system

Abstract

The system according to the embodiment aims to efficiently trade AI usage rights and facilitate fundraising. [Solution] A system according to an embodiment includes a tokenization unit, a publication unit, a trading unit, and a fee income unit. The tokenization unit tokenizes AI usage rights. The publication unit publishes the AI ​​usage rights tokenized by the tokenization unit. The trading unit buys and sells the tokens published by the publication unit. The fee income unit receives income from fees on tokens bought and sold by the trading unit.

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] Previous technology lacked a means to efficiently trade AI usage rights, limiting fundraising opportunities.

[0005] The system according to the embodiment aims to efficiently trade AI usage rights and facilitate fundraising. [Means for solving the problem]

[0006] The system according to the embodiment includes a tokenization unit, a publication unit, a trading unit, and a fee income unit. The tokenization unit tokenizes the AI ​​usage rights. The publication unit publishes the AI ​​usage rights tokenized by the tokenization unit. The trading unit buys and sells the tokens published by the publication unit. The fee income unit receives income from fees on tokens bought and sold by the trading unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently trade AI usage rights and facilitate fundraising. [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 non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) An AI usage rights trading system according to an embodiment of the present invention is a platform where AI usage rights, such as generated AI, can be traded like stocks. This AI usage rights trading system tokenizes AI usage rights and makes them available on the platform. Anyone can buy and sell tokens, and token purchasers have the right to use the AI ​​exclusively. The platform operator receives fees upon purchase and sale. This allows AI companies to raise funds from the market even during the development stage. For example, AI usage rights can be tokenized. Tokenization is the process of representing AI usage rights as digital tokens. This allows AI usage rights to be divided and owned by multiple people. For example, the usage rights of a certain AI can be divided into 100 tokens, each representing 1% of the AI ​​usage rights. The tokenized AI usage rights are then made available on the platform. The platform is a marketplace for buying and selling tokens and is accessible to anyone. Tokens are bought and sold on the platform, and purchasers receive the right to use the AI ​​exclusively. For example, those who purchase tokens can use the AI ​​for a certain period of time. Furthermore, the platform operator receives fees upon token purchase and sale. The fees are set as a fixed percentage of the purchase price. This allows platform operators to earn stable revenues. This system allows AI companies to raise funds from the market even during the development stage. By selling tokenized AI usage rights, they can raise development funds and advance AI development. For example, the funds needed to develop a new AI model can be raised by selling tokens. In this way, by tokenizing AI usage rights and buying and selling them on the platform, AI companies can expand their means of fundraising, and platform operators can earn commission income. In addition, token purchasers can obtain the right to exclusively use AI, which is expected to spread the use of AI. As a result, the AI ​​usage rights trading system allows AI companies to raise funds from the market even during the development stage, and platform operators can earn stable revenues.

[0029] An AI usage right trading system according to an embodiment includes a tokenization unit, a publication unit, a trading unit, and a fee income unit. The tokenization unit tokenizes the AI ​​usage rights. Tokenization refers to representing the AI ​​usage rights as digital tokens. For example, the tokenization unit divides the AI ​​usage rights into 100 tokens, each representing 1% of the AI ​​usage rights. The tokenization unit can generate tokens using blockchain technology. For example, the tokenization unit can generate tokens in formats such as ERC-20 tokens or non-fungible tokens (NFTs). The publication unit publishes the tokenized AI usage rights on a platform. The publication unit provides a market for buying and selling tokens. For example, the publication unit can publish tokens through a web platform or a mobile app. The publication unit can also adjust the timing of token publication. For example, the publication unit can estimate user sentiment and adjust the timing of token publication based on the estimated user sentiment. The trading unit provides a market for buying and selling tokens. The trading unit manages the token buying and selling process and can set the currency used. For example, the trading unit can buy and sell tokens using fiat currency or cryptocurrency. The trading unit can also adjust the timing of buying and selling tokens. For example, the trading unit can estimate user emotions and adjust the timing of buying and selling tokens based on the estimated user emotions. The fee income unit sets a certain percentage of the trading amount as a fee. The fee income unit earns a certain percentage of the trading amount as a fee. For example, the fee income unit can set 5% of the trading amount as a fee. The fee income unit can also adjust the fee setting. For example, the fee income unit can estimate user emotions and adjust the fee setting based on the estimated user emotions. As a result, the AI ​​usage right trading system according to the embodiment can tokenize AI usage rights, make them public, buy and sell them, and earn fee income.

[0030] The tokenization unit can represent the AI ​​usage rights as a digital token. The tokenization unit represents the AI ​​usage rights as a digital token. Digital tokens include, but are not limited to, ERC-20 tokens and non-fungible tokens (NFTs). The tokenization unit can generate tokens using blockchain technology. For example, the tokenization unit generates ERC-20 tokens to represent the AI ​​usage rights in a divided form. The tokenization unit can also generate NFTs to represent specific AI usage rights as unique tokens. For example, the tokenization unit divides the AI ​​usage rights into 100 tokens, each representing 1% of the AI ​​usage rights. This makes it possible to divide the AI ​​usage rights by representing them as digital tokens. Some or all of the above-described processing in the tokenization unit may be performed using AI, for example, or without AI. For example, the tokenization unit can generate tokens using AI when tokenizing the AI ​​usage rights.

[0031] The publishing unit can publish the tokenized AI usage right on a platform. The publishing unit publishes the tokenized AI usage right on a platform. Platforms include, but are not limited to, web platforms and mobile apps. The publishing unit can adjust the timing of token publication. For example, the publishing unit can estimate a user's emotions and adjust the timing of token publication based on the estimated user emotions. The publishing unit can also select a method of token publication. For example, the publishing unit can select an optimal publication method taking into account user attribute information. By publishing the tokenized AI usage right on the platform, anyone can access it. Some or all of the above-described processing in the publishing unit may be performed using, for example, AI, or may be performed without using AI. For example, when adjusting the timing of token publication, the publishing unit can estimate a user's emotions using AI and adjust the timing of token publication.

[0032] The trading unit can provide a market for buying and selling tokens. The trading unit provides a market for buying and selling tokens. Markets include, but are not limited to, centralized markets and decentralized markets. The trading unit can manage the token buying and selling process and set the currency to be used. For example, the trading unit can buy and sell tokens using fiat currency or cryptocurrency. The trading unit can adjust the timing of token buying and selling. For example, the trading unit can estimate user sentiment and adjust the timing of token buying and selling based on the estimated user sentiment. This enables token buying and selling by providing a market for token buying and selling. Some or all of the above-described processing in the trading unit may be performed using, for example, AI, or may be performed without using AI. For example, when adjusting the timing of token buying and selling, the trading unit can estimate user sentiment using AI and adjust the timing of token buying and selling.

[0033] The commission income unit can set a certain percentage of the trading amount as the commission. The commission income unit sets a certain percentage of the trading amount as the commission. The commission income unit earns a certain percentage of the trading amount as the commission. For example, the commission income unit can set 5% of the trading amount as the commission. The commission income unit can adjust the commission setting. For example, the commission income unit can estimate a user's emotions and adjust the commission setting based on the estimated user's emotions. In this way, by setting a certain percentage of the trading amount as the commission, stable profits can be obtained. Some or all of the above-described processing in the commission income unit may be performed using, for example, AI, or may be performed without using AI. For example, when adjusting the commission setting, the commission income unit can estimate a user's emotions using AI and adjust the commission setting.

[0034] The tokenization unit can divide the AI ​​usage rights so that they can be owned by multiple people. The tokenization unit can divide the AI ​​usage rights so that they can be owned by multiple people. For example, division can include, but is not limited to, dividing the AI ​​usage rights into 100 tokens, with each token representing 1% of the AI ​​usage rights. The tokenization unit can generate tokens using blockchain technology to represent the divided usage rights. For example, the tokenization unit can generate ERC-20 tokens to represent the divided AI usage rights. The tokenization unit can also generate NFTs to represent specific AI usage rights as unique tokens. This allows the AI ​​usage rights to be divided and owned by multiple people. Some or all of the above-mentioned processing in the tokenization unit can be performed, for example, using AI, or can be performed without using AI. For example, when dividing the AI ​​usage rights, the tokenization unit can generate tokens using AI.

[0035] The disclosure unit can provide a platform accessible to anyone. The disclosure unit provides a platform accessible to anyone. Examples of platforms include, but are not limited to, web platforms and mobile apps. The disclosure unit can adjust the timing of token disclosure. For example, the disclosure unit can estimate user sentiment and adjust the timing of token disclosure based on the estimated user sentiment. The disclosure unit can also select a method of token disclosure. For example, the disclosure unit can select an optimal disclosure method taking into account user attribute information. This provides a platform accessible to anyone, thereby expanding token trading. Some or all of the above-described processing in the disclosure unit may be performed using, for example, AI, or may be performed without using AI. For example, when adjusting the timing of token disclosure, the disclosure unit can estimate user sentiment using AI and adjust the timing of token disclosure.

[0036] The trading unit can enable token purchasers to obtain the right to exclusively use the AI. The trading unit can enable token purchasers to obtain the right to exclusively use the AI. The right to exclusively use the AI ​​includes, but is not limited to, the usage period and the scope of use. The trading unit can manage the token trading process and set the currency to be used. For example, the trading unit can buy and sell tokens using fiat currency or cryptocurrency. The trading unit can adjust the timing of token buying and selling. For example, the trading unit can estimate user emotions and adjust the timing of token buying and selling based on the estimated user emotions. This allows token purchasers to obtain the right to exclusively use the AI. Some or all of the above-mentioned processing in the trading unit can be performed using, for example, AI, or can be performed without using AI. For example, when adjusting the timing of token buying and selling, the trading unit can use AI to estimate user emotions and adjust the timing of token buying and selling.

[0037] The commission income unit can enable stable profits to be obtained. The commission income unit enables stable profits to be obtained. The commission income unit earns a certain percentage of the trading amount as a commission. For example, the commission income unit can set the commission at 5% of the trading amount. The commission income unit can adjust the commission setting. For example, the commission income unit can estimate the user's emotions and adjust the commission setting based on the estimated user's emotions. This allows stable profits to be obtained. Some or all of the above-mentioned processing in the commission income unit may be performed using, for example, AI, or may be performed without using AI. For example, the commission income unit can estimate the user's emotions and adjust the commission setting when adjusting the commission setting.

[0038] When tokenizing the AI ​​usage rights, the tokenization unit can evaluate the value of the usage rights in real time and dynamically set the value of the tokens. When tokenizing the AI ​​usage rights, the tokenization unit can evaluate the value of the usage rights in real time and dynamically set the value of the tokens. Real-time evaluation includes, but is not limited to, analysis of market demand and supply, evaluation of AI performance and frequency of use, etc. The tokenization unit can evaluate the value of the usage rights in real time using an evaluation algorithm. For example, the tokenization unit analyzes market demand and supply in real time and sets the value of the tokens. The tokenization unit can also dynamically adjust the value of the tokens based on the AI ​​performance and frequency of use. Furthermore, the tokenization unit can set an appropriate value by referring to the token values ​​of competitors. In this way, by evaluating the value of the usage rights in real time and dynamically setting the value of the tokens, appropriate value can be provided. Some or all of the above-mentioned processing in the tokenization unit may be performed, for example, using AI or without using AI. For example, the tokenization department can use AI to analyze market supply and demand and set the value of tokens when assessing the value of usage rights in real time.

[0039] The tokenization unit can subdivide the AI's usage rights during tokenization and issue different tokens according to specific functions or periods. The tokenization unit subdivides the AI's usage rights during tokenization and issue different tokens according to specific functions or periods. Subdivision includes, but is not limited to, issuing tokens according to specific AI functions (e.g., image recognition, voice recognition) or usage periods (e.g., one month, three months, one year). The tokenization unit can set subdivision units and standards and issue tokens according to user needs. For example, the tokenization unit issues different tokens according to specific AI functions. The tokenization unit can also issue different tokens according to usage periods. Furthermore, the tokenization unit can issue tokens customized according to user needs. By subdividing the AI's usage rights and issuing different tokens according to specific functions or periods, tokens according to user needs can be provided. Some or all of the above-described processing in the tokenization unit may be performed, for example, using AI or without AI. For example, when subdividing AI usage rights, the tokenization unit can use AI to issue tokens according to specific functions or periods.

[0040] During tokenization, the tokenization unit can divide the AI ​​usage rights by region and issue tokens according to the demand in each region. During tokenization, the tokenization unit can divide the AI ​​usage rights by region and issue tokens according to the demand in each region. Regions include, but are not limited to, the economic situation and demand assessment of a country or region. The tokenization unit can conduct market research for each region and issue tokens according to the demand. For example, the tokenization unit can issue tokens for regions with high demand based on the market research for each region. The tokenization unit can also set the value of tokens taking into account the economic situation in each region. Furthermore, the tokenization unit can analyze the competitive situation in each region and issue appropriate tokens. As a result, by dividing the AI ​​usage rights by region and issuing tokens according to the demand in each region, tokens can be provided according to the needs of each region. Some or all of the above-mentioned processing in the tokenization unit may be performed using, for example, AI, or may be performed without using AI. For example, when evaluating the demand in each region, the tokenization unit can conduct market research using AI and issue tokens.

[0041] During tokenization, the tokenization unit can customize the AI ​​usage rights for different industries and uses and issue tokens for specific industries. During tokenization, the tokenization unit can customize the AI ​​usage rights for different industries and uses and issue tokens for specific industries. Industries include, but are not limited to, the medical industry, education, and manufacturing. The tokenization unit can evaluate the needs of each industry and issue customized tokens. For example, the tokenization unit can issue tokens for AI usage rights specialized for the medical industry. The tokenization unit can also issue tokens for AI usage rights specialized for the education industry. Furthermore, the tokenization unit can issue tokens for AI usage rights specialized for the manufacturing industry. This allows the AI ​​usage rights to be customized for different industries and uses and tokens for specific industries to be issued, thereby providing tokens that meet the needs of each industry. Some or all of the above-described processing by the tokenization unit may be performed using, or without, AI. For example, when evaluating the needs of each industry, the tokenization unit can conduct market research using AI and issue tokens.

[0042] When disclosing the tokenized AI usage right, the disclosure unit can select the optimal disclosure method by taking into account the user's attribute information. When disclosing the tokenized AI usage right, the disclosure unit can select the optimal disclosure method by taking into account the user's attribute information. Attribute information includes, but is not limited to, age, gender, occupation, and interests. The disclosure unit can collect the user's attribute information and select the optimal disclosure method. For example, the disclosure unit can select an appropriate disclosure method according to the user's age group. The disclosure unit can also select an appropriate disclosure method according to the user's occupation. Furthermore, the disclosure unit can select an appropriate disclosure method according to the user's interests. In this way, by selecting the optimal disclosure method by taking into account the user's attribute information, it is possible to provide a disclosure method that is suitable for the user. Some or all of the above-mentioned processing in the disclosure unit may be performed, for example, using AI, or may be performed without using AI. For example, when collecting the user's attribute information, the disclosure unit can use AI to collect the information and select the optimal disclosure method.

[0043] The publishing unit can dynamically set the public offering price by referring to past transaction data when the token is publicly offered. The publishing unit can dynamically set the public offering price by referring to past transaction data when the token is publicly offered. Past transaction data includes, but is not limited to, transaction history, transaction volume, and transaction price. The publishing unit can analyze past transaction data and set an optimal public offering price. For example, the publishing unit can set the public offering price based on the past transaction data, taking into account the balance between supply and demand. The publishing unit can also set the public offering price based on the past transaction data, taking into account the prices of competitors. In this way, by dynamically setting the public offering price by referring to the past transaction data, an appropriate public offering price can be provided. Some or all of the above-described processing in the publishing unit may be performed, for example, using AI, or may be performed without using AI. For example, when analyzing past transaction data, the publishing unit can use AI to analyze the data and set the public offering price.

[0044] The publishing unit may provide different publishing methods for each region, taking into account the user's geographical location information, when publishing tokens. The publishing unit may provide different publishing methods for each region, taking into account the user's geographical location information, when publishing tokens. Examples of geographical location information include, but are not limited to, GPS data and address information. The publishing unit may conduct market research for each region and provide an appropriate publishing method. For example, the publishing unit may publish tokens in regions with high demand based on the market research for each region. The publishing unit may also adjust the publishing method taking into account the economic situation for each region. Furthermore, the publishing unit may analyze the competitive situation for each region and provide an appropriate publishing method. By providing different publishing methods for each region taking into account the user's geographical location information, it is possible to provide a publishing method that meets the needs of each region. Some or all of the above-described processing in the publishing unit may be performed using, for example, AI, or may be performed without using AI. For example, the publishing unit may use AI to collect geographical location information and provide different publishing methods for each region.

[0045] When publishing a token, the publishing unit may provide different publishing methods for each industry, taking into account the user's industry information. When publishing a token, the publishing unit may provide different publishing methods for each industry, taking into account the user's industry information. Industry information includes, but is not limited to, the medical industry, education industry, and manufacturing industry. The publishing unit may evaluate the needs of each industry and provide an appropriate publishing method. For example, the publishing unit may provide a publishing method specialized for the medical industry. The publishing unit may also provide a publishing method specialized for the education industry. Furthermore, the publishing unit may provide a publishing method specialized for the manufacturing industry. By providing different publishing methods for each industry, taking into account the user's industry information, it is possible to provide a publishing method that meets the needs of each industry. Some or all of the above-described processing in the publishing unit may be performed using, for example, AI, or may be performed without using AI. For example, when collecting industry information, the publishing unit may use AI to collect the information and provide different publishing methods for each industry.

[0046] The trading unit can analyze past transaction history and present optimal buying and selling prices when buying and selling tokens. The trading unit analyzes past transaction history and presents optimal buying and selling prices when buying and selling tokens. Transaction history includes, but is not limited to, transaction date and time, transaction volume, and transaction price. The trading unit can analyze past transaction history and set optimal buying and selling prices. For example, the trading unit sets buying and selling prices based on past transaction history, taking into account the balance between supply and demand. The trading unit can also set buying and selling prices based on past transaction history, taking competitors' prices into consideration. In this way, by analyzing past transaction history and presenting optimal buying and selling prices, appropriate buying and selling prices can be provided. Some or all of the above-described processing in the trading unit may be performed using, for example, AI, or may be performed without using AI. For example, when analyzing past transaction history, the trading unit can use AI to analyze data and set buying and selling prices.

[0047] The trading unit can provide customized trading options taking into account the user's attribute information when trading tokens. The trading unit can provide customized trading options taking into account the user's attribute information when trading tokens. Attribute information includes, but is not limited to, age, gender, occupation, and interests. The trading unit can collect the user's attribute information and provide customized trading options. For example, the trading unit can provide appropriate trading options depending on the user's age group. The trading unit can also provide appropriate trading options depending on the user's occupation. Furthermore, the trading unit can provide appropriate trading options depending on the user's interests. This allows the trading unit to provide customized trading options that are suitable for the user by taking into account the user's attribute information. Some or all of the above-described processing in the trading unit can be performed using, for example, AI, or without AI. For example, the trading unit can use AI to collect the user's attribute information and provide customized trading options.

[0048] The trading unit may provide different buying and selling options for each region by taking into account the user's geographical location information when buying and selling tokens. The trading unit may provide different buying and selling options for each region by taking into account the user's geographical location information when buying and selling tokens. Examples of geographical location information include, but are not limited to, GPS data and address information. The trading unit may conduct market research for each region and provide appropriate buying and selling options. For example, the trading unit may provide buying and selling options for regions with high demand based on the market research for each region. The trading unit may also adjust the buying and selling options by taking into account the economic conditions of each region. Furthermore, the trading unit may analyze the competitive situation for each region and provide appropriate buying and selling options. By providing different buying and selling options for each region by taking into account the user's geographical location information, it is possible to provide buying and selling options tailored to the needs of each region. Some or all of the above-described processing in the trading unit may be performed using, for example, AI, or may be performed without using AI. For example, the trading unit may use AI to collect geographical location information and provide different buying and selling options for each region.

[0049] When buying and selling tokens, the trading unit can provide different buying and selling options for each industry, taking into account the user's industry information. When buying and selling tokens, the trading unit can provide different buying and selling options for each industry, taking into account the user's industry information. Industry information includes, but is not limited to, the medical industry, the education industry, and the manufacturing industry. The trading unit can evaluate the needs of each industry and provide appropriate buying and selling options. For example, the trading unit can provide buying and selling options specialized for the medical industry. The trading unit can also provide buying and selling options specialized for the education industry. Furthermore, the trading unit can provide buying and selling options specialized for the manufacturing industry. In this way, by providing different buying and selling options for each industry, taking into account the user's industry information, it is possible to provide buying and selling options that meet the needs of each industry. Some or all of the above-mentioned processing in the trading unit may be performed, for example, using AI, or may be performed without using AI. For example, when collecting industry information, the trading unit can use AI to collect the information and provide different buying and selling options for each industry.

[0050] The commission income unit can dynamically set an optimal commission rate by analyzing past transaction data. The commission income unit can dynamically set an optimal commission rate by analyzing past transaction data. Transaction data includes, but is not limited to, transaction history, transaction volume, and transaction price. The commission income unit can analyze past transaction data and set an optimal commission rate. For example, the commission income unit can set a commission rate based on past transaction data, taking into account the balance between supply and demand. The commission income unit can also set a commission rate based on past transaction data, taking into account the commission rates of competitors. In this way, by analyzing past transaction data and dynamically setting an optimal commission rate, an appropriate commission rate can be provided. Some or all of the above-described processing in the commission income unit may be performed using, for example, AI, or may be performed without using AI. For example, when analyzing past transaction data, the commission income unit can use AI to analyze the data and set a commission rate.

[0051] The commission income unit can set different commission rates depending on the size and frequency of transactions. The commission income unit sets different commission rates depending on the size and frequency of transactions. Examples of the size and frequency of transactions include, but are not limited to, the transaction volume and the number of transactions. The commission income unit can evaluate the size and frequency of transactions and set an appropriate commission rate. For example, the commission income unit can set a lower commission rate for large transactions. The commission income unit can also set a discounted commission rate for frequent transactions. Furthermore, the commission income unit can set a normal commission rate for small transactions. In this way, by setting different commission rates depending on the size and frequency of transactions, it is possible to provide a commission rate that is appropriate for the user. Some or all of the above-mentioned processing in the commission income unit may be performed, for example, using AI, or may be performed without using AI. For example, when evaluating the size and frequency of transactions, the commission income unit can use AI to analyze data and set a commission rate.

[0052] The commission income department can set different commission rates for each region and earn commission income according to the demand in each region. The commission income department can set different commission rates for each region and earn commission income according to the demand in each region. For example, the region can be, but is not limited to, the economic situation or demand assessment of a country or region. The commission income department can conduct market research for each region and set commission rates according to the demand. For example, the commission income department can set commission rates for regions with high demand based on the market research for each region. The commission income department can also set commission rates taking into account the economic situation in each region. Furthermore, the commission income department can analyze the competitive situation in each region and set appropriate commission rates. By setting different commission rates for each region, it is possible to earn commission income according to the needs of each region. Some or all of the above-described processing in the commission income department can be performed using, for example, AI, or without AI. For example, the commission income department can conduct market research using AI to evaluate the demand in each region and set commission rates.

[0053] The commission income department can set different commission rates for each industry and earn commission income according to the demand of each industry. The commission income department can set different commission rates for each industry and earn commission income according to the demand of each industry. Examples of different industries include, but are not limited to, the medical industry, the education industry, and the manufacturing industry. The commission income department can conduct market research for each industry and set commission rates according to the demand. For example, the commission income department can set commission rates for industries with high demand based on the market research for each industry. The commission income department can also set commission rates taking into account the economic situation of each industry. Furthermore, the commission income department can analyze the competitive situation for each industry and set appropriate commission rates. By setting different commission rates for each industry, it is possible to earn commission income according to the needs of each industry. Some or all of the above-described processing in the commission income department can be performed using, for example, AI, or can be performed without AI. For example, the commission income department can conduct market research using AI to evaluate the demand of each industry and set commission rates.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When tokenizing AI usage rights, the tokenization unit can adjust the value of the token taking into account the user's past usage history. For example, it can discount the value of the token for users with a large usage record in the past. It can also set a higher token value for users who use the service infrequently. Furthermore, it can provide customized token values ​​to users with specific usage patterns. This makes it possible to improve user satisfaction by adjusting the value of the token based on the user's past usage history.

[0056] The buying and selling unit can analyze a user's purchase history when buying and selling tokens to provide customized buying and selling options. For example, a special discount option can be provided to a user who has purchased high-value tokens in the past. Also, a user who frequently purchases tokens in a specific category can be provided with options specialized for that category. Furthermore, recommended tokens can be presented based on the user's purchase history. This makes it possible to improve user satisfaction by providing customized buying and selling options based on the user's purchase history.

[0057] The commission income unit can adjust the commission rate by analyzing the user's payment history when buying and selling tokens. For example, a lower commission rate can be set for a user who has made prompt payments in the past. A higher commission rate can also be set for a user who has a high rate of late payments. Furthermore, customized commission rates can be provided to users with specific payment patterns. This makes it possible to improve user satisfaction by adjusting the commission rate based on the user's payment history.

[0058] When releasing tokens, the release unit can provide different release methods for each region, taking into account the user's geographic location information. For example, if demand is high in a particular region, a release method specialized for that region can be provided. The release price can also be adjusted taking into account the economic situation of each region. Furthermore, a release method tailored to the culture and customs of each region can be provided. This makes it possible to release tokens according to the needs of each region by providing different release methods for each region based on the user's geographic location information.

[0059] The commission income unit can adjust the commission rate when buying and selling tokens, taking into account the user's trading frequency. For example, it can set a lower commission rate for users who trade frequently. It can also set a higher commission rate for users who trade less frequently. Furthermore, it can provide customized commission rates to users with specific trading patterns. This allows for adjusting the commission rate based on the user's trading frequency, thereby improving user satisfaction.

[0060] When publishing tokens, the publishing unit can analyze the user's purchase history and provide the optimal publishing method. For example, a special publishing method can be provided to a user who has purchased high-value tokens in the past. Also, a publishing method specialized for a specific category can be provided to a user who frequently purchases tokens in that category. Furthermore, recommended tokens can be presented based on the user's purchase history. This makes it possible to improve user satisfaction by providing the optimal publishing method based on the user's purchase history.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The tokenization unit tokenizes the AI ​​usage rights. Tokenization is the process of representing the AI ​​usage rights as digital tokens. For example, the tokenization unit divides the AI ​​usage rights into 100 tokens, with each token representing 1% of the AI ​​usage rights. The tokenization unit can generate tokens using blockchain technology. For example, the tokenization unit can generate tokens in the form of ERC-20 tokens or NFTs (Non-Fungible Tokens). Step 2: The public department makes the tokenized AI usage rights available on the platform. The public department provides a marketplace for buying and selling tokens. For example, the public department can make the tokens available through a web platform or mobile app. The public department can also adjust the timing of token release. For example, the public department can estimate user sentiment and adjust the timing of token release based on the estimated user sentiment. Step 3: The trading unit provides a market for buying and selling tokens. The trading unit manages the token buying and selling process and can set the currency to be used. For example, the trading unit can buy and sell tokens using fiat currency or cryptocurrency. The trading unit can also adjust the timing of token buying and selling. For example, the trading unit can estimate user sentiment and adjust the timing of token buying and selling based on the estimated user sentiment. Step 4: The commission income unit sets a certain percentage of the trading amount as a commission. The commission income unit sets a certain percentage of the trading amount as a commission. For example, the commission income unit can set 5% of the trading amount as a commission. The commission income unit can also adjust the commission setting. For example, the commission income unit can estimate the user's emotions and adjust the commission setting based on the estimated user's emotions.

[0063] (Example 2) An AI usage rights trading system according to an embodiment of the present invention is a platform where AI usage rights, such as generated AI, can be traded like stocks. This AI usage rights trading system tokenizes AI usage rights and makes them available on the platform. Anyone can buy and sell tokens, and token purchasers have the right to use the AI ​​exclusively. The platform operator receives fees upon purchase and sale. This allows AI companies to raise funds from the market even during the development stage. For example, AI usage rights can be tokenized. Tokenization is the process of representing AI usage rights as digital tokens. This allows AI usage rights to be divided and owned by multiple people. For example, the usage rights of a certain AI can be divided into 100 tokens, each representing 1% of the AI ​​usage rights. The tokenized AI usage rights are then made available on the platform. The platform is a marketplace for buying and selling tokens and is accessible to anyone. Tokens are bought and sold on the platform, and purchasers receive the right to use the AI ​​exclusively. For example, those who purchase tokens can use the AI ​​for a certain period of time. Furthermore, the platform operator receives fees upon token purchase and sale. The fees are set as a fixed percentage of the purchase price. This allows platform operators to earn stable revenues. This system allows AI companies to raise funds from the market even during the development stage. By selling tokenized AI usage rights, they can raise development funds and advance AI development. For example, the funds needed to develop a new AI model can be raised by selling tokens. In this way, by tokenizing AI usage rights and buying and selling them on the platform, AI companies can expand their means of fundraising, and platform operators can earn commission income. In addition, token purchasers can obtain the right to exclusively use AI, which is expected to spread the use of AI. As a result, the AI ​​usage rights trading system allows AI companies to raise funds from the market even during the development stage, and platform operators can earn stable revenues.

[0064] An AI usage right trading system according to an embodiment includes a tokenization unit, a publication unit, a trading unit, and a fee income unit. The tokenization unit tokenizes the AI ​​usage rights. Tokenization refers to representing the AI ​​usage rights as digital tokens. For example, the tokenization unit divides the AI ​​usage rights into 100 tokens, each representing 1% of the AI ​​usage rights. The tokenization unit can generate tokens using blockchain technology. For example, the tokenization unit can generate tokens in formats such as ERC-20 tokens or non-fungible tokens (NFTs). The publication unit publishes the tokenized AI usage rights on a platform. The publication unit provides a market for buying and selling tokens. For example, the publication unit can publish tokens through a web platform or a mobile app. The publication unit can also adjust the timing of token publication. For example, the publication unit can estimate user sentiment and adjust the timing of token publication based on the estimated user sentiment. The trading unit provides a market for buying and selling tokens. The trading unit manages the token buying and selling process and can set the currency used. For example, the trading unit can buy and sell tokens using fiat currency or cryptocurrency. The trading unit can also adjust the timing of buying and selling tokens. For example, the trading unit can estimate user emotions and adjust the timing of buying and selling tokens based on the estimated user emotions. The fee income unit sets a certain percentage of the trading amount as a fee. The fee income unit earns a certain percentage of the trading amount as a fee. For example, the fee income unit can set 5% of the trading amount as a fee. The fee income unit can also adjust the fee setting. For example, the fee income unit can estimate user emotions and adjust the fee setting based on the estimated user emotions. As a result, the AI ​​usage right trading system according to the embodiment can tokenize AI usage rights, make them public, buy and sell them, and earn fee income.

[0065] The tokenization unit can represent the AI ​​usage rights as a digital token. The tokenization unit represents the AI ​​usage rights as a digital token. Digital tokens include, but are not limited to, ERC-20 tokens and non-fungible tokens (NFTs). The tokenization unit can generate tokens using blockchain technology. For example, the tokenization unit generates ERC-20 tokens to represent the AI ​​usage rights in a divided form. The tokenization unit can also generate NFTs to represent specific AI usage rights as unique tokens. For example, the tokenization unit divides the AI ​​usage rights into 100 tokens, each representing 1% of the AI ​​usage rights. This makes it possible to divide the AI ​​usage rights by representing them as digital tokens. Some or all of the above-described processing in the tokenization unit may be performed using AI, for example, or without AI. For example, the tokenization unit can generate tokens using AI when tokenizing the AI ​​usage rights.

[0066] The publishing unit can publish the tokenized AI usage right on a platform. The publishing unit publishes the tokenized AI usage right on a platform. Platforms include, but are not limited to, web platforms and mobile apps. The publishing unit can adjust the timing of token publication. For example, the publishing unit can estimate a user's emotions and adjust the timing of token publication based on the estimated user emotions. The publishing unit can also select a method of token publication. For example, the publishing unit can select an optimal publication method taking into account user attribute information. By publishing the tokenized AI usage right on the platform, anyone can access it. Some or all of the above-described processing in the publishing unit may be performed using, for example, AI, or may be performed without using AI. For example, when adjusting the timing of token publication, the publishing unit can estimate a user's emotions using AI and adjust the timing of token publication.

[0067] The trading unit can provide a market for buying and selling tokens. The trading unit provides a market for buying and selling tokens. Markets include, but are not limited to, centralized markets and decentralized markets. The trading unit can manage the token buying and selling process and set the currency to be used. For example, the trading unit can buy and sell tokens using fiat currency or cryptocurrency. The trading unit can adjust the timing of token buying and selling. For example, the trading unit can estimate user sentiment and adjust the timing of token buying and selling based on the estimated user sentiment. This enables token buying and selling by providing a market for token buying and selling. Some or all of the above-described processing in the trading unit may be performed using, for example, AI, or may be performed without using AI. For example, when adjusting the timing of token buying and selling, the trading unit can estimate user sentiment using AI and adjust the timing of token buying and selling.

[0068] The commission income unit can set a certain percentage of the trading amount as the commission. The commission income unit sets a certain percentage of the trading amount as the commission. The commission income unit earns a certain percentage of the trading amount as the commission. For example, the commission income unit can set 5% of the trading amount as the commission. The commission income unit can adjust the commission setting. For example, the commission income unit can estimate a user's emotions and adjust the commission setting based on the estimated user's emotions. In this way, by setting a certain percentage of the trading amount as the commission, stable profits can be obtained. Some or all of the above-described processing in the commission income unit may be performed using, for example, AI, or may be performed without using AI. For example, when adjusting the commission setting, the commission income unit can estimate a user's emotions using AI and adjust the commission setting.

[0069] The tokenization unit can divide the AI ​​usage rights so that they can be owned by multiple people. The tokenization unit can divide the AI ​​usage rights so that they can be owned by multiple people. For example, division can include, but is not limited to, dividing the AI ​​usage rights into 100 tokens, with each token representing 1% of the AI ​​usage rights. The tokenization unit can generate tokens using blockchain technology to represent the divided usage rights. For example, the tokenization unit can generate ERC-20 tokens to represent the divided AI usage rights. The tokenization unit can also generate NFTs to represent specific AI usage rights as unique tokens. This allows the AI ​​usage rights to be divided and owned by multiple people. Some or all of the above-mentioned processing in the tokenization unit can be performed, for example, using AI, or can be performed without using AI. For example, when dividing the AI ​​usage rights, the tokenization unit can generate tokens using AI.

[0070] The disclosure unit can provide a platform accessible to anyone. The disclosure unit provides a platform accessible to anyone. Examples of platforms include, but are not limited to, web platforms and mobile apps. The disclosure unit can adjust the timing of token disclosure. For example, the disclosure unit can estimate user sentiment and adjust the timing of token disclosure based on the estimated user sentiment. The disclosure unit can also select a method of token disclosure. For example, the disclosure unit can select an optimal disclosure method taking into account user attribute information. This provides a platform accessible to anyone, thereby expanding token trading. Some or all of the above-described processing in the disclosure unit may be performed using, for example, AI, or may be performed without using AI. For example, when adjusting the timing of token disclosure, the disclosure unit can estimate user sentiment using AI and adjust the timing of token disclosure.

[0071] The trading unit can enable token purchasers to obtain the right to exclusively use the AI. The trading unit can enable token purchasers to obtain the right to exclusively use the AI. The right to exclusively use the AI ​​includes, but is not limited to, the usage period and the scope of use. The trading unit can manage the token trading process and set the currency to be used. For example, the trading unit can buy and sell tokens using fiat currency or cryptocurrency. The trading unit can adjust the timing of token buying and selling. For example, the trading unit can estimate user emotions and adjust the timing of token buying and selling based on the estimated user emotions. This allows token purchasers to obtain the right to exclusively use the AI. Some or all of the above-mentioned processing in the trading unit can be performed using, for example, AI, or can be performed without using AI. For example, when adjusting the timing of token buying and selling, the trading unit can use AI to estimate user emotions and adjust the timing of token buying and selling.

[0072] The commission income unit can enable stable profits to be obtained. The commission income unit enables stable profits to be obtained. The commission income unit earns a certain percentage of the trading amount as a commission. For example, the commission income unit can set the commission at 5% of the trading amount. The commission income unit can adjust the commission setting. For example, the commission income unit can estimate the user's emotions and adjust the commission setting based on the estimated user's emotions. This allows stable profits to be obtained. Some or all of the above-mentioned processing in the commission income unit may be performed using, for example, AI, or may be performed without using AI. For example, the commission income unit can estimate the user's emotions and adjust the commission setting when adjusting the commission setting.

[0073] The tokenization unit can estimate a user's emotion and adjust the timing of tokenization based on the estimated user's emotion. The tokenization unit can estimate a user's emotion and adjust the timing of tokenization based on the estimated user's emotion. Examples of user emotions include, but are not limited to, excitement, anxiety, and relaxation. The tokenization unit can estimate a user's emotion using an emotion analysis algorithm. For example, the tokenization unit analyzes the user's facial expressions and voice data to estimate the emotion. The tokenization unit adjusts the timing of tokenization based on the estimated emotion. For example, if the user is excited, the tokenization unit can advance the timing of tokenization to provide immediate access rights. Also, if the user is anxious, the tokenization unit can delay the timing of tokenization to provide a detailed explanation. Furthermore, if the user is relaxed, the tokenization unit can perform tokenization at a normal timing. This improves user satisfaction by adjusting the timing of tokenization according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the tokenizer may be performed using, for example, AI, or may be performed without using AI. For example, when estimating a user's emotion, the tokenizer may estimate the emotion using AI and adjust the timing of tokenization.

[0074] When tokenizing the AI ​​usage rights, the tokenization unit can evaluate the value of the usage rights in real time and dynamically set the value of the tokens. When tokenizing the AI ​​usage rights, the tokenization unit can evaluate the value of the usage rights in real time and dynamically set the value of the tokens. Real-time evaluation includes, but is not limited to, analysis of market demand and supply, evaluation of AI performance and frequency of use, etc. The tokenization unit can evaluate the value of the usage rights in real time using an evaluation algorithm. For example, the tokenization unit analyzes market demand and supply in real time and sets the value of the tokens. The tokenization unit can also dynamically adjust the value of the tokens based on the AI ​​performance and frequency of use. Furthermore, the tokenization unit can set an appropriate value by referring to the token values ​​of competitors. In this way, by evaluating the value of the usage rights in real time and dynamically setting the value of the tokens, appropriate value can be provided. Some or all of the above-mentioned processing in the tokenization unit may be performed, for example, using AI or without using AI. For example, the tokenization department can use AI to analyze market supply and demand and set the value of tokens when assessing the value of usage rights in real time.

[0075] The tokenization unit can subdivide the AI's usage rights during tokenization and issue different tokens according to specific functions or periods. The tokenization unit subdivides the AI's usage rights during tokenization and issue different tokens according to specific functions or periods. Subdivision includes, but is not limited to, issuing tokens according to specific AI functions (e.g., image recognition, voice recognition) or usage periods (e.g., one month, three months, one year). The tokenization unit can set subdivision units and standards and issue tokens according to user needs. For example, the tokenization unit issues different tokens according to specific AI functions. The tokenization unit can also issue different tokens according to usage periods. Furthermore, the tokenization unit can issue tokens customized according to user needs. By subdividing the AI's usage rights and issuing different tokens according to specific functions or periods, tokens according to user needs can be provided. Some or all of the above-described processing in the tokenization unit may be performed, for example, using AI, or may be performed without using AI. For example, when subdividing AI usage rights, the tokenization unit can use AI to issue tokens according to specific functions or periods.

[0076] The tokenization unit can estimate a user's emotions and prioritize the AIs to be tokenized based on the estimated user emotions. The tokenization unit can estimate a user's emotions and prioritize the AIs to be tokenized based on the estimated user emotions. User emotions include, but are not limited to, excitement, anxiety, and relaxation. The tokenization unit can estimate a user's emotions using an emotion analysis algorithm. For example, the tokenization unit analyzes the user's facial expressions and voice data to estimate the emotion. The tokenization unit prioritizes the AIs to be tokenized based on the estimated emotion. For example, if a user is excited, the tokenization unit prioritizes the use of popular AIs. Also, if a user is anxious, the tokenization unit can prioritize the use of highly reliable AIs. Furthermore, if a user is relaxed, the tokenization unit can prioritize the use of AIs to be tokenized based on the user's emotions. This improves user satisfaction by prioritizing the AIs to be tokenized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the tokenizer may be performed using, for example, AI, or may be performed without using AI. For example, when estimating a user's emotion, the tokenizer may use AI to estimate the emotion and determine the priority of the AI ​​to be used for tokenization.

[0077] During tokenization, the tokenization unit can divide the AI ​​usage rights by region and issue tokens according to the demand in each region. During tokenization, the tokenization unit can divide the AI ​​usage rights by region and issue tokens according to the demand in each region. Regions include, but are not limited to, the economic situation and demand assessment of a country or region. The tokenization unit can conduct market research for each region and issue tokens according to the demand. For example, the tokenization unit can issue tokens for regions with high demand based on the market research for each region. The tokenization unit can also set the value of tokens taking into account the economic situation in each region. Furthermore, the tokenization unit can analyze the competitive situation in each region and issue appropriate tokens. As a result, by dividing the AI ​​usage rights by region and issuing tokens according to the demand in each region, tokens can be provided according to the needs of each region. Some or all of the above-mentioned processing in the tokenization unit may be performed using, for example, AI, or may be performed without using AI. For example, when evaluating the demand in each region, the tokenization unit can conduct market research using AI and issue tokens.

[0078] During tokenization, the tokenization unit can customize the AI ​​usage rights for different industries and uses and issue tokens for specific industries. During tokenization, the tokenization unit can customize the AI ​​usage rights for different industries and uses and issue tokens for specific industries. Industries include, but are not limited to, the medical industry, education, and manufacturing. The tokenization unit can evaluate the needs of each industry and issue customized tokens. For example, the tokenization unit can issue tokens for AI usage rights specialized for the medical industry. The tokenization unit can also issue tokens for AI usage rights specialized for the education industry. Furthermore, the tokenization unit can issue tokens for AI usage rights specialized for the manufacturing industry. This allows the AI ​​usage rights to be customized for different industries and uses and tokens for specific industries to be issued, thereby providing tokens that meet the needs of each industry. Some or all of the above-described processing by the tokenization unit may be performed using, or without, AI. For example, when evaluating the needs of each industry, the tokenization unit can conduct market research using AI and issue tokens.

[0079] The disclosure unit can estimate the user's emotion and adjust the timing of token disclosure based on the estimated user's emotion. The disclosure unit can estimate the user's emotion and adjust the timing of token disclosure based on the estimated user's emotion. User emotions include, but are not limited to, excitement, anxiety, and relaxation. The disclosure unit can estimate the user's emotion using an emotion analysis algorithm. For example, the disclosure unit can analyze the user's facial expressions and voice data to estimate the emotion. The disclosure unit can adjust the timing of token disclosure based on the estimated emotion. For example, if the user is excited, the disclosure unit can advance the timing of token disclosure. Also, if the user is anxious, the disclosure unit can delay the timing of token disclosure to provide more detailed information. Furthermore, if the user is relaxed, the disclosure unit can disclose the token at a normal timing. This improves user satisfaction by adjusting the timing of token disclosure according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the disclosure unit may be performed using, for example, AI, or may be performed without using AI. For example, when estimating a user's emotion, the disclosure unit may estimate the emotion using AI and adjust the timing of token disclosure.

[0080] When disclosing the tokenized AI usage right, the disclosure unit can select the optimal disclosure method by taking into account the user's attribute information. When disclosing the tokenized AI usage right, the disclosure unit can select the optimal disclosure method by taking into account the user's attribute information. Attribute information includes, but is not limited to, age, gender, occupation, and interests. The disclosure unit can collect the user's attribute information and select the optimal disclosure method. For example, the disclosure unit can select an appropriate disclosure method according to the user's age group. The disclosure unit can also select an appropriate disclosure method according to the user's occupation. Furthermore, the disclosure unit can select an appropriate disclosure method according to the user's interests. In this way, by selecting the optimal disclosure method by taking into account the user's attribute information, it is possible to provide a disclosure method that is suitable for the user. Some or all of the above-mentioned processing in the disclosure unit may be performed, for example, using AI, or may be performed without using AI. For example, when collecting the user's attribute information, the disclosure unit can use AI to collect the information and select the optimal disclosure method.

[0081] The publishing unit can dynamically set the public offering price by referring to past transaction data when the token is publicly offered. The publishing unit can dynamically set the public offering price by referring to past transaction data when the token is publicly offered. Past transaction data includes, but is not limited to, transaction history, transaction volume, and transaction price. The publishing unit can analyze past transaction data and set an optimal public offering price. For example, the publishing unit can set the public offering price based on the past transaction data, taking into account the balance between supply and demand. The publishing unit can also set the public offering price based on the past transaction data, taking into account the prices of competitors. In this way, by dynamically setting the public offering price by referring to the past transaction data, an appropriate public offering price can be provided. Some or all of the above-described processing in the publishing unit may be performed, for example, using AI, or may be performed without using AI. For example, when analyzing past transaction data, the publishing unit can use AI to analyze the data and set the public offering price.

[0082] The publishing unit can estimate the user's emotions and adjust the order in which tokens are published based on the estimated user emotions. The publishing unit can estimate the user's emotions and adjust the order in which tokens are published based on the estimated user emotions. Examples of user emotions include, but are not limited to, excitement, anxiety, and relaxation. The publishing unit can estimate the user's emotions using an emotion analysis algorithm. For example, the publishing unit can analyze the user's facial expressions and voice data to estimate the emotion. The publishing unit can adjust the order in which tokens are published based on the estimated emotion. For example, if the user is excited, popular tokens can be published preferentially. Also, if the user is anxious, highly reliable tokens can be published preferentially. Furthermore, if the user is relaxed, tokens can be published in a normal order. This improves user satisfaction by adjusting the order in which tokens are published according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the disclosure unit may be performed using, for example, AI, or may be performed without using AI. For example, when estimating a user's emotion, the disclosure unit may estimate the emotion using AI and adjust the order in which tokens are disclosed.

[0083] The publishing unit may provide different publishing methods for each region, taking into account the user's geographical location information, when publishing tokens. The publishing unit may provide different publishing methods for each region, taking into account the user's geographical location information, when publishing tokens. Examples of geographical location information include, but are not limited to, GPS data and address information. The publishing unit may conduct market research for each region and provide an appropriate publishing method. For example, the publishing unit may publish tokens in regions with high demand based on the market research for each region. The publishing unit may also adjust the publishing method taking into account the economic situation for each region. Furthermore, the publishing unit may analyze the competitive situation for each region and provide an appropriate publishing method. By providing different publishing methods for each region taking into account the user's geographical location information, it is possible to provide a publishing method that meets the needs of each region. Some or all of the above-described processing in the publishing unit may be performed using, for example, AI, or may be performed without using AI. For example, the publishing unit may use AI to collect geographical location information and provide different publishing methods for each region.

[0084] When publishing a token, the publishing unit may provide different publishing methods for each industry, taking into account the user's industry information. When publishing a token, the publishing unit may provide different publishing methods for each industry, taking into account the user's industry information. Industry information includes, but is not limited to, the medical industry, education industry, and manufacturing industry. The publishing unit may evaluate the needs of each industry and provide an appropriate publishing method. For example, the publishing unit may provide a publishing method specialized for the medical industry. The publishing unit may also provide a publishing method specialized for the education industry. Furthermore, the publishing unit may provide a publishing method specialized for the manufacturing industry. By providing different publishing methods for each industry, taking into account the user's industry information, it is possible to provide a publishing method that meets the needs of each industry. Some or all of the above-described processing in the publishing unit may be performed using, for example, AI, or may be performed without using AI. For example, when collecting industry information, the publishing unit may use AI to collect the information and provide different publishing methods for each industry.

[0085] The trading unit can estimate a user's emotions and adjust the timing of token buying and selling based on the estimated user emotions. The trading unit can estimate a user's emotions and adjust the timing of token buying and selling based on the estimated user emotions. User emotions include, but are not limited to, excitement, anxiety, and relaxation. The trading unit can estimate a user's emotions using an emotion analysis algorithm. For example, the trading unit analyzes the user's facial expressions and voice data to estimate emotions. The trading unit adjusts the timing of token buying and selling based on the estimated emotions. For example, if the user is excited, the trading unit can advance the timing of buying and selling. Also, if the user is anxious, the trading unit can delay the timing of buying and selling and provide more detailed information. Furthermore, if the user is relaxed, the trading unit can proceed at a normal timing. This improves user satisfaction by adjusting the timing of token buying and selling based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the trading unit may be performed using, for example, AI, or may be performed without using AI. For example, when estimating a user's emotions, the trading unit may use AI to estimate the emotions and adjust the timing of buying and selling.

[0086] The trading unit can analyze past transaction history and present optimal buying and selling prices when buying and selling tokens. The trading unit analyzes past transaction history and presents optimal buying and selling prices when buying and selling tokens. Transaction history includes, but is not limited to, transaction date and time, transaction volume, and transaction price. The trading unit can analyze past transaction history and set optimal buying and selling prices. For example, the trading unit sets buying and selling prices based on past transaction history, taking into account the balance between supply and demand. The trading unit can also set buying and selling prices based on past transaction history, taking competitors' prices into consideration. In this way, by analyzing past transaction history and presenting optimal buying and selling prices, appropriate buying and selling prices can be provided. Some or all of the above-described processing in the trading unit may be performed using, for example, AI, or may be performed without using AI. For example, when analyzing past transaction history, the trading unit can use AI to analyze data and set buying and selling prices.

[0087] The trading unit can provide customized trading options taking into account the user's attribute information when trading tokens. The trading unit can provide customized trading options taking into account the user's attribute information when trading tokens. Attribute information includes, but is not limited to, age, gender, occupation, and interests. The trading unit can collect the user's attribute information and provide customized trading options. For example, the trading unit can provide appropriate trading options depending on the user's age group. The trading unit can also provide appropriate trading options depending on the user's occupation. Furthermore, the trading unit can provide appropriate trading options depending on the user's interests. This allows the trading unit to provide customized trading options that are suitable for the user by taking into account the user's attribute information. Some or all of the above-described processing in the trading unit can be performed using, for example, AI, or without AI. For example, the trading unit can use AI to collect the user's attribute information and provide customized trading options.

[0088] The trading unit can estimate a user's emotions and determine the priority of token buying and selling based on the estimated user emotions. The trading unit can estimate a user's emotions and determine the priority of token buying and selling based on the estimated user emotions. User emotions include, but are not limited to, excitement, anxiety, and relaxation. The trading unit can estimate a user's emotions using an emotion analysis algorithm. For example, the trading unit can analyze a user's facial expressions and voice data to estimate emotions. The trading unit determines the priority of token buying and selling based on the estimated emotions. For example, if a user is excited, popular tokens can be traded preferentially. Also, if a user is anxious, highly reliable tokens can be traded preferentially. Furthermore, if a user is relaxed, trading can be performed with normal priority. This improves user satisfaction by determining the priority of token buying and selling based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the trading unit may be performed using, for example, AI, or may be performed without using AI. For example, when estimating user emotions, the trading unit may use AI to estimate emotions and determine trading priorities.

[0089] The trading unit may provide different buying and selling options for each region by taking into account the user's geographical location information when buying and selling tokens. The trading unit may provide different buying and selling options for each region by taking into account the user's geographical location information when buying and selling tokens. Examples of geographical location information include, but are not limited to, GPS data and address information. The trading unit may conduct market research for each region and provide appropriate buying and selling options. For example, the trading unit may provide buying and selling options for regions with high demand based on the market research for each region. The trading unit may also adjust the buying and selling options by taking into account the economic conditions of each region. Furthermore, the trading unit may analyze the competitive situation for each region and provide appropriate buying and selling options. By providing different buying and selling options for each region by taking into account the user's geographical location information, it is possible to provide buying and selling options tailored to the needs of each region. Some or all of the above-described processing in the trading unit may be performed using, for example, AI, or may be performed without using AI. For example, the trading unit may use AI to collect geographical location information and provide different buying and selling options for each region.

[0090] When buying and selling tokens, the trading unit can provide different buying and selling options for each industry, taking into account the user's industry information. When buying and selling tokens, the trading unit can provide different buying and selling options for each industry, taking into account the user's industry information. Industry information includes, but is not limited to, the medical industry, the education industry, and the manufacturing industry. The trading unit can evaluate the needs of each industry and provide appropriate buying and selling options. For example, the trading unit can provide buying and selling options specialized for the medical industry. The trading unit can also provide buying and selling options specialized for the education industry. Furthermore, the trading unit can provide buying and selling options specialized for the manufacturing industry. In this way, by providing different buying and selling options for each industry, taking into account the user's industry information, it is possible to provide buying and selling options that meet the needs of each industry. Some or all of the above-mentioned processing in the trading unit may be performed, for example, using AI, or may be performed without using AI. For example, when collecting industry information, the trading unit can use AI to collect the information and provide different buying and selling options for each industry.

[0091] The fee income unit can estimate the user's emotions and adjust the fee setting based on the estimated user emotions. The fee income unit can estimate the user's emotions and adjust the fee setting based on the estimated user emotions. User emotions include, but are not limited to, excitement, anxiety, and relaxation. The fee income unit can estimate the user's emotions using an emotion analysis algorithm. For example, the fee income unit analyzes the user's facial expressions and voice data to estimate the user's emotions. The fee income unit adjusts the fee setting based on the estimated emotions. For example, if the user is excited, the fee can be set higher. Also, if the user is anxious, the fee can be set lower. Furthermore, if the user is relaxed, the fee can be set to a normal level. This improves user satisfaction by adjusting the fee setting according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the fee income unit may be performed using, for example, AI, or may be performed without using AI. For example, when estimating a user's emotion, the fee income unit may estimate the emotion using AI and adjust the fee setting.

[0092] The commission income unit can dynamically set an optimal commission rate by analyzing past transaction data. The commission income unit can dynamically set an optimal commission rate by analyzing past transaction data. Transaction data includes, but is not limited to, transaction history, transaction volume, and transaction price. The commission income unit can analyze past transaction data and set an optimal commission rate. For example, the commission income unit can set a commission rate based on past transaction data, taking into account the balance between supply and demand. The commission income unit can also set a commission rate based on past transaction data, taking into account the commission rates of competitors. In this way, by analyzing past transaction data and dynamically setting an optimal commission rate, an appropriate commission rate can be provided. Some or all of the above-described processing in the commission income unit may be performed using, for example, AI, or may be performed without using AI. For example, when analyzing past transaction data, the commission income unit can use AI to analyze the data and set a commission rate.

[0093] The commission income unit can set different commission rates depending on the size and frequency of transactions. The commission income unit sets different commission rates depending on the size and frequency of transactions. Examples of the size and frequency of transactions include, but are not limited to, the transaction volume and the number of transactions. The commission income unit can evaluate the size and frequency of transactions and set an appropriate commission rate. For example, the commission income unit can set a lower commission rate for large transactions. The commission income unit can also set a discounted commission rate for frequent transactions. Furthermore, the commission income unit can set a normal commission rate for small transactions. In this way, by setting different commission rates depending on the size and frequency of transactions, it is possible to provide a commission rate that is appropriate for the user. Some or all of the above-mentioned processing in the commission income unit may be performed, for example, using AI, or may be performed without using AI. For example, when evaluating the size and frequency of transactions, the commission income unit can use AI to analyze data and set a commission rate.

[0094] The fee income unit can estimate the user's emotions and adjust the fee collection method based on the estimated user emotions. The fee income unit can estimate the user's emotions and adjust the fee collection method based on the estimated user emotions. User emotions include, but are not limited to, excitement, anxiety, and relaxation. The fee income unit can estimate the user's emotions using an emotion analysis algorithm. For example, the fee income unit can analyze the user's facial expressions and voice data to estimate the user's emotions. The fee income unit can adjust the fee collection method based on the estimated emotions. For example, if the user is excited, the fee income unit can collect the fee immediately. If the user is anxious, the fee income unit can suggest installment collection. Furthermore, if the user is relaxed, the fee income unit can apply a normal collection method. This improves user satisfaction by adjusting the fee collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the fee income unit may be performed using, for example, AI, or may be performed without using AI. For example, when estimating a user's emotion, the fee income unit may estimate the emotion using AI and adjust the method of collecting fees.

[0095] The commission income department can set different commission rates for each region and earn commission income according to the demand in each region. The commission income department can set different commission rates for each region and earn commission income according to the demand in each region. For example, the region can be, but is not limited to, the economic situation or demand assessment of a country or region. The commission income department can conduct market research for each region and set commission rates according to the demand. For example, the commission income department can set commission rates for regions with high demand based on the market research for each region. The commission income department can also set commission rates taking into account the economic situation in each region. Furthermore, the commission income department can analyze the competitive situation in each region and set appropriate commission rates. By setting different commission rates for each region, it is possible to earn commission income according to the needs of each region. Some or all of the above-described processing in the commission income department can be performed using, for example, AI, or without AI. For example, the commission income department can conduct market research using AI to evaluate the demand in each region and set commission rates.

[0096] The commission income department can set different commission rates for each industry and earn commission income according to the demand of each industry. The commission income department can set different commission rates for each industry and earn commission income according to the demand of each industry. Examples of different industries include, but are not limited to, the medical industry, the education industry, and the manufacturing industry. The commission income department can conduct market research for each industry and set commission rates according to the demand. For example, the commission income department can set commission rates for industries with high demand based on the market research for each industry. The commission income department can also set commission rates taking into account the economic situation of each industry. Furthermore, the commission income department can analyze the competitive situation for each industry and set appropriate commission rates. By setting different commission rates for each industry, it is possible to earn commission income according to the needs of each industry. Some or all of the above-described processing in the commission income department can be performed using, for example, AI, or can be performed without AI. For example, the commission income department can conduct market research using AI to evaluate the demand of each industry and set commission rates. === Hard Collateral 1-1 === Each of the multiple elements including the tokenization unit, the publishing unit, the trading unit, and the fee income unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the tokenization unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The publishing unit publishes tokens via the output device 40 of the smart device 14 or the communication I / F 26 of the data processing device 12. The trading unit manages the buying and selling of tokens by the acceptance device 38 of the smart device 14 or the specific processing unit 290 of the data processing device 12. The fee income unit sets fees and earns income by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned tokenization unit, publishing unit, trading unit, and fee income unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the tokenization unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The publishing unit publishes tokens via the speaker 240 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The trading unit manages the buying and selling of tokens by the microphone 238 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The fee income unit sets fees and earns income by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned tokenization unit, publishing unit, buying and selling unit, and fee income unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the tokenization unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The publishing unit publishes tokens via the display 343 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12. The buying and selling unit manages the buying and selling of tokens by the microphone 238 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The fee income unit sets fees and earns income by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned tokenization unit, publishing unit, trading unit, and fee income unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the tokenization unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The publishing unit publishes tokens via the speaker 240 of the robot 414 or the communication I / F 26 of the data processing device 12. The trading unit manages the buying and selling of tokens by the microphone 238 of the robot 414 or the specific processing unit 290 of the data processing device 12. The fee income unit sets fees and earns income by the specific processing unit 290 of the data processing device 12.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] When tokenizing AI usage rights, the tokenization unit can adjust the value of the token taking into account the user's past usage history. For example, it can discount the value of the token for users with a large usage record in the past. It can also set a higher token value for users who use the service infrequently. Furthermore, it can provide customized token values ​​to users with specific usage patterns. This makes it possible to improve user satisfaction by adjusting the value of the token based on the user's past usage history.

[0099] When releasing a token, the publishing department can analyze users' social media activity and adjust the timing of release. For example, releasing a token at a time when users are most active on social media can attract more attention. It can also analyze users' emotional expressions on social media and release the token at a time when there is a lot of positive emotion. Furthermore, it can adjust the timing of release to coincide with specific events or campaigns. This allows the token to attract more attention by adjusting the timing of release based on social media activity.

[0100] The buying and selling unit can analyze a user's purchase history when buying and selling tokens to provide customized buying and selling options. For example, a special discount option can be provided to a user who has purchased high-value tokens in the past. Also, a user who frequently purchases tokens in a specific category can be provided with options specialized for that category. Furthermore, recommended tokens can be presented based on the user's purchase history. This makes it possible to improve user satisfaction by providing customized buying and selling options based on the user's purchase history.

[0101] The commission income unit can adjust the commission rate by analyzing the user's payment history when buying and selling tokens. For example, a lower commission rate can be set for a user who has made prompt payments in the past. A higher commission rate can also be set for a user who has a high rate of late payments. Furthermore, customized commission rates can be provided to users with specific payment patterns. This makes it possible to improve user satisfaction by adjusting the commission rate based on the user's payment history.

[0102] When tokenizing AI usage rights, the tokenization unit can collect user feedback and adjust the value of the token. For example, it can set a higher token value for AI that users have rated highly. It can also set a lower token value for AI that users have rated poorly. Furthermore, it can dynamically adjust the token value based on user feedback. This makes it possible to improve user satisfaction by adjusting the token value based on user feedback.

[0103] When releasing tokens, the release unit can provide different release methods for each region, taking into account the user's geographic location information. For example, if demand is high in a particular region, a release method specialized for that region can be provided. The release price can also be adjusted taking into account the economic situation of each region. Furthermore, a release method tailored to the culture and customs of each region can be provided. This makes it possible to release tokens according to the needs of each region by providing different release methods for each region based on the user's geographic location information.

[0104] The trading unit can estimate the user's emotions when buying and selling tokens and customize the trading process based on the estimated emotions. For example, if the user is excited, a quick trading process can be provided. If the user is anxious, a detailed explanation can be provided to reassure the user. Furthermore, if the user is relaxed, a normal trading process can be provided. This allows the trading process to be customized according to the user's emotions, thereby improving user satisfaction.

[0105] The commission income unit can adjust the commission rate when buying and selling tokens, taking into account the user's trading frequency. For example, it can set a lower commission rate for users who trade frequently. It can also set a higher commission rate for users who trade less frequently. Furthermore, it can provide customized commission rates to users with specific trading patterns. This allows for adjusting the commission rate based on the user's trading frequency, thereby improving user satisfaction.

[0106] When tokenizing AI usage rights, the tokenization unit can estimate the user's emotions and adjust the value of the token based on the estimated emotions. For example, if the user is excited, the value of the token can be set higher. Also, if the user is feeling anxious, the value of the token can be set lower. Furthermore, if the user is relaxed, the value can be set to normal. In this way, adjusting the value of the token based on the user's emotions can improve user satisfaction.

[0107] When publishing tokens, the publishing unit can analyze the user's purchase history and provide the optimal publishing method. For example, a special publishing method can be provided to a user who has purchased high-value tokens in the past. Also, a publishing method specialized for a specific category can be provided to a user who frequently purchases tokens in that category. Furthermore, recommended tokens can be presented based on the user's purchase history. This makes it possible to improve user satisfaction by providing the optimal publishing method based on the user's purchase history.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The tokenization unit tokenizes the AI ​​usage rights. Tokenization is the process of representing the AI ​​usage rights as digital tokens. For example, the tokenization unit divides the AI ​​usage rights into 100 tokens, with each token representing 1% of the AI ​​usage rights. The tokenization unit can generate tokens using blockchain technology. For example, the tokenization unit can generate tokens in the form of ERC-20 tokens or NFTs (Non-Fungible Tokens). Step 2: The public department makes the tokenized AI usage rights available on the platform. The public department provides a marketplace for buying and selling tokens. For example, the public department can make the tokens available through a web platform or mobile app. The public department can also adjust the timing of token release. For example, the public department can estimate user sentiment and adjust the timing of token release based on the estimated user sentiment. Step 3: The trading unit provides a market for buying and selling tokens. The trading unit manages the token buying and selling process and can set the currency to be used. For example, the trading unit can buy and sell tokens using fiat currency or cryptocurrency. The trading unit can also adjust the timing of token buying and selling. For example, the trading unit can estimate user sentiment and adjust the timing of token buying and selling based on the estimated user sentiment. Step 4: The commission income unit sets a certain percentage of the trading amount as a commission. The commission income unit sets a certain percentage of the trading amount as a commission. For example, the commission income unit can set 5% of the trading amount as a commission. The commission income unit can also adjust the commission setting. For example, the commission income unit can estimate the user's emotions and adjust the commission setting based on the estimated user's emotions.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[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 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.

[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 (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).

[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] 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.

[0122] 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.

[0123] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] 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.

[0139] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0158] 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.

[0159] 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.

[0160] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 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 tokenization unit that tokenizes the AI ​​usage rights; A disclosure unit that discloses the AI ​​usage rights tokenized by the tokenization unit; a trading unit that trades the tokens published by the publishing unit; a fee income unit that receives income from fees on tokens traded by the trading unit; A system characterized by:

2. The tokenization unit Expressing the right to use AI as a digital token The system of claim 1 .

3. The disclosure section Tokenized AI usage rights will be made public on the platform The system of claim 1 .

4. The buying and selling department Providing a marketplace for buying and selling tokens The system of claim 1 .

5. The fee income department Set a certain percentage of the transaction amount as a commission The system of claim 1 .

6. The tokenization unit Divide the rights to use AI so that it can be owned by multiple people The system of claim 1 .

7. The disclosure section Providing a platform that is accessible to everyone The system of claim 1 .

8. The buying and selling department Allowing token purchasers to obtain the right to exclusively use AI The system of claim 1 .

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