System

Abstract

An object of a system according to an embodiment is to ensure the reliability of an AI service.SOLUTION: A system according to an embodiment includes a reception unit, a verification unit, and a provision unit. The reception unit receives access from a user. The verification unit verifies the validity of the AI service using the blockchain technology based on the information received by the reception unit. The providing unit provides a reliable AI service based on the result verified by the verifying unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology posed the risk of AI services being replaced by malicious AI, making it difficult to ensure reliability.

[0005] The system according to the embodiment aims to ensure the reliability of AI services. [Means for solving the problem]

[0006] A system according to an embodiment includes a reception unit, a verification unit, and a provision unit. The reception unit receives access from a user. The verification unit verifies the legitimacy of the AI ​​service using blockchain technology based on the information received by the reception unit. The provision unit provides a reliable AI service based on the results of verification by the verification unit. [Effects of the Invention]

[0007] The system according to the embodiment can ensure the reliability of AI services. [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) A system according to an embodiment of the present invention is a service that prevents an AI service from being replaced with a malicious AI when it is accessed after countless services using generative AI have been implemented in society. This system verifies the legitimacy of an AI service using web 3.0 technology when a user accesses the AI ​​service, confirms that the verified AI service is legitimate, and provides the service to the user. This prevents replacement with a malicious AI and allows the system to provide a safe AI service. For example, by verifying the legitimacy of an AI service using web 3.0 technology when a user accesses an AI service, replacement with a malicious AI can be prevented. Furthermore, by providing only legitimate AI services to users based on the verification results, users can use AI services with peace of mind.

[0029] An AI service verification system according to an embodiment includes a reception unit, a verification unit, and a provision unit. The reception unit receives access from a user. Examples of user access include, but are not limited to, web access and API requests. The verification unit uses blockchain technology to verify the legitimacy of the AI ​​service based on the information received by the reception unit. For example, the verification unit determines whether the AI ​​service provider is legitimate by checking whether the AI ​​service provider is registered on the blockchain. Some or all of the above-described processing in the verification unit may be performed using AI, or may be performed without AI. For example, the verification unit may verify the legitimacy using an AI model that receives information about the AI ​​service provider as input and outputs legitimacy. The provision unit provides a reliable AI service based on the results of verification by the verification unit. For example, if the provision unit determines that the AI ​​service is legitimate, it provides the user with a link to access the AI ​​service. This allows the AI ​​service verification system according to an embodiment to safely use the AI ​​service.

[0030] The verification unit can verify the provider of the AI ​​service with high accuracy using blockchain technology. High accuracy includes, but is not limited to, methods for measuring error rates and accuracy. The verification unit can verify the provider of the AI ​​service with, for example, blockchain technology. For example, the verification unit verifies the provider of the AI ​​service by comparing the provider information registered in the blockchain and verifying its legitimacy. The verification unit can also evaluate the accuracy of the provider information using AI. For example, the verification unit can verify the legitimacy of the provider with high accuracy using an AI model that inputs the provider information and outputs the accuracy. This allows the legitimacy of the AI ​​service to be verified with high accuracy.

[0031] The providing unit can provide the user with a link for accessing an AI service whose legitimacy has been confirmed by the verifying unit. Examples of links include, but are not limited to, a URL link or a two-dimensional code (e.g., a QR code (registered trademark)). The providing unit can provide the user with a link for accessing an AI service whose legitimacy has been confirmed by the verifying unit. For example, if the providing unit confirms that the AI ​​service is legitimate, it can provide the user with a URL link for accessing the AI ​​service. The providing unit can also generate a two-dimensional code that the user can scan with a smartphone to access the AI ​​service. This allows the user to access the legitimate AI service.

[0032] The AI ​​service verification system includes a management unit that manages information to be registered in the blockchain. The management unit manages the information to be registered in the blockchain. Management includes, but is not limited to, database management and access control, for example. The management unit, for example, stores the information to be registered in the blockchain in a database and performs access control as needed. The management unit can also update or delete the registered information. For example, the management unit periodically checks the registered information and updates or deletes it as needed. This makes it easier to manage the information to be registered in the blockchain.

[0033] The AI ​​service verification system includes a description unit that describes details of the verification process. The description unit describes the details of the verification process. Details include, but are not limited to, step-by-step procedures and tools used. For example, the description unit describes each step of the verification process in detail to make it easy for users to understand. The description unit can also describe the tools and technologies used. For example, the description unit describes in detail how to use blockchain technology and AI models. This improves the transparency of the verification process.

[0034] The reception unit can analyze the user's past access history and select the optimal reception method. Optimal includes, but is not limited to, efficiency and user satisfaction, for example. The reception unit can, for example, analyze the time periods in which the user frequently accessed the service in the past and prioritize reception of access requests during those time periods. The reception unit can also analyze the devices the user has used in the past and suggest the optimal device for access. Furthermore, the reception unit can prioritize reception of access requests to specific AI services based on the user's past access history. This allows the optimal reception method to be provided based on the user's past access history.

[0035] The reception unit can perform filtering based on the user's current device and network conditions when receiving access. Filtering includes, but is not limited to, for example, the type of device and network speed. For example, the reception unit can provide a lightweight interface when the user is using a slow network. The reception unit can also provide a rich interface when the user is using a high-performance device. Furthermore, the reception unit can select the optimal data transfer method depending on the user's network conditions. This makes it possible to provide an optimal interface depending on the user's device and network conditions.

[0036] When accepting access, the acceptance unit can select the optimum acceptance means according to the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and gesture input. For example, if the user is using voice input, the acceptance unit accepts access using voice recognition technology. Furthermore, if the user is using text input, the acceptance unit can also accept access using text analysis technology. Furthermore, if the user is using gesture input, the acceptance unit can also accept access using gesture recognition technology. This makes it possible to provide the optimum acceptance means according to the user's input method.

[0037] When accepting access, the reception unit can prioritize accepting highly relevant accesses by taking into consideration the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP address, for example. For example, when the user is in a specific area, the reception unit can prioritize accepting accesses to AI services related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize accepting accesses to AI services related to the user's destination. Furthermore, when the user is at home, the reception unit can also prioritize accepting accesses to AI services related to the user's home. This makes it possible to prioritize accepting highly relevant accesses based on the user's geographical location information.

[0038] When accepting access requests, the reception unit can analyze the user's social media activity and accept related access requests. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the reception unit can preferentially accept access requests to AI services related to locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and preferentially accept access requests to related AI services. Furthermore, the reception unit can also refer to the activities of the user's friends on social media and preferentially accept access requests to related AI services. This makes it possible to accept related access requests based on the user's social media activity.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving access. Feedback includes, but is not limited to, for example, questionnaires and user reviews. The reception unit can, for example, suggest the optimal reception method based on the user's past feedback. The reception unit can also preferentially select a specific reception method based on the user's past feedback. Furthermore, the reception unit can customize the reception interface by reflecting the user's feedback. This makes it possible to provide the optimal reception method based on the user's past feedback.

[0040] During verification, the verification unit can adjust the level of detail of the verification based on the importance of the AI ​​service. Examples of importance include, but are not limited to, business impact and user priority. For example, the verification unit performs detailed verification for important AI services. The verification unit can also perform standard verification for general AI services. Furthermore, the verification unit can also perform simplified verification for low-importance AI services. This allows verification to be performed at an appropriate level of detail according to the importance of the AI ​​service.

[0041] During verification, the verification unit can apply different verification algorithms depending on the category of the AI ​​service. Examples of categories include, but are not limited to, technology categories and business categories. For example, the verification unit applies a strict verification algorithm for security-related AI services. The verification unit can also apply a flexible verification algorithm for entertainment-related AI services. Furthermore, the verification unit can also apply a verification algorithm according to business requirements for business-related AI services. This makes it possible to apply an appropriate verification algorithm depending on the category of the AI ​​service.

[0042] During verification, the verification unit can improve the accuracy of the verification by referring to the user's past verification results. Accuracy includes, but is not limited to, for example, an error rate and a method for measuring accuracy. For example, the verification unit optimizes the verification algorithm based on the user's past verification results. The verification unit can also extract specific patterns from the user's past verification results to improve the verification accuracy. Furthermore, the verification unit can analyze the user's past verification results and improve the verification process. This makes it possible to improve the accuracy of the verification based on the user's past verification results.

[0043] During verification, the verification unit can determine the priority of verification based on the timing of provision of the AI ​​service. The timing of provision includes, but is not limited to, for example, the release date and the service start date. For example, the verification unit prioritizes verification of an AI service that will be provided soon. In addition, the verification unit can also verify an AI service that will not be provided for a long time with normal priority. Furthermore, the verification unit can postpone verification of an AI service whose timing of provision is unknown. This allows verification to be performed with appropriate priority according to the timing of provision of the AI ​​service.

[0044] During verification, the verification unit can adjust the verification order based on the relevance of the AI ​​service. The order includes, but is not limited to, for example, priority and relevance. For example, the verification unit prioritizes verification for AI services with high relevance. Furthermore, the verification unit can also verify AI services with medium relevance in the normal order. Furthermore, the verification unit can postpone verification for AI services with low relevance. This allows verification to be performed in an appropriate order according to the relevance of the AI ​​service.

[0045] During verification, the verification unit can adjust the use of technical terms in the verification depending on the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the user has specialized knowledge, the verification unit can perform verification using a lot of technical terms. Furthermore, if the user has general knowledge, the verification unit can also perform verification using less technical terms. Furthermore, if the user is a beginner, the verification unit can perform verification without using any technical terms at all. This allows verification to be performed using appropriate technical terms according to the user's level of expertise.

[0046] The providing unit can adjust the level of detail of the information provided based on the importance of the AI ​​service when providing the information. Examples of importance include, but are not limited to, business impact and user priority. For example, the providing unit provides detailed information for an important AI service. The providing unit can also provide regular information for a general AI service. Furthermore, the providing unit can provide simplified information for an AI service with low importance. This allows the information to be provided with an appropriate level of detail according to the importance of the AI ​​service.

[0047] The provision unit can apply different provision algorithms depending on the category of the AI ​​service when providing the service. Examples of categories include, but are not limited to, technology categories and business categories. For example, the provision unit applies a strict provision algorithm for security-related AI services. The provision unit can also apply a flexible provision algorithm for entertainment-related AI services. Furthermore, the provision unit can also apply a provision algorithm according to business requirements for business-related AI services. This makes it possible to apply an appropriate provision algorithm depending on the category of the AI ​​service.

[0048] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. Accuracy includes, but is not limited to, for example, an error rate and a method for measuring accuracy. For example, the providing unit can optimize the provision algorithm based on the results of AI services used by the user in the past. The providing unit can also extract specific patterns from the user's past provision results to improve the provision accuracy. Furthermore, the providing unit can analyze the user's past provision results and improve the provision process. This allows the accuracy of the provision to be improved based on the user's past provision results.

[0049] The provision unit can determine the priority of provision based on the timing of provision of the AI ​​service at the time of provision. The provision timing includes, but is not limited to, for example, the release date and the service start date. For example, the provision unit provides an AI service that will be provided most recently with priority. In addition, the provision unit can provide an AI service whose provision timing is far away with normal priority. Furthermore, the provision unit can postpone the provision of an AI service whose provision timing is unknown. This allows the AI ​​service to be provided with appropriate priority according to the timing of provision.

[0050] The providing unit can adjust the order of provision based on the relevance of the AI ​​service when providing the services. The order includes, but is not limited to, priority and relevance, for example. For example, the providing unit provides a highly relevant AI service with priority. Furthermore, the providing unit can provide a medium-relevant AI service in the normal order. Furthermore, the providing unit can provide a low-relevant AI service at a later date. This allows the AI ​​services to be provided in an appropriate order according to their relevance.

[0051] The providing unit can adjust the use of technical terminology in the provision depending on the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the user has technical expertise, the providing unit can provide an AI service that uses a lot of technical terminology. Furthermore, if the user has general knowledge, the providing unit can provide an AI service that uses less technical terminology. Furthermore, if the user is a beginner, the providing unit can provide an AI service that does not use any technical terminology at all. This allows the provision of appropriate technical terminology according to the user's level of expertise.

[0052] During management, the management unit can optimize the management algorithm by referring to past management data. Optimization includes, but is not limited to, for example, adjusting the algorithm and improving performance. For example, the management unit selects an optimal management algorithm based on the past management data. The management unit can also extract specific patterns from the past management data and optimize the management algorithm. Furthermore, the management unit can analyze the past management data and improve the management process. This allows the management algorithm to be optimized based on the past management data.

[0053] During management, the management unit can update the management data by reflecting user feedback. Feedback includes, but is not limited to, for example, surveys and user reviews. The management unit updates the management data based on, for example, feedback provided by the user. The management unit can also prioritize management of specific data based on user feedback. Furthermore, the management unit can improve the management process by reflecting user feedback. This allows the management data to be updated based on user feedback.

[0054] During management, the management unit can weight the management data based on the timing of provision of the AI ​​service. Provision times include, but are not limited to, for example, the release date and the service start date. For example, the management unit can weight the management data higher for an AI service that has recently been provided. The management unit can also weight the management data normally for an AI service that will be provided in the distant future. Furthermore, the management unit can also weight the management data lower for an AI service whose provision time is unknown. This allows the management data to be handled with appropriate weighting according to the timing of provision of the AI ​​service.

[0055] During management, the management unit can integrate information from different data sources to enrich the management data. Integration includes, but is not limited to, data merging and data cleansing, for example. The management unit, for example, integrates information from different data sources to enrich the management data. The management unit can also analyze information from different data sources and select optimal management data. Furthermore, the management unit can improve the management process based on information from different data sources. This allows information from different data sources to be integrated to enrich the management data.

[0056] When writing, the description unit can adjust the level of detail of the description based on the importance of the verification process. Examples of importance include, but are not limited to, business impact and user priority. For example, the description unit provides detailed description for an important verification process. Furthermore, the description unit can also provide standard description for a general verification process. Furthermore, the description unit can also provide simplified description for a verification process of low importance. This allows the description to be provided with an appropriate level of detail according to the importance of the verification process.

[0057] The description unit can apply different description algorithms depending on the category of the verification process during description. Examples of categories include, but are not limited to, a technology category and a business category. For example, the description unit can apply a strict description algorithm in the case of a security-related verification process. The description unit can also apply a flexible description algorithm in the case of an entertainment-related verification process. Furthermore, the description unit can also apply a description algorithm according to business requirements in the case of a business-related verification process. This makes it possible to apply an appropriate description algorithm depending on the category of the verification process.

[0058] When writing, the writing unit can improve the accuracy of the writing by referring to the user's past writing results. Accuracy includes, but is not limited to, for example, error rate and accuracy measurement methods. For example, the writing unit can optimize the writing algorithm based on the user's past writing results. The writing unit can also extract specific patterns from the user's past writing results to improve the writing accuracy. Furthermore, the writing unit can analyze the user's past writing results and improve the writing process. This makes it possible to improve the accuracy of the writing based on the user's past writing results.

[0059] When writing, the writing unit can determine the priority of writing based on the time when the verification process is provided. The time when the verification process is provided includes, but is not limited to, for example, the release date or the service start date. For example, the writing unit writes the verification process that is most recently provided first. In addition, the writing unit can write the verification process that is not yet provided first in the normal priority order. Furthermore, the writing unit can write the verification process whose time when the verification process is provided is unknown at a later date. This allows the verification process to be written in an appropriate priority order according to the time when the verification process is provided.

[0060] When writing, the writing unit can adjust the order of writing based on the relevance of the verification process. The order includes, for example, priority and relevance, but is not limited to these examples. For example, in the case of a verification process with high relevance, the writing unit writes first. In addition, in the case of a verification process with medium relevance, the writing unit can write in the normal order. Furthermore, in the case of a verification process with low relevance, the writing unit can write later. This allows writing to be done in an appropriate order according to the relevance of the verification process.

[0061] The description unit can adjust the use of technical terms in the description according to the user's level of expertise when writing. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the user has specialized knowledge, the description unit can write using a lot of technical terms. Furthermore, if the user has general knowledge, the description unit can also write using less technical terms. Furthermore, if the user is a beginner, the description unit can also write using no technical terms at all. This allows the description to be written using appropriate technical terms according to the user's level of expertise.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The reception unit can analyze the user's past access history and select the optimal reception method. For example, it can analyze the time periods in which the user frequently accessed the service in the past and prioritize reception of access requests during those times. The reception unit can also analyze the devices the user has used in the past and suggest the optimal device for access. Furthermore, the reception unit can prioritize reception of access requests to specific AI services based on the user's past access history. This makes it possible to provide the optimal reception method based on the user's past access history.

[0064] During verification, the verification unit can adjust the level of verification detail based on the importance of the AI ​​service. For example, for important AI services, detailed verification can be performed. For general AI services, normal verification can also be performed. Furthermore, for low-importance AI services, simplified verification can also be performed. This allows verification to be performed at an appropriate level of detail according to the importance of the AI ​​service.

[0065] The provision unit can apply different provision algorithms depending on the category of the AI ​​service when providing the service. For example, a strict provision algorithm can be applied to security-related AI services. A flexible provision algorithm can also be applied to entertainment-related AI services. Furthermore, a provision algorithm according to business requirements can also be applied to business-related AI services. This makes it possible to apply an appropriate provision algorithm depending on the category of the AI ​​service.

[0066] During management, the management unit can optimize the management algorithm by referring to past management data. For example, the optimal management algorithm can be selected based on past management data. It can also extract specific patterns from past management data and optimize the management algorithm. Furthermore, it can analyze past management data and improve the management process. This makes it possible to optimize the management algorithm based on past management data.

[0067] The description unit can adjust the level of detail of the description based on the importance of the verification process when describing it. For example, a detailed description can be provided for an important verification process. A normal description can also be provided for a general verification process. Furthermore, a simplified description can also be provided for a verification process of low importance. This allows the description to be provided with an appropriate level of detail according to the importance of the verification process.

[0068] The provision unit can improve the accuracy of provision by referring to the user's past provision results when providing information. For example, the provision algorithm can be optimized based on the results of AI services the user has used in the past. It can also extract specific patterns from the user's past provision results to improve the accuracy of provision. Furthermore, it can analyze the user's past provision results and improve the provision process. This makes it possible to improve the accuracy of provision based on the user's past provision results.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The reception unit receives access from a user. The access from a user includes, but is not limited to, web access and API requests. Step 2: The verification unit uses blockchain technology to verify the legitimacy of the AI ​​service based on the information received by the reception unit. For example, the verification unit determines whether the AI ​​service is legitimate by checking whether the provider of the AI ​​service is registered on the blockchain. The processing in the verification unit may be performed using AI or may be performed without using AI. Step 3: The provider provides a reliable AI service based on the results of verification by the verification unit. For example, if the provider confirms that the AI ​​service is legitimate, it provides the user with a link to access the AI ​​service.

[0071] (Example 2) A system according to an embodiment of the present invention is a service that prevents an AI service from being replaced with a malicious AI when it is accessed after countless services using generative AI have been implemented in society. This system verifies the legitimacy of an AI service using web 3.0 technology when a user accesses the AI ​​service, confirms that the verified AI service is legitimate, and provides the service to the user. This prevents replacement with a malicious AI and allows the system to provide a safe AI service. For example, by verifying the legitimacy of an AI service using web 3.0 technology when a user accesses an AI service, replacement with a malicious AI can be prevented. Furthermore, by providing only legitimate AI services to users based on the verification results, users can use AI services with peace of mind.

[0072] An AI service verification system according to an embodiment includes a reception unit, a verification unit, and a provision unit. The reception unit receives access from a user. Examples of user access include, but are not limited to, web access and API requests. The verification unit uses blockchain technology to verify the legitimacy of the AI ​​service based on the information received by the reception unit. For example, the verification unit determines whether the AI ​​service provider is legitimate by checking whether the AI ​​service provider is registered on the blockchain. Some or all of the above-described processing in the verification unit may be performed using AI, or may be performed without AI. For example, the verification unit may verify the legitimacy using an AI model that receives information about the AI ​​service provider as input and outputs legitimacy. The provision unit provides a reliable AI service based on the results of verification by the verification unit. For example, if the provision unit determines that the AI ​​service is legitimate, it provides the user with a link to access the AI ​​service. This allows the AI ​​service verification system according to an embodiment to safely use the AI ​​service.

[0073] The verification unit can verify the provider of the AI ​​service with high accuracy using blockchain technology. High accuracy includes, but is not limited to, methods for measuring error rates and accuracy. The verification unit can verify the provider of the AI ​​service with, for example, blockchain technology. For example, the verification unit verifies the provider of the AI ​​service by comparing the provider information registered in the blockchain and verifying its legitimacy. The verification unit can also evaluate the accuracy of the provider information using AI. For example, the verification unit can verify the legitimacy of the provider with high accuracy using an AI model that inputs the provider information and outputs the accuracy. This allows the legitimacy of the AI ​​service to be verified with high accuracy.

[0074] The providing unit can provide the user with a link for accessing an AI service whose legitimacy has been confirmed by the verifying unit. Examples of links include, but are not limited to, a URL link or a two-dimensional code (e.g., a QR code). The providing unit can provide the user with a link for accessing an AI service whose legitimacy has been confirmed by the verifying unit. For example, if the providing unit confirms that the AI ​​service is legitimate, it can provide the user with a URL link for accessing the AI ​​service. The providing unit can also generate a two-dimensional code that the user can scan with a smartphone to access the AI ​​service. This allows the user to access the legitimate AI service.

[0075] The AI ​​service verification system includes a management unit that manages information to be registered in the blockchain. The management unit manages the information to be registered in the blockchain. Management includes, but is not limited to, database management and access control, for example. The management unit, for example, stores the information to be registered in the blockchain in a database and performs access control as needed. The management unit can also update or delete the registered information. For example, the management unit periodically checks the registered information and updates or deletes it as needed. This makes it easier to manage the information to be registered in the blockchain.

[0076] The AI ​​service verification system includes a description unit that describes details of the verification process. The description unit describes the details of the verification process. Details include, but are not limited to, step-by-step procedures and tools used. For example, the description unit describes each step of the verification process in detail to make it easy for users to understand. The description unit can also describe the tools and technologies used. For example, the description unit describes in detail how to use blockchain technology and AI models. This improves the transparency of the verification process.

[0077] The reception unit can estimate the user's emotions and adjust the timing of access acceptance based on the estimated user emotions. Emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the reception unit can delay the timing of access acceptance and wait until the user is relaxed. Furthermore, if the user is relaxed, the reception unit can also accept access immediately. Furthermore, if the user is in a hurry, the reception unit can also accept access quickly. This allows access to be accepted at an appropriate timing according to the user's emotions. The 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.

[0078] The reception unit can analyze the user's past access history and select the optimal reception method. Optimal includes, but is not limited to, efficiency and user satisfaction, for example. The reception unit can, for example, analyze the time periods in which the user frequently accessed the service in the past and prioritize reception of access requests during those time periods. The reception unit can also analyze the devices the user has used in the past and suggest the optimal device for access. Furthermore, the reception unit can prioritize reception of access requests to specific AI services based on the user's past access history. This allows the optimal reception method to be provided based on the user's past access history.

[0079] The reception unit can perform filtering based on the user's current device and network conditions when receiving access. Filtering includes, but is not limited to, for example, the type of device and network speed. For example, the reception unit can provide a lightweight interface when the user is using a slow network. The reception unit can also provide a rich interface when the user is using a high-performance device. Furthermore, the reception unit can select the optimal data transfer method depending on the user's network conditions. This makes it possible to provide an optimal interface depending on the user's device and network conditions.

[0080] When accepting access, the acceptance unit can select the optimum acceptance means according to the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and gesture input. For example, if the user is using voice input, the acceptance unit accepts access using voice recognition technology. Furthermore, if the user is using text input, the acceptance unit can also accept access using text analysis technology. Furthermore, if the user is using gesture input, the acceptance unit can also accept access using gesture recognition technology. This makes it possible to provide the optimum acceptance means according to the user's input method.

[0081] The reception unit can estimate the user's emotions and determine the priority of the accesses to be received based on the estimated user's emotions. Priorities include, but are not limited to, urgency and importance, for example. For example, when the user is stressed, the reception unit can receive important accesses with priority. Furthermore, when the user is relaxed, the reception unit can also receive urgent accesses with top priority, when the user is in a hurry. This allows accesses to be received with an appropriate priority according to the user's emotions. The 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.

[0082] When accepting access, the reception unit can prioritize accepting highly relevant accesses by taking into consideration the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP address, for example. For example, when the user is in a specific area, the reception unit can prioritize accepting accesses to AI services related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize accepting accesses to AI services related to the user's destination. Furthermore, when the user is at home, the reception unit can also prioritize accepting accesses to AI services related to the user's home. This makes it possible to prioritize accepting highly relevant accesses based on the user's geographical location information.

[0083] When accepting access requests, the reception unit can analyze the user's social media activity and accept related access requests. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the reception unit can preferentially accept access requests to AI services related to locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and preferentially accept access requests to related AI services. Furthermore, the reception unit can also refer to the activities of the user's friends on social media and preferentially accept access requests to related AI services. This makes it possible to accept related access requests based on the user's social media activity.

[0084] The reception unit can customize the reception method by reflecting the user's past feedback when receiving access. Feedback includes, but is not limited to, for example, questionnaires and user reviews. The reception unit can, for example, suggest the optimal reception method based on the user's past feedback. The reception unit can also preferentially select a specific reception method based on the user's past feedback. Furthermore, the reception unit can customize the reception interface by reflecting the user's feedback. This makes it possible to provide the optimal reception method based on the user's past feedback.

[0085] The verification unit can estimate the user's emotions and adjust the way the verification process is expressed based on the estimated user's emotions. Examples of the expression methods include, but are not limited to, text tone and visual elements. For example, if the user is nervous, the verification unit can provide a simple, highly visible verification process. Furthermore, if the user is relaxed, the verification unit can provide a verification process that includes detailed information. Furthermore, if the user is in a hurry, the verification unit can provide a process that quickly performs verification. This allows the verification process to be provided using an appropriate expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] During verification, the verification unit can adjust the level of detail of the verification based on the importance of the AI ​​service. Examples of importance include, but are not limited to, business impact and user priority. For example, the verification unit performs detailed verification for important AI services. The verification unit can also perform standard verification for general AI services. Furthermore, the verification unit can also perform simplified verification for low-importance AI services. This allows verification to be performed at an appropriate level of detail according to the importance of the AI ​​service.

[0087] During verification, the verification unit can apply different verification algorithms depending on the category of the AI ​​service. Examples of categories include, but are not limited to, technology categories and business categories. For example, the verification unit applies a strict verification algorithm for security-related AI services. The verification unit can also apply a flexible verification algorithm for entertainment-related AI services. Furthermore, the verification unit can also apply a verification algorithm according to business requirements for business-related AI services. This makes it possible to apply an appropriate verification algorithm depending on the category of the AI ​​service.

[0088] During verification, the verification unit can improve the accuracy of the verification by referring to the user's past verification results. Accuracy includes, but is not limited to, for example, an error rate and a method for measuring accuracy. For example, the verification unit optimizes the verification algorithm based on the user's past verification results. The verification unit can also extract specific patterns from the user's past verification results to improve the verification accuracy. Furthermore, the verification unit can analyze the user's past verification results and improve the verification process. This makes it possible to improve the accuracy of the verification based on the user's past verification results.

[0089] The verification unit can estimate the user's emotions and adjust the length of the verification based on the estimated user's emotions. Examples of the length include, but are not limited to, the length of time and the amount of data. For example, if the user is nervous, the verification unit can perform a short, to-the-point verification. If the user is relaxed, the verification unit can also perform a longer verification including detailed explanations. Furthermore, if the user is in a hurry, the verification unit can also perform a quick verification. This allows the verification to be performed at an appropriate length depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] During verification, the verification unit can determine the priority of verification based on the timing of provision of the AI ​​service. The timing of provision includes, but is not limited to, for example, the release date and the service start date. For example, the verification unit prioritizes verification of an AI service that will be provided soon. In addition, the verification unit can also verify an AI service that will not be provided for a long time with normal priority. Furthermore, the verification unit can postpone verification of an AI service whose timing of provision is unknown. This allows verification to be performed with appropriate priority according to the timing of provision of the AI ​​service.

[0091] During verification, the verification unit can adjust the verification order based on the relevance of the AI ​​service. The order includes, but is not limited to, for example, priority and relevance. For example, the verification unit prioritizes verification for AI services with high relevance. Furthermore, the verification unit can also verify AI services with medium relevance in the normal order. Furthermore, the verification unit can postpone verification for AI services with low relevance. This allows verification to be performed in an appropriate order according to the relevance of the AI ​​service.

[0092] During verification, the verification unit can adjust the use of technical terms in the verification depending on the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the user has specialized knowledge, the verification unit can perform verification using a lot of technical terms. Furthermore, if the user has general knowledge, the verification unit can also perform verification using less technical terms. Furthermore, if the user is a beginner, the verification unit can perform verification without using any technical terms at all. This allows verification to be performed using appropriate technical terms according to the user's level of expertise.

[0093] The providing unit can estimate the user's emotions and adjust the expression method of the AI ​​service provided based on the estimated user's emotions. Expression methods include, but are not limited to, text tone and visual elements. For example, if the user is relaxed, the providing unit can provide an AI service that proceeds at a leisurely pace. Furthermore, if the user is in a hurry, the providing unit can also provide an AI service that quickly provides information. Furthermore, if the user is excited, the providing unit can also provide an AI service that adds visually stimulating effects. This allows the AI ​​service to be provided with an appropriate expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] The providing unit can adjust the level of detail of the information provided based on the importance of the AI ​​service when providing the information. Examples of importance include, but are not limited to, business impact and user priority. For example, the providing unit provides detailed information for an important AI service. The providing unit can also provide regular information for a general AI service. Furthermore, the providing unit can provide simplified information for an AI service with low importance. This allows the information to be provided with an appropriate level of detail according to the importance of the AI ​​service.

[0095] The provision unit can apply different provision algorithms depending on the category of the AI ​​service when providing the service. Examples of categories include, but are not limited to, technology categories and business categories. For example, the provision unit applies a strict provision algorithm for security-related AI services. The provision unit can also apply a flexible provision algorithm for entertainment-related AI services. Furthermore, the provision unit can also apply a provision algorithm according to business requirements for business-related AI services. This makes it possible to apply an appropriate provision algorithm depending on the category of the AI ​​service.

[0096] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. Accuracy includes, but is not limited to, for example, an error rate and a method for measuring accuracy. For example, the providing unit can optimize the provision algorithm based on the results of AI services used by the user in the past. The providing unit can also extract specific patterns from the user's past provision results to improve the provision accuracy. Furthermore, the providing unit can analyze the user's past provision results and improve the provision process. This allows the accuracy of the provision to be improved based on the user's past provision results.

[0097] The providing unit can estimate the user's emotions and adjust the length of the AI ​​service to be provided based on the estimated user's emotions. Examples of the length include, but are not limited to, the length of time and the amount of data. For example, if the user is in a hurry, the providing unit can provide a short, to-the-point AI service. If the user is relaxed, the providing unit can also provide a longer AI service with detailed explanations. Furthermore, if the user is excited, the providing unit can also provide an AI service with visually stimulating effects. This allows the AI ​​service to be provided at an appropriate length 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0098] The provision unit can determine the priority of provision based on the timing of provision of the AI ​​service at the time of provision. The provision timing includes, but is not limited to, for example, the release date and the service start date. For example, the provision unit provides an AI service that will be provided most recently with priority. In addition, the provision unit can provide an AI service whose provision timing is far away with normal priority. Furthermore, the provision unit can postpone the provision of an AI service whose provision timing is unknown. This allows the AI ​​service to be provided with appropriate priority according to the timing of provision.

[0099] The providing unit can adjust the order of provision based on the relevance of the AI ​​service when providing the services. The order includes, but is not limited to, priority and relevance, for example. For example, the providing unit provides a highly relevant AI service with priority. Furthermore, the providing unit can provide a medium-relevant AI service in the normal order. Furthermore, the providing unit can provide a low-relevant AI service at a later date. This allows the AI ​​services to be provided in an appropriate order according to their relevance.

[0100] The providing unit can adjust the use of technical terminology in the provision depending on the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the user has technical expertise, the providing unit can provide an AI service that uses a lot of technical terminology. Furthermore, if the user has general knowledge, the providing unit can provide an AI service that uses less technical terminology. Furthermore, if the user is a beginner, the providing unit can provide an AI service that does not use any technical terminology at all. This allows the provision of appropriate technical terminology according to the user's level of expertise.

[0101] The management unit can estimate the user's emotions and select information to manage based on the estimated user emotions. Emotions include, but are not limited to, facial expression recognition and voice analysis. For example, when the user is feeling stressed, the management unit manages only important information. Furthermore, when the user is relaxed, the management unit can also manage detailed information. Furthermore, when the user is in a hurry, the management unit can prioritize information that requires quick management. This allows appropriate information to be managed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] During management, the management unit can optimize the management algorithm by referring to past management data. Optimization includes, but is not limited to, for example, adjusting the algorithm and improving performance. For example, the management unit selects an optimal management algorithm based on the past management data. The management unit can also extract specific patterns from the past management data and optimize the management algorithm. Furthermore, the management unit can analyze the past management data and improve the management process. This allows the management algorithm to be optimized based on the past management data.

[0103] During management, the management unit can update the management data by reflecting user feedback. Feedback includes, but is not limited to, for example, surveys and user reviews. The management unit updates the management data based on, for example, feedback provided by the user. The management unit can also prioritize management of specific data based on user feedback. Furthermore, the management unit can improve the management process by reflecting user feedback. This allows the management data to be updated based on user feedback.

[0104] The management unit can estimate the user's emotions and adjust the frequency of management based on the estimated user emotions. Examples of frequencies include, but are not limited to, periodic and real-time. For example, the management unit can reduce the frequency of management when the user is stressed. Furthermore, the management unit can also perform management at a normal frequency when the user is relaxed. Furthermore, the management unit can increase the frequency of management when the user is in a hurry. This allows management to be performed at an appropriate frequency according to the user's emotions. The 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.

[0105] During management, the management unit can weight the management data based on the timing of provision of the AI ​​service. Provision times include, but are not limited to, for example, the release date and the service start date. For example, the management unit can weight the management data higher for an AI service that has recently been provided. The management unit can also weight the management data normally for an AI service that will be provided in the distant future. Furthermore, the management unit can also weight the management data lower for an AI service whose provision time is unknown. This allows the management data to be handled with appropriate weighting according to the timing of provision of the AI ​​service.

[0106] During management, the management unit can integrate information from different data sources to enrich the management data. Integration includes, but is not limited to, data merging and data cleansing, for example. The management unit, for example, integrates information from different data sources to enrich the management data. The management unit can also analyze information from different data sources and select optimal management data. Furthermore, the management unit can improve the management process based on information from different data sources. This allows information from different data sources to be integrated to enrich the management data.

[0107] The description unit can estimate the user's emotions and adjust the way information is expressed based on the estimated user's emotions. Examples of the expression methods include, but are not limited to, text tone and visual elements. For example, if the user is nervous, the description unit can provide a simple, highly visible expression method. Furthermore, if the user is relaxed, the description unit can provide a detailed expression method. Furthermore, if the user is in a hurry, the description unit can provide a brief expression method. This allows information to be written in an appropriate expression method according to the user's emotions. The 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.

[0108] When writing, the description unit can adjust the level of detail of the description based on the importance of the verification process. Examples of importance include, but are not limited to, business impact and user priority. For example, the description unit provides detailed description for an important verification process. Furthermore, the description unit can also provide standard description for a general verification process. Furthermore, the description unit can also provide simplified description for a verification process of low importance. This allows the description to be provided with an appropriate level of detail according to the importance of the verification process.

[0109] The description unit can apply different description algorithms depending on the category of the verification process during description. Examples of categories include, but are not limited to, a technology category and a business category. For example, the description unit can apply a strict description algorithm in the case of a security-related verification process. The description unit can also apply a flexible description algorithm in the case of an entertainment-related verification process. Furthermore, the description unit can also apply a description algorithm according to business requirements in the case of a business-related verification process. This makes it possible to apply an appropriate description algorithm depending on the category of the verification process.

[0110] When writing, the writing unit can improve the accuracy of the writing by referring to the user's past writing results. Accuracy includes, but is not limited to, for example, error rate and accuracy measurement methods. For example, the writing unit can optimize the writing algorithm based on the user's past writing results. The writing unit can also extract specific patterns from the user's past writing results to improve the writing accuracy. Furthermore, the writing unit can analyze the user's past writing results and improve the writing process. This makes it possible to improve the accuracy of the writing based on the user's past writing results.

[0111] The description unit can estimate the user's emotions and adjust the length of the information to be written based on the estimated user emotions. Examples of length include, but are not limited to, the length of time and the amount of data. For example, if the user is in a hurry, the description unit can write a short, to-the-point description. Furthermore, if the user is relaxed, the description unit can write a longer description with detailed explanations. Furthermore, if the user is excited, the description unit can write a description with a visually stimulating effect. This allows information to be written at an appropriate length depending on the user's emotions. The 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.

[0112] When writing, the writing unit can determine the priority of writing based on the time when the verification process is provided. The time when the verification process is provided includes, but is not limited to, for example, the release date or the service start date. For example, the writing unit writes the verification process that is most recently provided first. In addition, the writing unit can write the verification process that is not yet provided first in the normal priority order. Furthermore, the writing unit can write the verification process whose time when the verification process is provided is unknown at a later date. This allows the verification process to be written in an appropriate priority order according to the time when the verification process is provided.

[0113] When writing, the writing unit can adjust the order of writing based on the relevance of the verification process. The order includes, for example, priority and relevance, but is not limited to these examples. For example, in the case of a verification process with high relevance, the writing unit writes first. In addition, in the case of a verification process with medium relevance, the writing unit can write in the normal order. Furthermore, in the case of a verification process with low relevance, the writing unit can write later. This allows writing to be done in an appropriate order according to the relevance of the verification process.

[0114] The description unit can adjust the use of technical terms in the description according to the user's level of expertise when writing. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the user has specialized knowledge, the description unit can write using a lot of technical terms. Furthermore, if the user has general knowledge, the description unit can also write using less technical terms. Furthermore, if the user is a beginner, the description unit can also write using no technical terms at all. This allows the description to be written using appropriate technical terms according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, verification unit, provision unit, management unit, and description unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives access from a user. The verification unit is realized by the specific processing unit 290 of the data processing device 12 and verifies the legitimacy of an AI service using blockchain technology. The provision unit is realized by the control unit 46A of the smart device 14 and provides the verified AI service to the user. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages information to be registered in the blockchain. The description unit is realized by the specific processing unit 290 of the data processing device 12 and describes details of the verification process. Furthermore, the management unit can estimate the user's emotions and select information based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, verification unit, provision unit, management unit, and description unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives access from a user. The verification unit is realized by the specific processing unit 290 of the data processing device 12 and verifies the legitimacy of an AI service using blockchain technology. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the verified AI service to the user. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages information to be registered in the blockchain. The description unit is realized by the specific processing unit 290 of the data processing device 12 and describes details of the verification process. Furthermore, the management unit can estimate the user's emotions and select information based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, verification unit, provision unit, management unit, and description unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives access from a user. The verification unit is realized by the specific processing unit 290 of the data processing device 12 and verifies the legitimacy of an AI service using blockchain technology. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides the verified AI service to the user. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages information to be registered in the blockchain. The description unit is realized by the specific processing unit 290 of the data processing device 12 and describes details of the verification process. Furthermore, the management unit can estimate the user's emotions and select information based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, verification unit, provision unit, management unit, and description unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives access from a user. The verification unit is realized by the specific processing unit 290 of the data processing device 12 and verifies the legitimacy of an AI service using blockchain technology. The provision unit is realized by the control unit 46A of the robot 414 and provides the verified AI service to the user. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages information to be registered in the blockchain. The description unit is realized by the specific processing unit 290 of the data processing device 12 and describes details of the verification process. Furthermore, the management unit can estimate the user's emotions and select information based on the estimated emotions.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The reception unit can analyze the user's past access history and select the optimal reception method. For example, it can analyze the time periods in which the user frequently accessed the service in the past and prioritize reception of access requests during those times. The reception unit can also analyze the devices the user has used in the past and suggest the optimal device for access. Furthermore, the reception unit can prioritize reception of access requests to specific AI services based on the user's past access history. This makes it possible to provide the optimal reception method based on the user's past access history.

[0117] The verification unit can estimate the user's emotions and adjust the way the verification process is presented based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible verification process can be provided. If the user is relaxed, a verification process including detailed information can be provided. Furthermore, if the user is in a hurry, a process for quickly carrying out verification can be provided. This makes it possible to provide the verification process in an appropriate way according to the user's emotions.

[0118] The providing unit can estimate the user's emotions and adjust the expression method of the AI ​​service it provides based on the estimated user's emotions. For example, if the user is relaxed, it can provide an AI service that proceeds at a leisurely pace. If the user is in a hurry, it can also provide an AI service that provides information quickly. Furthermore, if the user is excited, it can also provide an AI service that adds visually stimulating effects. This makes it possible to provide an AI service with an appropriate expression method according to the user's emotions.

[0119] The management unit can estimate the user's emotions and select information to manage based on the estimated user emotions. For example, if the user is feeling stressed, only important information can be managed. Also, if the user is relaxed, detailed information can be managed. Furthermore, if the user is in a hurry, information that needs to be managed quickly can be prioritized. This makes it possible to manage appropriate information according to the user's emotions.

[0120] The description unit can estimate the user's emotions and adjust the way information is expressed based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible expression method can be provided. If the user is relaxed, an expression method including detailed information can be provided. Furthermore, if the user is in a hurry, an expression method that focuses on the main points can be provided. This allows information to be written in an appropriate expression method according to the user's emotions.

[0121] During verification, the verification unit can adjust the level of verification detail based on the importance of the AI ​​service. For example, for important AI services, detailed verification can be performed. For general AI services, normal verification can also be performed. Furthermore, for low-importance AI services, simplified verification can also be performed. This allows verification to be performed at an appropriate level of detail according to the importance of the AI ​​service.

[0122] The provision unit can apply different provision algorithms depending on the category of the AI ​​service when providing the service. For example, a strict provision algorithm can be applied to security-related AI services. A flexible provision algorithm can also be applied to entertainment-related AI services. Furthermore, a provision algorithm according to business requirements can also be applied to business-related AI services. This makes it possible to apply an appropriate provision algorithm depending on the category of the AI ​​service.

[0123] During management, the management unit can optimize the management algorithm by referring to past management data. For example, the optimal management algorithm can be selected based on past management data. It can also extract specific patterns from past management data and optimize the management algorithm. Furthermore, it can analyze past management data and improve the management process. This makes it possible to optimize the management algorithm based on past management data.

[0124] The description unit can adjust the level of detail of the description based on the importance of the verification process when describing it. For example, a detailed description can be provided for an important verification process. A normal description can also be provided for a general verification process. Furthermore, a simplified description can also be provided for a verification process of low importance. This allows the description to be provided with an appropriate level of detail according to the importance of the verification process.

[0125] The provision unit can improve the accuracy of provision by referring to the user's past provision results when providing information. For example, the provision algorithm can be optimized based on the results of AI services the user has used in the past. It can also extract specific patterns from the user's past provision results to improve the accuracy of provision. Furthermore, it can analyze the user's past provision results and improve the provision process. This makes it possible to improve the accuracy of provision based on the user's past provision results.

[0126] The processing flow of the second embodiment will be briefly explained below.

[0127] Step 1: The reception unit receives access from a user. The access from a user includes, but is not limited to, web access and API requests. Step 2: The verification unit uses blockchain technology to verify the legitimacy of the AI ​​service based on the information received by the reception unit. For example, the verification unit determines whether the AI ​​service is legitimate by checking whether the provider of the AI ​​service is registered on the blockchain. The processing in the verification unit may be performed using AI or may be performed without using AI. Step 3: The provider provides a reliable AI service based on the results of verification by the verification unit. For example, if the provider confirms that the AI ​​service is legitimate, it provides the user with a link to access the AI ​​service.

[0128] 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.

[0129] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] 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.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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."

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] [Explanation of symbols]

[0200] 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 reception unit that receives access from a user; A verification unit that verifies the legitimacy of the AI ​​service using blockchain technology based on the information received by the reception unit; A providing unit that provides a reliable AI service based on the results of verification by the verification unit. A system characterized by:

2. The verification unit Using blockchain technology to verify the source of AI services with high accuracy 2. The system of claim 1.

3. The providing unit Providing a link to the user for accessing the AI ​​service whose legitimacy has been confirmed by the verification unit 2. The system of claim 1.

4. Equipped with a management department that manages information registered on the blockchain 2. The system of claim 1.

5. A description section is provided for describing the details of the verification process.

2. The system of claim 1.

6. The reception unit Estimates user emotions and adjusts access acceptance timing based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past access history and select the optimal reception method 2. The system of claim 1.

8. The reception unit Filtering access requests based on the user's current device and network conditions 2. The system of claim 1.

9. The reception unit When accepting access, the most appropriate acceptance method is selected according to the user's input method.

2. The system of claim 1.

10. The reception unit Estimate the user's emotions and determine the priority of accepted access based on the estimated user emotions.

2. The system of claim 1.

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