system

Abstract

An object of a system according to an embodiment is to return a reward to a model generation user by using a rightful generative AI model.SOLUTION: A system includes a right check unit, a return unit, and a provision unit. The rights checking unit checks the rights status of the generated AI model. The return unit returns a reward to the model generation user according to use of the generated AI model whose right is determined to be clean by the right check unit. The provision unit provides a GPU server AI that provides computational resources necessary for using the generative environmental model.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 has faced the challenge of making it difficult to properly manage the rights status of generative AI models and to use only rights-clean models.

[0005] The system according to the embodiment aims to utilize a rights-clean generative AI model and return rewards to the model generation user. [Means for solving the problem]

[0006] The system according to the embodiment includes a rights check unit, a refund unit, and a providing unit. The rights check unit checks the rights status of the generative AI model. The refund unit returns rewards to the model generation user in accordance with the use of the generative AI model that has been determined to be rights-clean by the rights check unit. The providing unit provides a GPU server environment that provides the computational resources necessary for using the generative AI model. [Effects of the Invention]

[0007] The system according to the embodiment utilizes a rights-clean generative AI model and can return rewards to model generation users. [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) The generative AI aggregation system according to an embodiment of the present invention is a system that handles rights-clean generative AI models, returns rewards to model generation users, and provides a GPU server environment. As a result, the generative AI aggregation system promotes the use of rights-clean generative AI models and promotes the spread of generative AI in the B2B field.

[0029] A generative AI aggregation system according to an embodiment includes a rights check unit, a refund unit, and a providing unit. The rights check unit checks the rights status of the generative AI model. For example, the rights check unit obtains rights information for the generative AI model from a database and evaluates the risk of rights infringement. The rights check unit also checks permission information from the rights holder of the generative AI model and selects a rights-clean model. The rights check unit also checks the open source license of the generative AI model and evaluates whether it meets appropriate license conditions. For example, the rights check unit references a patent database to check whether the generative AI model infringes patent rights. The refund unit refunds a reward to a model generation user based on the use of a generative AI model determined by the rights check unit to be rights-clean. For example, the refund unit calculates a reward based on the number of times the generative AI model is used and pays it to the model generation user. The refund unit also calculates a reward based on the usage time of the generative AI model and pays it to the model generation user. The refund unit also adjusts the reward based on user evaluations of the generative AI model and pays it to the model generation user. For example, the refund unit tracks the number of times the generative AI model is used and automatically calculates a reward based on the number of times the generative AI model is used. The provision unit provides a GPU server environment that provides the computational resources necessary for using the generative AI model. For example, the provision unit provides a server equipped with a high-performance GPU on the cloud, allowing users to use it when needed. The provision unit also monitors the usage status of the GPU server environment and efficiently allocates resources. The provision unit also automatically calculates and bills users for the usage fee of the GPU server environment. For example, the provision unit monitors the usage status of the GPU server in real time and prevents overuse of resources. This allows the generative AI aggregation system according to the embodiment to promote the use of rights-clean generative AI models and promote the spread of generative AI in the B2B field. For example, the generative AI aggregation system helps companies use generative AI to improve business efficiency and develop new services. The generative AI aggregation system tracks the usage status of generative AI models, allowing companies to evaluate the effectiveness of generative AI.The Generative AI Aggregation System collects data on the use of generative AI models, allowing companies to quantitatively evaluate the effectiveness of introducing generative AI.

[0030] The rights checking unit can use blockchain technology to make the rights status of the generative AI model transparent and traceable in real time. For example, the rights checking unit records the rights information of the generative AI model on a blockchain and makes it publicly accessible. This makes it possible to check the rights status of the model in real time. The rights checking unit also uses blockchain technology to track the history of rights changes to the generative AI model, making it possible to check the past rights status. For example, if the rights holder of the model is changed, this history is recorded on the blockchain. The rights checking unit also records the rights information of the generative AI model on the blockchain to reduce the risk of rights infringement. For example, information officially authorized by the rights holder of the model is recorded on the blockchain to reduce the risk of rights infringement. This makes the rights status of the generative AI model transparent and traceable in real time, reducing the risk of rights infringement.

[0031] The rights check unit can use the generative AI to learn from past rights infringement cases and automate risk assessment. The rights check unit, for example, uses the generative AI to learn from past rights infringement cases and use this to help select a rights-clean model. For example, the past rights infringement cases are compiled into a database, and the generative AI performs risk assessment based on that data. The rights check unit also uses the generative AI to automate the selection process for rights-clean models. For example, the generative AI learns from past rights infringement cases and performs risk assessment to automatically select a rights-clean model. The rights check unit also uses the generative AI to dynamically adjust the selection criteria for rights-clean models. For example, the generative AI learns from past rights infringement cases and performs risk assessment to dynamically adjust the selection criteria. In this way, the process of selecting rights-clean models is made more efficient by using the generative AI to learn from past rights infringement cases and automate risk assessment.

[0032] The rights check unit can apply the selection criteria for the rights-clean model to other digital content, thereby building a wide-ranging digital rights management system. The rights check unit can also apply the selection criteria for the rights-clean model to other digital content, such as music and video, thereby building a wide-ranging digital rights management system. For example, it can strictly check the rights status of music and video and handle only rights-clean content. The rights check unit can also apply the selection criteria for the rights-clean model to other digital content, thereby integrating digital rights management systems. For example, it can centrally manage rights information for music and video and provide only rights-clean content. The rights check unit can also apply the selection criteria for the rights-clean model to other digital content, thereby expanding the digital rights management system. For example, it can track the rights status of music and video in real time and provide only rights-clean content. In this way, the selection criteria for the rights-clean model can be applied to other digital content, thereby building a wide-ranging digital rights management system.

[0033] The rights checking department can conduct the selection process for rights-clean models in collaboration with experts from different industries and evaluate them from a cross-industry perspective. For example, the rights checking department conducts the selection process for rights-clean models in collaboration with experts from different industries and evaluates them from a cross-industry perspective. For example, technical, legal, and business experts jointly evaluate the rights status of models. The rights checking department also formulates selection criteria for rights-clean models in collaboration with experts from different industries. For example, technical experts evaluate from a technical perspective, and legal experts evaluate from a legal perspective. The rights checking department also improves the selection process for rights-clean models from a cross-industry perspective. For example, experts from different industries hold regular meetings to evaluate the rights status of models. In this way, collaborative evaluation with experts from different industries increases the reliability of the selection process for rights-clean models.

[0034] The reward unit can use the generation AI to analyze the model usage status in detail and develop an algorithm for optimal reward allocation. The reward unit, for example, uses the generation AI to analyze the model usage status in detail and develop an algorithm for optimal reward allocation. For example, rewards are allocated based on the number of times the model is used, the usage time, user ratings, etc. The reward unit also uses the generation AI to build a system that monitors the model usage status in real time and dynamically adjusts reward allocation. For example, reward allocation is automatically adjusted according to usage status. The reward unit also uses the generation AI to analyze the model usage status and develop an algorithm that optimizes reward allocation. For example, reward allocation is optimized based on user feedback and ratings. In this way, the generation AI can be used to analyze the model usage status in detail and optimal reward allocation is achieved, thereby increasing the motivation of model generation users.

[0035] The provision unit can optimize the usage status of the GPU server environment using the generation AI and achieve efficient resource allocation. For example, the provision unit uses the generation AI to monitor the usage status of the GPU server environment in real time and build a system that efficiently allocates resources. For example, it dynamically adjusts resources according to the usage status. The provision unit also uses the generation AI to analyze the usage patterns of the GPU server environment and develops an algorithm that optimally allocates resources. For example, it optimizes resources based on user behavior patterns. The provision unit also uses the generation AI to predict the usage status of the GPU server environment and develops a system that efficiently allocates resources. For example, it predicts user demand and allocates resources in advance. In this way, the generation AI is used to optimize the usage status of the GPU server environment and achieve efficient resource allocation, thereby improving user convenience.

[0036] The providing unit can provide high-performance GPU servers over a distributed network, allowing users to select a geographically nearby server. The providing unit, for example, builds a system that provides high-performance GPU servers over a distributed network and allows users to select a geographically nearby server. For example, the providing unit selects the optimal server based on the user's location information. The providing unit also uses the distributed network to provide high-performance GPU servers and allow users to use geographically nearby servers. For example, multiple data centers are installed to allocate resources near the users. The providing unit also develops a system that provides high-performance GPU servers over a distributed network and allows users to select a geographically nearby server. For example, a geographically nearby server is selected to optimize the user's access speed. This optimizes access speed by providing high-performance GPU servers over a distributed network and allowing users to select a geographically nearby server.

[0037] The provider can provide a GPU server environment in combination with other high-performance computing resources. For example, the provider builds a system that provides a GPU server environment in combination with other high-performance computing resources such as a quantum computer. For example, the provider allows users to select the computing resources they need. The provider also integrates high-performance computing resources and provides them in combination with a GPU server environment. For example, the provider links a quantum computer and a GPU server to efficiently process complex computing tasks. The provider also provides a GPU server environment in combination with other high-performance computing resources, allowing users to use a variety of computing resources. For example, the provider integrates resources such as a quantum computer and an FPGA. By providing a GPU server environment in combination with other high-performance computing resources, the provider allows users to use a variety of computing resources.

[0038] The provision unit can refer to computing resource provision models in different industries and design a GPU server environment for generative AI from multiple perspectives. The provision unit, for example, refers to computing resource provision models in different industries and designs a GPU server environment for generative AI from multiple perspectives. For example, it refers to cloud computing and edge computing models. The provision unit also analyzes computing resource provision models in different industries and applies them to the GPU server environment for generative AI. For example, it designs a GPU server environment based on a cloud service provider model. The provision unit also incorporates computing resource provision models in different industries to improve the GPU server environment for generative AI. For example, it refers to an edge computing model to optimize resource allocation. In this way, by referring to computing resource provision models in different industries, it is possible to design a GPU server environment for generative AI from multiple perspectives.

[0039] The provision unit can use the generation AI to analyze the user's usage history in the generated AISaaS service and propose an optimal service plan. The provision unit, for example, uses the generation AI to analyze the user's usage history of the generated AISaaS service and build a system that proposes an optimal service plan. For example, the provision unit customizes a service plan based on the user's usage patterns. The provision unit also uses the generation AI to monitor the user's usage history of the generated AISaaS service in real time and proposes an optimal service plan. For example, the service plan is dynamically adjusted depending on the usage situation. The provision unit also uses the generation AI to analyze the user's usage history of the generated AISaaS service and develop an algorithm that proposes an optimal service plan. For example, the service plan is optimized based on the user's behavioral patterns. In this way, the user's convenience is improved by using the generation AI to analyze the user's usage history and propose an optimal service plan.

[0040] The provision unit can integrate the generative AISaaS service with other cloud services to provide a comprehensive cloud solution. For example, the provision unit integrates the generative AISaaS service with other cloud services such as data storage and analysis tools to build a system that provides a comprehensive cloud solution. For example, the provision unit allows users to use multiple services on a single platform. The provision unit also integrates the generative AISaaS service with other cloud services to enable users to utilize generative AI while using data storage and analysis tools. For example, the provision unit collaborates with a data storage service to efficiently manage training data for generative AI models. The provision unit also integrates the generative AISaaS service with other cloud services to enable users to use comprehensive cloud solutions. For example, the provision unit collaborates with analysis tools to efficiently analyze the results of generative AI models. In this way, by integrating the generative AISaaS service with other cloud services, users can use comprehensive cloud solutions.

[0041] The provision unit can refer to SaaS models from different industries and design the generated AISaaS service from multiple angles. The provision unit, for example, refers to SaaS models from different industries and designs the generated AISaaS service from multiple angles. For example, it refers to enterprise SaaS models and consumer SaaS models. The provision unit also analyzes SaaS models from different industries and applies them to the generated AISaaS service. For example, it designs the functions of the generated AISaaS service based on the enterprise SaaS model. The provision unit also incorporates SaaS models from different industries to improve the generated AISaaS service. For example, it optimizes the user interface by referring to consumer SaaS models. In this way, the generated AISaaS service can be designed from multiple angles by referring to SaaS models from different industries.

[0042] The provision department can develop tools that use generative AI to optimize business processes in order to promote the spread of generative AI in the BtoB area. The provision department, for example, uses generative AI to develop tools that optimize business processes in the BtoB area. For example, by automating tasks such as data entry and report creation. The provision department also uses generative AI to develop tools that improve the efficiency of business processes in the BtoB area. For example, by optimizing business flows and resource allocation. The provision department also uses generative AI to develop tools that improve business processes in the BtoB area. For example, by identifying bottlenecks in business processes and proposing improvement measures. In this way, developing tools that optimize business processes using generative AI will promote the spread of generative AI in the BtoB area.

[0043] The provision department can build a system that uses the generation AI to automatically generate metrics for quantitatively evaluating the effects of introducing the generation AI. For example, the provision department uses the generation AI to build a system that automatically generates metrics for quantitatively evaluating the effects of introducing the generation AI in the BtoB area. For example, the effects of business efficiency improvement and cost reduction are quantified. The provision department also uses the generation AI to develop a system that monitors the effects of introducing the generation AI in the BtoB area in real time and automatically generates metrics. For example, the effects of introduction are quantitatively evaluated and reports are automatically generated. The provision department also uses the generation AI to analyze the effects of introducing the generation AI in the BtoB area and develop an algorithm that automatically generates metrics. For example, the effects of improving business processes are quantified and evaluated. In this way, the effects of introducing the generation AI in the BtoB area are clarified by using the generation AI to automatically generate metrics that quantitatively evaluate the effects of introduction.

[0044] The provision department can build and share a database that collects success stories from different industries in order to promote the spread of generative AI in the BtoB realm. For example, the provision department can build a database that collects success stories of generative AI implementation from different industries to promote the spread of generative AI in the BtoB realm. For example, the success stories can be organized by category so that companies can use them as reference. The provision department can also develop a system that collects success stories of generative AI implementation from different industries, compiles them into a database, and shares them. For example, detailed information about the success stories can be registered in the database so that companies can access it. The provision department can also build a database that collects success stories of generative AI implementation from different industries to allow companies to use it as a reference for introducing generative AI. For example, the analysis results of the success stories can be registered in the database so that companies can use it. In this way, building and sharing a database of success stories from different industries will promote the spread of generative AI in the BtoB realm.

[0045] The provision unit can provide customizable generative AI solutions that meet the needs of different industries. For example, the provision unit builds a system that provides customizable generative AI solutions that meet the needs of different industries. For example, it provides generative AI models that meet the specific requirements of each industry. The provision unit also analyzes the needs of different industries and develops customizable generative AI solutions that meet the needs of different industries. For example, it provides generative AI models that use industry-specific datasets. The provision unit also provides customizable generative AI solutions that meet the needs of different industries, enabling companies to effectively utilize generative AI. For example, it provides generative AI models that meet the specific challenges of each industry. In this way, customizable generative AI solutions that meet the needs of different industries are provided, enabling companies to effectively utilize generative AI.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The rights checking unit can use blockchain technology to make the rights status of the generative AI model transparent and traceable in real time. For example, the rights information of the generative AI model can be recorded on the blockchain and made public in a form accessible to anyone. This makes it possible to check the rights status of the model in real time. The rights checking unit also uses blockchain technology to track the history of changes to the rights of the generative AI model, making it possible to check the past rights status. For example, if the rights holder of the model is changed, this history is recorded on the blockchain. The rights checking unit also records the rights information of the generative AI model on the blockchain to reduce the risk of rights infringement. For example, information officially authorized by the rights holder of the model can be recorded on the blockchain to reduce the risk of rights infringement. This makes the rights status of the generative AI model transparent and traceable in real time, reducing the risk of rights infringement.

[0048] The rights check unit can use the generative AI to learn from past cases of rights infringement and automate risk assessment. For example, past cases of rights infringement are compiled into a database, and the generative AI performs risk assessment based on that data. The rights check unit also uses the generative AI to automate the process of selecting a rights-clean model. For example, the generative AI learns from past cases of rights infringement and performs risk assessment to automatically select a rights-clean model. The rights check unit also uses the generative AI to dynamically adjust the selection criteria for rights-clean models. For example, the generative AI learns from past cases of rights infringement and performs risk assessment to dynamically adjust the selection criteria. In this way, the process of selecting a rights-clean model is made more efficient by using the generative AI to learn from past cases of rights infringement and automate risk assessment.

[0049] The rights checking unit can apply the selection criteria for the rights-clean model to other digital content, thereby building a wide-ranging digital rights management system. For example, the selection criteria for the rights-clean model can be applied to other digital content such as music and video, thereby building a wide-ranging digital rights management system. For example, the rights status of music and video can be strictly checked, and only rights-clean content can be handled. The rights checking unit can also apply the selection criteria for the rights-clean model to other digital content, thereby integrating digital rights management systems. For example, the rights information for music and video can be centrally managed, and only rights-clean content can be provided. The rights checking unit can also apply the selection criteria for the rights-clean model to other digital content, thereby expanding the digital rights management system. For example, the rights status of music and video can be tracked in real time, and only rights-clean content can be provided. In this way, the selection criteria for the rights-clean model can be applied to other digital content, thereby building a wide-ranging digital rights management system.

[0050] The Rights Checking Department can conduct the selection process for rights-clean models in collaboration with experts from different industries and evaluate them from a cross-industry perspective. For example, the selection process for rights-clean models is conducted in collaboration with experts from different industries and evaluation is performed from a cross-industry perspective. For example, technical, legal, and business experts jointly evaluate the rights status of models. The Rights Checking Department also formulates selection criteria for rights-clean models in collaboration with experts from different industries. For example, technical experts evaluate from a technical perspective, and legal experts evaluate from a legal perspective. The Rights Checking Department also improves the selection process for rights-clean models from a cross-industry perspective. For example, experts from different industries hold regular meetings to evaluate the rights status of models. In this way, collaborative evaluation with experts from different industries increases the reliability of the selection process for rights-clean models.

[0051] The rewards section can use the generation AI to analyze the model usage status in detail and develop an algorithm for optimal reward allocation. For example, the generation AI can be used to analyze the model usage status in detail and develop an algorithm for optimal reward allocation. For example, rewards can be allocated based on the number of times the model is used, the usage time, user ratings, etc. The rewards section can also use the generation AI to build a system that monitors the model usage status in real time and dynamically adjusts reward allocation. For example, reward allocation can be automatically adjusted according to usage status. The rewards section can also use the generation AI to analyze the model usage status and develop an algorithm that optimizes reward allocation. For example, reward allocation can be optimized based on user feedback and ratings. In this way, the generation AI can be used to analyze the model usage status in detail and optimal reward allocation can be achieved, thereby increasing the motivation of model generation users.

[0052] The provision unit can use the generation AI to optimize the usage of the GPU server environment and achieve efficient resource allocation. For example, the generation AI can be used to monitor the usage of the GPU server environment in real time and build a system that efficiently allocates resources. For example, resources can be dynamically adjusted according to usage. The provision unit can also use the generation AI to analyze the usage patterns of the GPU server environment and develop an algorithm that optimally allocates resources. For example, resources can be optimized based on user behavior patterns. The provision unit can also use the generation AI to predict the usage of the GPU server environment and develop a system that efficiently allocates resources. For example, user demand can be predicted and resources can be allocated in advance. In this way, the generation AI can be used to optimize the usage of the GPU server environment and achieve efficient resource allocation, thereby improving user convenience.

[0053] The provider can provide high-performance GPU servers over a distributed network, allowing users to select a geographically nearby server. For example, a system is constructed in which high-performance GPU servers are provided over a distributed network, allowing users to select a geographically nearby server. For example, the optimal server is selected based on the user's location information. The provider also uses the distributed network to provide high-performance GPU servers, allowing users to use geographically nearby servers. For example, multiple data centers are installed to allocate resources close to users. The provider also develops a system in which high-performance GPU servers are provided over a distributed network, allowing users to select a geographically nearby server. For example, a geographically nearby server is selected to optimize user access speed. This optimizes access speed by providing high-performance GPU servers over a distributed network and allowing users to select a geographically nearby server.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The rights checking unit checks the rights status of the generative AI model. Specifically, the rights checking unit retrieves the rights information of the generative AI model from a database and evaluates the risk of rights infringement. It also checks the permission information from the rights holder of the generative AI model and selects a rights-clean model. It also checks the open source license of the generative AI model and evaluates whether it meets the appropriate license conditions. For example, it refers to a patent database to check whether the generative AI model infringes patent rights. Step 2: The Reward Unit returns rewards to the model generation user based on the use of the generative AI model that has been determined to be rights-clean by the Rights Check Unit. Specifically, the Reward Unit calculates rewards based on the number of times and duration of use of the generative AI model, and pays them to the model generation user. The Reward Unit also adjusts rewards based on user evaluations of the generative AI model. For example, the Reward Unit tracks the number of times the generative AI model is used, and automatically calculates rewards based on the number of times it is used. Step 3: The provider provides a GPU server environment that provides the computational resources necessary for using the generative AI model. Specifically, it provides servers equipped with high-performance GPUs on the cloud, making them available to users when they need them. It also monitors the usage status of the GPU server environment and allocates resources efficiently. It also automatically calculates and bills users for the usage fee of the GPU server environment. For example, it monitors the usage status of the GPU server in real time to prevent overuse of resources.

[0056] (Example 2) The generative AI aggregation system according to an embodiment of the present invention is a system that handles rights-clean generative AI models, returns rewards to model generation users, and provides a GPU server environment. As a result, the generative AI aggregation system promotes the use of rights-clean generative AI models and promotes the spread of generative AI in the B2B field.

[0057] A generative AI aggregation system according to an embodiment includes a rights check unit, a refund unit, and a providing unit. The rights check unit checks the rights status of the generative AI model. For example, the rights check unit obtains rights information for the generative AI model from a database and evaluates the risk of rights infringement. The rights check unit also checks permission information from the rights holder of the generative AI model and selects a rights-clean model. The rights check unit also checks the open source license of the generative AI model and evaluates whether it meets appropriate license conditions. For example, the rights check unit references a patent database to check whether the generative AI model infringes patent rights. The refund unit refunds a reward to a model generation user based on the use of a generative AI model determined by the rights check unit to be rights-clean. For example, the refund unit calculates a reward based on the number of times the generative AI model is used and pays it to the model generation user. The refund unit also calculates a reward based on the usage time of the generative AI model and pays it to the model generation user. The refund unit also adjusts the reward based on user evaluations of the generative AI model and pays it to the model generation user. For example, the refund unit tracks the number of times the generative AI model is used and automatically calculates a reward based on the number of times the generative AI model is used. The provision unit provides a GPU server environment that provides the computational resources necessary for using the generative AI model. For example, the provision unit provides a server equipped with a high-performance GPU on the cloud, allowing users to use it when needed. The provision unit also monitors the usage status of the GPU server environment and efficiently allocates resources. The provision unit also automatically calculates and bills users for the usage fee of the GPU server environment. For example, the provision unit monitors the usage status of the GPU server in real time and prevents overuse of resources. This allows the generative AI aggregation system according to the embodiment to promote the use of rights-clean generative AI models and promote the spread of generative AI in the B2B field. For example, the generative AI aggregation system helps companies use generative AI to improve business efficiency and develop new services. The generative AI aggregation system tracks the usage status of generative AI models, allowing companies to evaluate the effectiveness of generative AI.The Generative AI Aggregation System collects data on the use of generative AI models, allowing companies to quantitatively evaluate the effectiveness of introducing generative AI.

[0058] The rights checking unit can use blockchain technology to make the rights status of the generative AI model transparent and traceable in real time. For example, the rights checking unit records the rights information of the generative AI model on a blockchain and makes it publicly accessible. This makes it possible to check the rights status of the model in real time. The rights checking unit also uses blockchain technology to track the history of rights changes to the generative AI model, making it possible to check the past rights status. For example, if the rights holder of the model is changed, this history is recorded on the blockchain. The rights checking unit also records the rights information of the generative AI model on the blockchain to reduce the risk of rights infringement. For example, information officially authorized by the rights holder of the model is recorded on the blockchain to reduce the risk of rights infringement. This makes the rights status of the generative AI model transparent and traceable in real time, reducing the risk of rights infringement.

[0059] The rights check unit can use the generative AI to learn from past rights infringement cases and automate risk assessment. The rights check unit, for example, uses the generative AI to learn from past rights infringement cases and use this to help select a rights-clean model. For example, the past rights infringement cases are compiled into a database, and the generative AI performs risk assessment based on that data. The rights check unit also uses the generative AI to automate the selection process for rights-clean models. For example, the generative AI learns from past rights infringement cases and performs risk assessment to automatically select a rights-clean model. The rights check unit also uses the generative AI to dynamically adjust the selection criteria for rights-clean models. For example, the generative AI learns from past rights infringement cases and performs risk assessment to dynamically adjust the selection criteria. In this way, the process of selecting rights-clean models is made more efficient by using the generative AI to learn from past rights infringement cases and automate risk assessment.

[0060] The rights check unit can use the emotion estimation function to analyze the user's emotional response to the rights status of the model and preferentially select a highly reliable model. The rights check unit, for example, uses the emotion estimation function to analyze the user's emotional response to the rights status of the model and preferentially select a highly reliable model. For example, it preferentially selects a model with a high user emotion score. The rights check unit also uses the emotion estimation function to collect the user's emotional response to the rights status of the model in real time and selects a highly reliable model. For example, it evaluates the reliability of the model based on the user's emotional response. The rights check unit also uses the emotion estimation function to analyze the user's emotional response to the rights status of the model and builds a system that selects a highly reliable model. For example, it evaluates the reliability of the model based on the user's emotion score. In this way, the emotion estimation function is used to analyze the user's emotional response and preferentially select a highly reliable model, thereby gaining the user's trust.

[0061] The rights check unit can apply the selection criteria for the rights-clean model to other digital content, thereby building a wide-ranging digital rights management system. The rights check unit can also apply the selection criteria for the rights-clean model to other digital content, such as music and video, thereby building a wide-ranging digital rights management system. For example, it can strictly check the rights status of music and video and handle only rights-clean content. The rights check unit can also apply the selection criteria for the rights-clean model to other digital content, thereby integrating digital rights management systems. For example, it can centrally manage rights information for music and video and provide only rights-clean content. The rights check unit can also apply the selection criteria for the rights-clean model to other digital content, thereby expanding the digital rights management system. For example, it can track the rights status of music and video in real time and provide only rights-clean content. In this way, the selection criteria for the rights-clean model can be applied to other digital content, thereby building a wide-ranging digital rights management system.

[0062] The rights checking department can conduct the selection process for rights-clean models in collaboration with experts from different industries and evaluate them from a cross-industry perspective. For example, the rights checking department conducts the selection process for rights-clean models in collaboration with experts from different industries and evaluates them from a cross-industry perspective. For example, technical, legal, and business experts jointly evaluate the rights status of models. The rights checking department also formulates selection criteria for rights-clean models in collaboration with experts from different industries. For example, technical experts evaluate from a technical perspective, and legal experts evaluate from a legal perspective. The rights checking department also improves the selection process for rights-clean models from a cross-industry perspective. For example, experts from different industries hold regular meetings to evaluate the rights status of models. In this way, collaborative evaluation with experts from different industries increases the reliability of the selection process for rights-clean models.

[0063] The rights check unit can use the emotion estimation function to provide real-time feedback on user emotions in the rights-clean model selection process and dynamically adjust the selection criteria. For example, the rights check unit uses the emotion estimation function to provide real-time feedback on user emotions in the rights-clean model selection process and dynamically adjust the selection criteria. For example, the selection criteria is adjusted based on the user's emotion score. The rights check unit also uses the emotion estimation function to collect user emotional responses in the rights-clean model selection process and dynamically adjust the selection criteria. For example, the selection criteria is improved based on the user's emotional responses. The rights check unit also uses the emotion estimation function to analyze user emotional data in the rights-clean model selection process and dynamically adjust the selection criteria. For example, the selection criteria is optimized based on the user's emotion data. In this way, user emotions are fed back in real time and the selection criteria are dynamically adjusted, thereby increasing user satisfaction.

[0064] The reward unit can use the generation AI to analyze the model usage status in detail and develop an algorithm for optimal reward allocation. The reward unit, for example, uses the generation AI to analyze the model usage status in detail and develop an algorithm for optimal reward allocation. For example, rewards are allocated based on the number of times the model is used, the usage time, user ratings, etc. The reward unit also uses the generation AI to build a system that monitors the model usage status in real time and dynamically adjusts reward allocation. For example, reward allocation is automatically adjusted according to usage status. The reward unit also uses the generation AI to analyze the model usage status and develop an algorithm that optimizes reward allocation. For example, reward allocation is optimized based on user feedback and ratings. In this way, the generation AI can be used to analyze the model usage status in detail and optimal reward allocation is achieved, thereby increasing the motivation of model generation users.

[0065] The return unit can use the emotion estimation function to monitor user satisfaction with the reward system in real time and adjust reward allocation. The return unit, for example, uses the emotion estimation function to build a system that monitors user satisfaction with the reward system in real time and adjusts reward allocation. For example, the return unit adjusts reward allocation based on the user's emotion score. The return unit also uses the emotion estimation function to collect users' emotional responses to the reward system and dynamically adjust reward allocation. For example, the return unit improves reward allocation based on the user's emotional responses. The return unit also uses the emotion estimation function to analyze users' emotional data regarding the reward system and develop a system that optimizes reward allocation. For example, the return unit optimizes reward allocation based on the user's emotional data. In this way, user satisfaction is monitored in real time and reward allocation is adjusted, thereby increasing user satisfaction.

[0066] The provision unit can optimize the usage status of the GPU server environment using the generation AI and achieve efficient resource allocation. For example, the provision unit uses the generation AI to monitor the usage status of the GPU server environment in real time and build a system that efficiently allocates resources. For example, it dynamically adjusts resources according to the usage status. The provision unit also uses the generation AI to analyze the usage patterns of the GPU server environment and develops an algorithm that optimally allocates resources. For example, it optimizes resources based on user behavior patterns. The provision unit also uses the generation AI to predict the usage status of the GPU server environment and develops a system that efficiently allocates resources. For example, it predicts user demand and allocates resources in advance. In this way, the generation AI is used to optimize the usage status of the GPU server environment and achieve efficient resource allocation, thereby improving user convenience.

[0067] The providing unit can provide high-performance GPU servers over a distributed network, allowing users to select a geographically nearby server. The providing unit, for example, builds a system that provides high-performance GPU servers over a distributed network and allows users to select a geographically nearby server. For example, the providing unit selects the optimal server based on the user's location information. The providing unit also uses the distributed network to provide high-performance GPU servers and allow users to use geographically nearby servers. For example, multiple data centers are installed to allocate resources near the users. The providing unit also develops a system that provides high-performance GPU servers over a distributed network and allows users to select a geographically nearby server. For example, a geographically nearby server is selected to optimize the user's access speed. This optimizes access speed by providing high-performance GPU servers over a distributed network and allowing users to select a geographically nearby server.

[0068] The providing unit can use the emotion estimation function to analyze user emotions regarding the experience of using the GPU server and use the results to improve the service. The providing unit, for example, uses the emotion estimation function to analyze user emotions regarding the experience of using the GPU server and builds a system that is useful for improving the service. For example, the service is improved based on the user's emotion score. The providing unit also uses the emotion estimation function to collect user emotional responses regarding the experience of using the GPU server and use the results to improve the service. For example, the service is optimized based on the user's emotional responses. The providing unit also uses the emotion estimation function to analyze user emotional data regarding the experience of using the GPU server and develops a system that is useful for improving the service. For example, the service is optimized based on the user's emotion data. In this way, the emotion estimation function is used to analyze user emotions and use the results to improve the service, thereby increasing user satisfaction.

[0069] The provider can provide a GPU server environment in combination with other high-performance computing resources. For example, the provider builds a system that provides a GPU server environment in combination with other high-performance computing resources such as a quantum computer. For example, the provider allows users to select the computing resources they need. The provider also integrates high-performance computing resources and provides them in combination with a GPU server environment. For example, the provider links a quantum computer and a GPU server to efficiently process complex computing tasks. The provider also provides a GPU server environment in combination with other high-performance computing resources, allowing users to use a variety of computing resources. For example, the provider integrates resources such as a quantum computer and an FPGA. By providing a GPU server environment in combination with other high-performance computing resources, the provider allows users to use a variety of computing resources.

[0070] The provision unit can refer to computing resource provision models in different industries and design a GPU server environment for generative AI from multiple perspectives. The provision unit, for example, refers to computing resource provision models in different industries and designs a GPU server environment for generative AI from multiple perspectives. For example, it refers to cloud computing and edge computing models. The provision unit also analyzes computing resource provision models in different industries and applies them to the GPU server environment for generative AI. For example, it designs a GPU server environment based on a cloud service provider model. The provision unit also incorporates computing resource provision models in different industries to improve the GPU server environment for generative AI. For example, it refers to an edge computing model to optimize resource allocation. In this way, by referring to computing resource provision models in different industries, it is possible to design a GPU server environment for generative AI from multiple perspectives.

[0071] The providing unit can use the emotion estimation function to provide feedback on the user's emotions regarding the GPU server usage experience in real time, thereby improving the quality of the service. For example, the providing unit uses the emotion estimation function to provide feedback on the user's emotions regarding the GPU server usage experience in real time, thereby building a system to improve the quality of the service. For example, the providing unit improves the service based on the user's emotion score. The providing unit also uses the emotion estimation function to collect the user's emotional responses regarding the GPU server usage experience, thereby improving the quality of the service. For example, the providing unit optimizes the service based on the user's emotional responses. The providing unit also uses the emotion estimation function to analyze the user's emotional data regarding the GPU server usage experience, thereby developing a system to improve the quality of the service. For example, the service is optimized based on the user's emotional data. In this way, the emotion estimation function is used to provide feedback on the user's emotions in real time, thereby improving the quality of the service and increasing user satisfaction.

[0072] The provision unit can use the generation AI to analyze the user's usage history in the generated AISaaS service and propose an optimal service plan. The provision unit, for example, uses the generation AI to analyze the user's usage history of the generated AISaaS service and build a system that proposes an optimal service plan. For example, the provision unit customizes a service plan based on the user's usage patterns. The provision unit also uses the generation AI to monitor the user's usage history of the generated AISaaS service in real time and proposes an optimal service plan. For example, the service plan is dynamically adjusted depending on the usage situation. The provision unit also uses the generation AI to analyze the user's usage history of the generated AISaaS service and develop an algorithm that proposes an optimal service plan. For example, the service plan is optimized based on the user's behavioral patterns. In this way, the user's convenience is improved by using the generation AI to analyze the user's usage history and propose an optimal service plan.

[0073] The provision unit can use the emotion estimation function to analyze the user's emotions regarding their usage experience of the generated AISaS service and use the results to improve the service. The provision unit, for example, uses the emotion estimation function to analyze the user's emotions regarding their usage experience of the generated AISaS service and build a system that helps improve the service. For example, the service is improved based on the user's emotion score. The provision unit also uses the emotion estimation function to collect the user's emotional responses regarding their usage experience of the generated AISaS service and use the results to improve the service. For example, the service is optimized based on the user's emotional responses. The provision unit also uses the emotion estimation function to analyze the user's emotional data regarding their usage experience of the generated AISaS service and develop a system that helps improve the service. For example, the service is optimized based on the user's emotional data. In this way, the emotion estimation function is used to analyze the user's emotions and use the results to improve the service, thereby increasing user satisfaction.

[0074] The provision unit can integrate the generative AISaaS service with other cloud services to provide a comprehensive cloud solution. For example, the provision unit integrates the generative AISaaS service with other cloud services such as data storage and analysis tools to build a system that provides a comprehensive cloud solution. For example, the provision unit allows users to use multiple services on a single platform. The provision unit also integrates the generative AISaaS service with other cloud services to enable users to utilize generative AI while using data storage and analysis tools. For example, the provision unit collaborates with a data storage service to efficiently manage training data for generative AI models. The provision unit also integrates the generative AISaaS service with other cloud services to enable users to use comprehensive cloud solutions. For example, the provision unit collaborates with analysis tools to efficiently analyze the results of generative AI models. In this way, by integrating the generative AISaaS service with other cloud services, users can use comprehensive cloud solutions.

[0075] The provision unit can refer to SaaS models from different industries and design the generated AISaaS service from multiple angles. The provision unit, for example, refers to SaaS models from different industries and designs the generated AISaaS service from multiple angles. For example, it refers to enterprise SaaS models and consumer SaaS models. The provision unit also analyzes SaaS models from different industries and applies them to the generated AISaaS service. For example, it designs the functions of the generated AISaaS service based on the enterprise SaaS model. The provision unit also incorporates SaaS models from different industries to improve the generated AISaaS service. For example, it optimizes the user interface by referring to consumer SaaS models. In this way, the generated AISaaS service can be designed from multiple angles by referring to SaaS models from different industries.

[0076] The provision unit can use the emotion estimation function to provide feedback on the user's emotions regarding their usage experience of the generated AISaS service in real time, thereby improving the quality of the service. For example, the provision unit uses the emotion estimation function to build a system that provides feedback on the user's emotions regarding their usage experience of the generated AISaS service in real time, thereby improving the quality of the service. For example, the provision unit improves the service based on the user's emotion score. The provision unit also uses the emotion estimation function to collect the user's emotional responses to their usage experience of the generated AISaS service, thereby improving the quality of the service. For example, the provision unit optimizes the service based on the user's emotional responses. The provision unit also uses the emotion estimation function to analyze the user's emotional data regarding their usage experience of the generated AISaS service, thereby developing a system that improves the quality of the service. For example, the service is optimized based on the user's emotional data. In this way, the emotion estimation function is used to provide feedback on the user's emotions in real time, improving the quality of the service, and thereby increasing user satisfaction.

[0077] The provision department can develop tools that use generative AI to optimize business processes in order to promote the spread of generative AI in the BtoB area. The provision department, for example, uses generative AI to develop tools that optimize business processes in the BtoB area. For example, by automating tasks such as data entry and report creation. The provision department also uses generative AI to develop tools that improve the efficiency of business processes in the BtoB area. For example, by optimizing business flows and resource allocation. The provision department also uses generative AI to develop tools that improve business processes in the BtoB area. For example, by identifying bottlenecks in business processes and proposing improvement measures. In this way, developing tools that optimize business processes using generative AI will promote the spread of generative AI in the BtoB area.

[0078] The provision department can build a system that uses the generation AI to automatically generate metrics for quantitatively evaluating the effects of introducing the generation AI. For example, the provision department uses the generation AI to build a system that automatically generates metrics for quantitatively evaluating the effects of introducing the generation AI in the BtoB area. For example, the effects of business efficiency improvement and cost reduction are quantified. The provision department also uses the generation AI to develop a system that monitors the effects of introducing the generation AI in the BtoB area in real time and automatically generates metrics. For example, the effects of introduction are quantitatively evaluated and reports are automatically generated. The provision department also uses the generation AI to analyze the effects of introducing the generation AI in the BtoB area and develop an algorithm that automatically generates metrics. For example, the effects of improving business processes are quantified and evaluated. In this way, the effects of introducing the generation AI in the BtoB area are clarified by using the generation AI to automatically generate metrics that quantitatively evaluate the effects of introduction.

[0079] The provision department can use the emotion estimation function to analyze a company's emotions regarding the introduction of generative AI and propose implementation support measures. The provision department, for example, uses the emotion estimation function to build a system that analyzes a company's emotions regarding the introduction of generative AI and proposes implementation support measures. For example, it designs implementation support measures based on the company's emotion score. The provision department also uses the emotion estimation function to collect a company's emotional reactions to the introduction of generative AI and proposes implementation support measures. For example, it optimizes the implementation support measures based on the company's emotional reactions. The provision department also uses the emotion estimation function to analyze a company's emotional data regarding the introduction of generative AI and develops a system that proposes implementation support measures. For example, it optimizes the implementation support measures based on the company's emotional data. In this way, the introduction of generative AI is promoted by using the emotion estimation function to analyze a company's emotions and propose implementation support measures.

[0080] The provision department can build and share a database that collects success stories from different industries in order to promote the spread of generative AI in the BtoB realm. For example, the provision department can build a database that collects success stories of generative AI implementation from different industries to promote the spread of generative AI in the BtoB realm. For example, the success stories can be organized by category so that companies can use them as reference. The provision department can also develop a system that collects success stories of generative AI implementation from different industries, compiles them into a database, and shares them. For example, detailed information about the success stories can be registered in the database so that companies can access it. The provision department can also build a database that collects success stories of generative AI implementation from different industries to allow companies to use it as a reference for introducing generative AI. For example, the analysis results of the success stories can be registered in the database so that companies can use it. In this way, building and sharing a database of success stories from different industries will promote the spread of generative AI in the BtoB realm.

[0081] The provision unit can provide customizable generative AI solutions that meet the needs of different industries. For example, the provision unit builds a system that provides customizable generative AI solutions that meet the needs of different industries. For example, it provides generative AI models that meet the specific requirements of each industry. The provision unit also analyzes the needs of different industries and develops customizable generative AI solutions that meet the needs of different industries. For example, it provides generative AI models that use industry-specific datasets. The provision unit also provides customizable generative AI solutions that meet the needs of different industries, enabling companies to effectively utilize generative AI. For example, it provides generative AI models that meet the specific challenges of each industry. In this way, customizable generative AI solutions that meet the needs of different industries are provided, enabling companies to effectively utilize generative AI.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The rights checking unit can use blockchain technology to make the rights status of the generative AI model transparent and traceable in real time. For example, the rights information of the generative AI model can be recorded on the blockchain and made public in a form accessible to anyone. This makes it possible to check the rights status of the model in real time. The rights checking unit also uses blockchain technology to track the history of changes to the rights of the generative AI model, making it possible to check the past rights status. For example, if the rights holder of the model is changed, this history is recorded on the blockchain. The rights checking unit also records the rights information of the generative AI model on the blockchain to reduce the risk of rights infringement. For example, information officially authorized by the rights holder of the model can be recorded on the blockchain to reduce the risk of rights infringement. This makes the rights status of the generative AI model transparent and traceable in real time, reducing the risk of rights infringement.

[0084] The rights check unit can use the generative AI to learn from past cases of rights infringement and automate risk assessment. For example, past cases of rights infringement are compiled into a database, and the generative AI performs risk assessment based on that data. The rights check unit also uses the generative AI to automate the process of selecting a rights-clean model. For example, the generative AI learns from past cases of rights infringement and performs risk assessment to automatically select a rights-clean model. The rights check unit also uses the generative AI to dynamically adjust the selection criteria for rights-clean models. For example, the generative AI learns from past cases of rights infringement and performs risk assessment to dynamically adjust the selection criteria. In this way, the process of selecting a rights-clean model is made more efficient by using the generative AI to learn from past cases of rights infringement and automate risk assessment.

[0085] The rights check unit can use the emotion estimation function to analyze the user's emotional response to the rights status of a model and preferentially select a highly reliable model. For example, the emotion estimation function is used to analyze the user's emotional response to the rights status of a model and preferentially select a highly reliable model. For example, a model with a high user emotion score is preferentially selected. The rights check unit also uses the emotion estimation function to collect the user's emotional response to the rights status of a model in real time and select a highly reliable model. For example, the reliability of the model is evaluated based on the user's emotional response. The rights check unit also uses the emotion estimation function to analyze the user's emotional response to the rights status of a model and build a system that selects a highly reliable model. For example, the reliability of the model is evaluated based on the user's emotion score. In this way, the emotion estimation function is used to analyze the user's emotional response and preferentially select a highly reliable model, thereby gaining the user's trust.

[0086] The rights checking unit can apply the selection criteria for the rights-clean model to other digital content, thereby building a wide-ranging digital rights management system. For example, the selection criteria for the rights-clean model can be applied to other digital content such as music and video, thereby building a wide-ranging digital rights management system. For example, the rights status of music and video can be strictly checked, and only rights-clean content can be handled. The rights checking unit can also apply the selection criteria for the rights-clean model to other digital content, thereby integrating digital rights management systems. For example, the rights information for music and video can be centrally managed, and only rights-clean content can be provided. The rights checking unit can also apply the selection criteria for the rights-clean model to other digital content, thereby expanding the digital rights management system. For example, the rights status of music and video can be tracked in real time, and only rights-clean content can be provided. In this way, the selection criteria for the rights-clean model can be applied to other digital content, thereby building a wide-ranging digital rights management system.

[0087] The Rights Checking Department can conduct the selection process for rights-clean models in collaboration with experts from different industries and evaluate them from a cross-industry perspective. For example, the selection process for rights-clean models is conducted in collaboration with experts from different industries and evaluation is performed from a cross-industry perspective. For example, technical, legal, and business experts jointly evaluate the rights status of models. The Rights Checking Department also formulates selection criteria for rights-clean models in collaboration with experts from different industries. For example, technical experts evaluate from a technical perspective, and legal experts evaluate from a legal perspective. The Rights Checking Department also improves the selection process for rights-clean models from a cross-industry perspective. For example, experts from different industries hold regular meetings to evaluate the rights status of models. In this way, collaborative evaluation with experts from different industries increases the reliability of the selection process for rights-clean models.

[0088] The rights check unit can use the emotion estimation function to provide real-time feedback on user emotions in the rights-clean model selection process and dynamically adjust the selection criteria. For example, the emotion estimation function is used to provide real-time feedback on user emotions in the rights-clean model selection process and dynamically adjust the selection criteria. For example, the selection criteria is adjusted based on the user's emotion score. The rights check unit also uses the emotion estimation function to collect user emotional responses in the rights-clean model selection process and dynamically adjust the selection criteria. For example, the selection criteria is improved based on the user's emotional responses. The rights check unit also uses the emotion estimation function to analyze user emotional data in the rights-clean model selection process and dynamically adjust the selection criteria. For example, the selection criteria is optimized based on the user's emotion data. In this way, user emotions are provided in real time and the selection criteria are dynamically adjusted, thereby increasing user satisfaction.

[0089] The rewards section can use the generation AI to analyze the model usage status in detail and develop an algorithm for optimal reward allocation. For example, the generation AI can be used to analyze the model usage status in detail and develop an algorithm for optimal reward allocation. For example, rewards can be allocated based on the number of times the model is used, the usage time, user ratings, etc. The rewards section can also use the generation AI to build a system that monitors the model usage status in real time and dynamically adjusts reward allocation. For example, reward allocation can be automatically adjusted according to usage status. The rewards section can also use the generation AI to analyze the model usage status and develop an algorithm that optimizes reward allocation. For example, reward allocation can be optimized based on user feedback and ratings. In this way, the generation AI can be used to analyze the model usage status in detail and optimal reward allocation can be achieved, thereby increasing the motivation of model generation users.

[0090] The return unit can use the emotion estimation function to monitor user satisfaction with the reward system in real time and adjust reward allocation. For example, a system is constructed that uses the emotion estimation function to monitor user satisfaction with the reward system in real time and adjust reward allocation. For example, reward allocation is adjusted based on the user's emotion score. The return unit also uses the emotion estimation function to collect users' emotional responses to the reward system and dynamically adjust reward allocation. For example, reward allocation is improved based on the user's emotional responses. The return unit also uses the emotion estimation function to analyze users' emotional data regarding the reward system and develop a system that optimizes reward allocation. For example, reward allocation is optimized based on the user's emotional data. In this way, user satisfaction is monitored in real time and reward allocation is adjusted, thereby increasing user satisfaction.

[0091] The provision unit can use the generation AI to optimize the usage of the GPU server environment and achieve efficient resource allocation. For example, the generation AI can be used to monitor the usage of the GPU server environment in real time and build a system that efficiently allocates resources. For example, resources can be dynamically adjusted according to usage. The provision unit can also use the generation AI to analyze the usage patterns of the GPU server environment and develop an algorithm that optimally allocates resources. For example, resources can be optimized based on user behavior patterns. The provision unit can also use the generation AI to predict the usage of the GPU server environment and develop a system that efficiently allocates resources. For example, user demand can be predicted and resources can be allocated in advance. In this way, the generation AI can be used to optimize the usage of the GPU server environment and achieve efficient resource allocation, thereby improving user convenience.

[0092] The provider can provide high-performance GPU servers over a distributed network, allowing users to select a geographically nearby server. For example, a system is constructed in which high-performance GPU servers are provided over a distributed network, allowing users to select a geographically nearby server. For example, the optimal server is selected based on the user's location information. The provider also uses the distributed network to provide high-performance GPU servers, allowing users to use geographically nearby servers. For example, multiple data centers are installed to allocate resources close to users. The provider also develops a system in which high-performance GPU servers are provided over a distributed network, allowing users to select a geographically nearby server. For example, a geographically nearby server is selected to optimize user access speed. This optimizes access speed by providing high-performance GPU servers over a distributed network and allowing users to select a geographically nearby server.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The rights checking unit checks the rights status of the generative AI model. Specifically, the rights checking unit retrieves the rights information of the generative AI model from a database and evaluates the risk of rights infringement. It also checks the permission information from the rights holder of the generative AI model and selects a rights-clean model. It also checks the open source license of the generative AI model and evaluates whether it meets the appropriate license conditions. For example, it refers to a patent database to check whether the generative AI model infringes patent rights. Step 2: The Reward Unit returns rewards to the model generation user based on the use of the generative AI model that has been determined to be rights-clean by the Rights Check Unit. Specifically, the Reward Unit calculates rewards based on the number of times and duration of use of the generative AI model, and pays them to the model generation user. The Reward Unit also adjusts rewards based on user evaluations of the generative AI model. For example, the Reward Unit tracks the number of times the generative AI model is used, and automatically calculates rewards based on the number of times it is used. Step 3: The provider provides a GPU server environment that provides the computational resources necessary for using the generative AI model. Specifically, it provides servers equipped with high-performance GPUs on the cloud, making them available to users when they need them. It also monitors the usage status of the GPU server environment and allocates resources efficiently. It also automatically calculates and bills users for the usage fee of the GPU server environment. For example, it monitors the usage status of the GPU server in real time to prevent overuse of resources.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the robot 414, 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 robot 414 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 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 rights checking unit that checks the rights status of the generated AI model; A reward unit that returns rewards to model generation users in accordance with the use of the generation AI model that has been determined to be rights-clean by the rights check unit; a provision unit that provides a GPU server environment that provides the computational resources necessary for using the generation AI model; A system characterized by:

2. The right check unit The rights status of generated AI models will be made transparent and traceable in real time using blockchain technology.

2. The system of claim 1.

3. The right check unit Generative AI learns from past infringement cases and automates risk assessment 2. The system of claim 1.

4. The right check unit Analyze users' emotional reactions to the rights status of models and prioritize reliable models.

2. The system of claim 1.

5. The right check unit Apply the selection criteria for rights-clean models to other digital content and build a comprehensive digital rights management system.

2. The system of claim 1.

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