision unit. The stamp generation unit analyzes a user's text or sketch and generates a stamp. For example, if a user inputs "thank you," the generation AI generates a stamp expressing gratitude based on the text. Alternatively, if a user draws a simple sketch, the generation AI generates a stamp based on the sketch. The generation AI analyzes the user's input and generates a stamp using a text generation AI (e.g., LLM) or a multimodal generation AI. The NFT registration unit registers the stamp generated by the stamp generation unit as an NFT on a blockchain. For example, metadata such as the stamp's creation date and time, creator information, and a unique stamp identifier are generated and recorded on the blockchain. This ensures the stamp's uniqueness and rarity. The marketplace provision unit sells the stamps registered by the NFT registration unit on a marketplace. For example, the generation AI evaluates the stamp's market value and suggests an appropriate price. Users can sell the stamps based on the suggested price. This allows users to monetize their creativity. As a result, the custom stamp generation system according to the embodiment allows users to easily create custom stamps that express their ideas and emotions, register them as NFTs, and sell them on the marketplace.

[0030] The stamp generation unit can analyze a user's past stamp creation history and develop a stamp generation algorithm optimized for each individual user. For example, the stamp generation unit stores the user's past stamp creation history in a database and analyzes that data to understand the user's preferences and trends. For example, it identifies the colors and designs that the user frequently uses. The stamp generation unit also develops a stamp generation algorithm optimized for each individual user based on the past stamp creation history. For example, it analyzes the design patterns of stamps that the user has created in the past and generates new stamps based on those patterns. This makes it possible to generate stamps that match the user's preferences.

[0031] The stamp generation unit can customize stamp designs based on a theme or color palette selected by the user. For example, the stamp generation unit provides an interface that allows the user to select a theme or color palette when creating a stamp. For example, if the user selects the theme "summer," the generation AI generates stamps with a summery design. Also, if the user selects a color palette, the generation AI customizes the stamp design based on that color palette. For example, if the user selects a blue palette, the generation AI generates stamps with a blue base. This makes it possible to customize stamp designs based on the user's selections.

[0032] The stamp generation unit can strengthen cooperation with other SNS platforms, enabling the generated stamps to be shared across multiple platforms. For example, the stamp generation unit adds a function that enables the generated stamps to be shared across other SNS platforms (e.g., Facebook and Instagram). For example, the stamp generation unit enables a user to use stamps created on LINE in Facebook posts. The stamp generation unit also provides an interface that enables the generated stamps to be shared across multiple platforms. For example, after a user creates a stamp, the user can post the stamp on multiple SNS platforms by clicking a share button. This allows the generated stamps to be shared across multiple SNS platforms.

[0033] The NFT registration unit records the stamp creation process on the blockchain, making the stamp creation history transparent, thereby further strengthening authenticity. The NFT registration unit, for example, builds a system that records the stamp creation process on the blockchain, making the stamp creation history transparent. For example, the stamp creation date and time and information about the creator are recorded on the blockchain. The NFT registration unit also evaluates the authenticity of the stamp based on the stamp creation history. For example, by confirming that the stamp creation process is transparent, the authenticity of the stamp can be guaranteed. This makes the stamp creation history transparent and strengthens authenticity.

[0034] The NFT registration unit can promote collaboration with other digital artworks and create composite NFT works. For example, when converting a stamp into an NFT, the NFT registration unit will build a system that promotes collaboration with other digital artworks, such as digital paintings and music. For example, it will create an NFT work that combines a stamp and a digital painting. The NFT registration unit also supports collaboration with other digital artworks to create composite NFT works. For example, it will create an NFT work that combines a stamp and music. This will promote collaboration with other digital artworks and create composite NFT works.

[0035] The NFT registration unit can automate the stamp NFT conversion process, allowing users to easily convert multiple stamps into NFTs at once. For example, the NFT registration unit automates the stamp NFT conversion process and builds a system that allows users to easily convert multiple stamps into NFTs at once. For example, it provides a function that allows users to convert multiple stamps selected by the user into NFTs at once. Furthermore, the NFT registration unit reduces the user's workload by automating the NFT conversion process. For example, a user can convert multiple stamps into NFTs at once by simply selecting the stamps and clicking a button. This automates the stamp NFT conversion process, allowing users to easily convert multiple stamps into NFTs at once.

[0036] The marketplace providing unit can analyze real-time market data when assessing the market value of stamps and develop an algorithm that dynamically adjusts prices. The marketplace providing unit, for example, analyzes real-time market data and develops an algorithm that assesses the market value of stamps. For example, the price is dynamically adjusted based on the supply and demand of stamps. The marketplace providing unit also optimizes the price of stamps based on real-time market data. For example, the price is set taking into account the popularity and rarity of the stamp. This makes it possible to analyze real-time market data and dynamically adjust prices.

[0037] The marketplace providing unit can analyze the sales history of stamps and make personalized sales proposals based on the user's purchasing tendencies. For example, the marketplace providing unit stores the sales history of stamps in a database and analyzes the data to understand the user's purchasing tendencies. For example, it identifies the types and designs of stamps that the user frequently purchases. The marketplace providing unit also makes personalized sales proposals based on the user's purchasing tendencies. For example, it proposes stamps with designs similar to stamps that the user has purchased in the past. This makes it possible to make personalized sales proposals based on the user's purchasing tendencies.

[0038] The marketplace providing unit can introduce a stamp auction function into the marketplace, allowing users to compete over stamp prices. The marketplace providing unit, for example, introduces an auction function into the marketplace and builds a system in which users can compete over stamp prices. For example, a user sets a bid price for a stamp, and other users make bids that exceed that price. The marketplace providing unit also provides an interface that displays the progress of the auction in real time. For example, it displays the current highest bid price and information about the bidders. This allows users to compete over stamp prices.

[0039] The marketplace providing unit can sell stamps bundled with other digital content. For example, the marketplace providing unit builds a system for selling stamps bundled with other digital content (e.g., digital goods or limited content). For example, stamps and digital goods are sold as a set. The marketplace providing unit also promotes the bundle sale. For example, it provides benefits and discount information for the bundle sale. This allows stamps to be sold bundled with other digital content.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The custom sticker generation system can also utilize the user's geographical information to generate stickers tailored to local trends. For example, stickers can be generated based on characters or events popular in a particular region. Users can also create stickers themed around local specialties or tourist attractions while traveling. This allows the system to provide stickers that reflect the culture and trends of each region.

[0042] The custom stamp generation system can also analyze a user's past purchase history and predict the stamp designs and themes that the user will like. For example, it can analyze the design patterns of stamps the user has purchased in the past and suggest new stamps based on those patterns. Also, if a user likes a particular theme (e.g., animals or food), it can suggest stamps based on that theme. This makes it possible to provide stamps that match the user's preferences.

[0043] The custom stamp generation system can also be equipped with a function that encourages collaboration with other users when users create stamps. For example, multiple users can jointly create stamps and register them as NFTs. Users can also customize their own stamps by referring to stamp designs created by other users. This promotes interaction between users and allows them to jointly create creative stamps.

[0044] The custom stamp generation system can also be equipped with a function where an AI assistant can provide real-time design advice when a user is creating a stamp. For example, as the user designs the stamp, the AI ​​assistant can suggest colors and layouts. If the user is unsure about the design, the AI ​​assistant can also present reference samples. This allows users to create stamps more efficiently.

[0045] The custom stamp generation system can also have a function that suggests designs to users when they create stamps, taking into account the popularity of stamps from the past. For example, it can analyze the design patterns of stamps that were popular in the past and suggest new stamps based on those patterns. Also, if a user has a preference for a particular theme or design, it can suggest stamps based on that theme or design. This allows users to create stamps by referring to popular designs from the past.

[0046] The custom stamp generation system can also be equipped with a function whereby AI automatically generates animations for stamps when users create them. For example, if a user creates a still image stamp, the AI ​​can add movement to the stamp to generate an animated stamp. In addition, if the user specifies a specific movement, an animated stamp reflecting that movement can be generated. This allows users to easily create animated stamps.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The stamp generator analyzes the user's text or sketch and generates a stamp. For example, if the user types "thank you," the generation AI generates a stamp expressing gratitude based on that text. Alternatively, if the user draws a simple sketch, the generation AI generates a stamp based on that sketch. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the user's input and generate a stamp. Step 2: The NFT registration unit registers the stamp generated by the stamp generation unit as an NFT on the blockchain. For example, metadata such as the stamp's creation date and time, creator information, and a unique identifier for the stamp are generated and recorded on the blockchain. This ensures the stamp's uniqueness and rarity. Step 3: The marketplace provider sells the stamps registered by the NFT registration unit on the marketplace. For example, the generation AI evaluates the market value of the stamps and suggests an appropriate price. Users can sell the stamps based on the suggested price. This allows users to monetize their creativity.

[0049] (Example 2) The custom sticker generation system according to an embodiment of the present invention allows users to easily create custom stickers that express their ideas and emotions, register them as NFTs, and sell them on the marketplace. This allows the custom sticker generation system to add new value to users' creativity and create new revenue streams for the LINE platform.

[0050] A custom stamp generation system according to an embodiment includes a stamp generation unit, an NFT registration unit, and a marketplace provision unit. The stamp generation unit analyzes a user's text or sketch and generates a stamp. For example, if a user inputs "thank you," the generation AI generates a stamp expressing gratitude based on the text. Alternatively, if a user draws a simple sketch, the generation AI generates a stamp based on the sketch. The generation AI analyzes the user's input and generates a stamp using a text generation AI (e.g., LLM) or a multimodal generation AI. The NFT registration unit registers the stamp generated by the stamp generation unit as an NFT on a blockchain. For example, metadata such as the stamp's creation date and time, creator information, and a unique stamp identifier are generated and recorded on the blockchain. This ensures the stamp's uniqueness and rarity. The marketplace provision unit sells the stamps registered by the NFT registration unit on a marketplace. For example, the generation AI evaluates the stamp's market value and suggests an appropriate price. Users can sell the stamps based on the suggested price. This allows users to monetize their creativity. As a result, the custom stamp generation system according to the embodiment allows users to easily create custom stamps that express their ideas and emotions, register them as NFTs, and sell them on the marketplace.

[0051] The stamp generation unit can generate stamps based on emotions inferred from text or sketches entered by the user. For example, the stamp generation unit analyzes text entered by the user and generates stamps based on emotions inferred from the text. For example, if the user enters "happy," the generation AI generates a smiling stamp that reflects that emotion. The stamp generation unit can also analyze a sketch drawn by the user and generate stamps based on emotions inferred from the sketch. For example, if the user draws a sketch of a heart, the generation AI generates a stamp of love that reflects that emotion. This makes it possible to generate stamps that reflect the user's emotions.

[0052] The stamp generation unit can analyze a user's past stamp creation history and develop a stamp generation algorithm optimized for each individual user. For example, the stamp generation unit stores the user's past stamp creation history in a database and analyzes that data to understand the user's preferences and trends. For example, it identifies the colors and designs that the user frequently uses. The stamp generation unit also develops a stamp generation algorithm optimized for each individual user based on the past stamp creation history. For example, it analyzes the design patterns of stamps that the user has created in the past and generates new stamps based on those patterns. This makes it possible to generate stamps that match the user's preferences.

[0053] The stamp generation unit can customize stamp designs based on a theme or color palette selected by the user. For example, the stamp generation unit provides an interface that allows the user to select a theme or color palette when creating a stamp. For example, if the user selects the theme "summer," the generation AI generates stamps with a summery design. Also, if the user selects a color palette, the generation AI customizes the stamp design based on that color palette. For example, if the user selects a blue palette, the generation AI generates stamps with a blue base. This makes it possible to customize stamp designs based on the user's selections.

[0054] The stamp generation unit can use voice input to generate stamps that reflect the user's emotions and intentions. For example, when a user creates a stamp using voice input, the generation AI analyzes the voice data and generates a stamp that reflects the emotion and intention. For example, if the user voice inputs "thank you," the generation AI generates a stamp that expresses gratitude. Also, if the user voice inputs "do your best," the generation AI generates a stamp that expresses encouragement. In this way, stamps that reflect the user's emotions and intentions can be generated using voice input.

[0055] The stamp generation unit can strengthen cooperation with other SNS platforms, enabling the generated stamps to be shared across multiple platforms. For example, the stamp generation unit adds a function that enables the generated stamps to be shared across other SNS platforms (e.g., Facebook and Instagram). For example, the stamp generation unit enables a user to use stamps created on LINE in Facebook posts. The stamp generation unit also provides an interface that enables the generated stamps to be shared across multiple platforms. For example, after a user creates a stamp, the user can post the stamp on multiple SNS platforms by clicking a share button. This allows the generated stamps to be shared across multiple SNS platforms.

[0056] The stamp generation unit uses the emotion estimation function to analyze the emotions of the user when creating a stamp in real time, and can make suggestions for creating stamps that will elicit positive emotions. For example, when a user creates a stamp, the stamp generation unit uses the emotion estimation function to analyze the user's emotions in real time, and can make suggestions for creating stamps that will elicit positive emotions. For example, if a user is creating a stamp feeling sad, the generation AI will suggest an encouraging stamp. Also, if the user is angry, the generation AI will suggest a stamp for relaxation. This makes it possible to analyze the user's emotions in real time and make suggestions for creating stamps that will elicit positive emotions.

[0057] The NFT registration unit can use the emotion estimation function to evaluate the emotional value of a stamp and set the rarity of the NFT based on that value. The NFT registration unit, for example, uses the emotion estimation function to build a system that evaluates the emotional value of generated stamps. For example, it analyzes the intensity and type of emotion expressed by the stamp and sets the rarity of the NFT based on the results. The NFT registration unit also ranks the rarity of the stamp based on the emotional value evaluation result. For example, stamps with high emotional value are set to be highly rare. This makes it possible to evaluate the emotional value of a stamp and set the rarity of the NFT based on that value.

[0058] The NFT registration unit records the stamp creation process on the blockchain, making the stamp creation history transparent, thereby further strengthening authenticity. The NFT registration unit, for example, builds a system that records the stamp creation process on the blockchain, making the stamp creation history transparent. For example, the stamp creation date and time and information about the creator are recorded on the blockchain. The NFT registration unit also evaluates the authenticity of the stamp based on the stamp creation history. For example, by confirming that the stamp creation process is transparent, the authenticity of the stamp can be guaranteed. This makes the stamp creation history transparent and strengthens authenticity.

[0059] The NFT registration unit can add information that reflects the user's emotions and intentions to the stamp's metadata, enriching the stamp's background story. The NFT registration unit, for example, builds a system that adds information that reflects the user's emotions and intentions to the stamp's metadata. For example, the metadata could include the emotion score at the time the stamp was created and the user's comments. The NFT registration unit also adds information that reflects the user's intentions to enrich the stamp's background story. For example, the metadata could record the background to the stamp's creation and the user's intentions. This can enrich the stamp's background story.

[0060] The NFT registration unit can promote collaboration with other digital artworks and create composite NFT works. For example, when converting a stamp into an NFT, the NFT registration unit will build a system that promotes collaboration with other digital artworks, such as digital paintings and music. For example, it will create an NFT work that combines a stamp and a digital painting. The NFT registration unit also supports collaboration with other digital artworks to create composite NFT works. For example, it will create an NFT work that combines a stamp and music. This will promote collaboration with other digital artworks and create composite NFT works.

[0061] The NFT registration unit can automate the stamp NFT conversion process, allowing users to easily convert multiple stamps into NFTs at once. For example, the NFT registration unit automates the stamp NFT conversion process and builds a system that allows users to easily convert multiple stamps into NFTs at once. For example, it provides a function that allows users to convert multiple stamps selected by the user into NFTs at once. Furthermore, the NFT registration unit reduces the user's workload by automating the NFT conversion process. For example, a user can convert multiple stamps into NFTs at once by simply selecting the stamps and clicking a button. This automates the stamp NFT conversion process, allowing users to easily convert multiple stamps into NFTs at once.

[0062] The marketplace providing unit can use the emotion estimation function to develop a marketing strategy based on the user's emotions and promote sales of stamps. The marketplace providing unit, for example, uses the emotion estimation function to analyze user emotion data and build a system that develops a marketing strategy based on the results. For example, stamps that users have positive emotions about are promoted preferentially. The marketplace providing unit also uses the emotion data to carry out promotions to increase users' purchasing motivation. For example, stamps that users can easily empathize with emotionally are featured. This allows for the development of a marketing strategy based on the user's emotions and the promotion of stamp sales.

[0063] The marketplace providing unit can analyze real-time market data when assessing the market value of stamps and develop an algorithm that dynamically adjusts prices. The marketplace providing unit, for example, analyzes real-time market data and develops an algorithm that assesses the market value of stamps. For example, the price is dynamically adjusted based on the supply and demand of stamps. The marketplace providing unit also optimizes the price of stamps based on real-time market data. For example, the price is set taking into account the popularity and rarity of the stamp. This makes it possible to analyze real-time market data and dynamically adjust prices.

[0064] The marketplace providing unit can analyze the sales history of stamps and make personalized sales proposals based on the user's purchasing tendencies. For example, the marketplace providing unit stores the sales history of stamps in a database and analyzes the data to understand the user's purchasing tendencies. For example, it identifies the types and designs of stamps that the user frequently purchases. The marketplace providing unit also makes personalized sales proposals based on the user's purchasing tendencies. For example, it proposes stamps with designs similar to stamps that the user has purchased in the past. This makes it possible to make personalized sales proposals based on the user's purchasing tendencies.

[0065] The marketplace providing unit can introduce a stamp auction function into the marketplace, allowing users to compete over stamp prices. The marketplace providing unit, for example, introduces an auction function into the marketplace and builds a system in which users can compete over stamp prices. For example, a user sets a bid price for a stamp, and other users make bids that exceed that price. The marketplace providing unit also provides an interface that displays the progress of the auction in real time. For example, it displays the current highest bid price and information about the bidders. This allows users to compete over stamp prices.

[0066] The marketplace providing unit can sell stamps bundled with other digital content. For example, the marketplace providing unit builds a system for selling stamps bundled with other digital content (e.g., digital goods or limited content). For example, stamps and digital goods are sold as a set. The marketplace providing unit also promotes the bundle sale. For example, it provides benefits and discount information for the bundle sale. This allows stamps to be sold bundled with other digital content.

[0067] The marketplace providing unit can use the emotion estimation function to analyze users' emotional reactions on the marketplace and prioritize promoting stamps that are likely to resonate emotionally. The marketplace providing unit, for example, uses the emotion estimation function to analyze users' emotional reactions on the marketplace in real time and build a system that prioritizes promoting stamps that are likely to resonate emotionally. For example, it can feature stamps that users have positive feelings about. The marketplace providing unit also evaluates the effectiveness of promotions based on the emotional reaction data. For example, it can analyze sales performance of stamps that are likely to resonate emotionally. This allows it to prioritize promoting stamps that are likely to resonate emotionally.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The custom sticker generation system can also utilize the user's geographical information to generate stickers tailored to local trends. For example, stickers can be generated based on characters or events popular in a particular region. Users can also create stickers themed around local specialties or tourist attractions while traveling. This allows the system to provide stickers that reflect the culture and trends of each region.

[0070] The stamp generation unit can estimate the user's emotions and suggest stamp designs based on those emotions. For example, if the user is feeling stressed, a design with a relaxing effect can be suggested. If the user is feeling happy, a design that will further enhance that happiness can be suggested. Furthermore, if the user wants to express gratitude, a stamp that reflects that emotion can be suggested. This makes it possible to provide stamp designs that correspond to the user's emotions.

[0071] The custom stamp generation system can also analyze a user's past purchase history and predict the stamp designs and themes that the user will like. For example, it can analyze the design patterns of stamps the user has purchased in the past and suggest new stamps based on those patterns. Also, if a user likes a particular theme (e.g., animals or food), it can suggest stamps based on that theme. This makes it possible to provide stamps that match the user's preferences.

[0072] The stamp generation unit can estimate the user's emotions and customize the color and design of the stamp based on those emotions. For example, if the user is feeling sad, a design using bright colors can be suggested. If the user is excited, a dynamic design that reflects that excitement can be suggested. Furthermore, if the user wants to relax, a design using calm colors can be suggested. In this way, it is possible to provide the color and design of the stamp according to the user's emotions.

[0073] The custom stamp generation system can also be equipped with a function that encourages collaboration with other users when users create stamps. For example, multiple users can jointly create stamps and register them as NFTs. Users can also customize their own stamps by referring to stamp designs created by other users. This promotes interaction between users and allows them to jointly create creative stamps.

[0074] The stamp generation unit can estimate the user's emotions and suggest stamp themes based on those emotions. For example, if the user is tired, stamps with a theme of refreshment can be suggested. If the user is feeling happy, a theme for sharing that happiness can be suggested. Furthermore, if the user wants to express gratitude, stamps with a theme of gratitude can be suggested. In this way, themes can be provided that correspond to the user's emotions.

[0075] The custom stamp generation system can also be equipped with a function where an AI assistant can provide real-time design advice when a user is creating a stamp. For example, as the user designs the stamp, the AI ​​assistant can suggest colors and layouts. If the user is unsure about the design, the AI ​​assistant can also present reference samples. This allows users to create stamps more efficiently.

[0076] The stamp generation unit can estimate the user's emotions and suggest stamp messages based on those emotions. For example, if the user wants to express gratitude, a message of gratitude can be suggested. Also, if the user wants to express encouragement, a message reflecting that emotion can be suggested. Furthermore, if the user wants to express congratulations, a message reflecting that congratulations can be suggested. In this way, messages can be provided that correspond to the user's emotions.

[0077] The custom stamp generation system can also have a function that suggests designs to users when they create stamps, taking into account the popularity of stamps from the past. For example, it can analyze the design patterns of stamps that were popular in the past and suggest new stamps based on those patterns. Also, if a user has a preference for a particular theme or design, it can suggest stamps based on that theme or design. This allows users to create stamps by referring to popular designs from the past.

[0078] The custom stamp generation system can also be equipped with a function whereby AI automatically generates animations for stamps when users create them. For example, if a user creates a still image stamp, the AI ​​can add movement to the stamp to generate an animated stamp. In addition, if the user specifies a specific movement, an animated stamp reflecting that movement can be generated. This allows users to easily create animated stamps.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The stamp generator analyzes the user's text or sketch and generates a stamp. For example, if the user types "thank you," the generation AI generates a stamp expressing gratitude based on that text. Alternatively, if the user draws a simple sketch, the generation AI generates a stamp based on that sketch. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the user's input and generate a stamp. Step 2: The NFT registration unit registers the stamp generated by the stamp generation unit as an NFT on the blockchain. For example, metadata such as the stamp's creation date and time, creator information, and a unique identifier for the stamp are generated and recorded on the blockchain. This ensures the stamp's uniqueness and rarity. Step 3: The marketplace provider sells the stamps registered by the NFT registration unit on the marketplace. For example, the generation AI evaluates the market value of the stamps and suggests an appropriate price. Users can sell the stamps based on the suggested price. This allows users to monetize their creativity.

[0081] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0092] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0131] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0137] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0138] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0139] 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.

[0140] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0141] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0142] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0143] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0144] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0145] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0146] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a stamp generator that analyzes a user's text or sketch and generates a stamp; An NFT registration unit that registers the stamp generated by the stamp generation unit as an NFT on a blockchain; a marketplace providing unit that sells the stamps registered by the NFT registration unit on a marketplace. A system characterized by:

2. The stamp generation unit generating the stamp based on an emotion estimated from the text or sketch input by the user; 2. The system of claim 1.

3. The stamp generation unit The stamp reflecting the user's emotion or intention is generated using voice input.

2. The system of claim 1.

4. The NFT registration unit: Using a sentiment estimation function, the emotional value of the stamp is assessed and the scarcity of the NFT is set based on that value.

2. The system of claim 1.

5. The marketplace providing unit, Using the emotion estimation function, a marketing strategy based on the user's emotions is developed to promote sales of the stamps.

2. The system of claim 1.

6. The stamp generation unit Analyze the user's stamp creation history and develop a stamp creation algorithm optimized for each individual user.

2. The system of claim 1.

7. The NFT registration unit: The process of creating the stamp is recorded on the blockchain, making the stamp creation history transparent, further strengthening the authenticity of the stamp.

2. The system of claim 1.

8. The marketplace providing unit, Develop an algorithm that analyzes real-time market data and dynamically adjusts prices when assessing the market value of said stamps.

2. The system of claim 1.

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