system

Abstract

The system according to this embodiment aims to make highly specialized AI easily accessible. [Solution] The system according to the embodiment comprises a generation unit, a publishing unit, a registration unit, and a collection unit. The generation unit creates expert AI using generation AI. The publishing unit publishes the expert AI created by the generation unit in a store. The registration unit registers highly specialized data. The collection unit collects usage fees for the AI.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to easily use a highly specialized AI in a scene where one wants to consult an expert.

[0005] The system according to the embodiment aims to enable easy use of a highly specialized AI.

Means for Solving the Problems

[0006] The system according to the embodiment includes a generation unit, a publication unit, a registration unit, and a collection unit. The generation unit creates an expert AI using a generation AI. The publication unit publishes the expert AI created by the generation unit in a store. The registration unit registers highly specialized data. The collection unit collects usage fees for the AI.

Effects of the Invention

[0007] The system according to this embodiment can make highly specialized AI easily available. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The expert AI store system according to an embodiment of the present invention is a system that provides AI services for a wide range of applications by creating expert AIs that have been trained on highly specialized data based on generative AI and publishing them in a store. This expert AI store system uses large-scale models such as generative AI to create expert AIs that have been trained on highly specialized data. These expert AIs can be given individuality by designing things like photos, illustrations, and personalities. Next, the created expert AIs are published in a store and made available to a wide range of people. The store also provides a function to register highly specialized data, and data providers can earn rewards by registering data. In addition, a usage fee is collected for the use of the AI, such as through a subscription, and a portion of the usage fee is paid as rewards to the AI ​​creators and data providers. This builds an ecosystem for expert AIs. For example, expert AIs are created using large-scale models such as generative AIs. In this case, by training the AIs on highly specialized data, AIs specialized in specific fields can be created. For example, AIs specialized in the medical field or AIs specialized in the legal field can be considered. This allows users to use the AI ​​as if they were consulting with an expert. Next, the created expert AIs are published in a store. In the store, users can search for and use expert AIs. For example, a user can search for an AI specialized in the medical field and receive health advice. The store also allows users to personalize expert AIs by designing their photos, illustrations, and personalities. This enables users to choose expert AIs that suit their preferences. Furthermore, the store provides a function for registering highly specialized data. Data providers can earn rewards by registering their highly specialized data. For example, by registering medical data or legal data, expert AIs can be created using that data. This allows for the collection of highly specialized data, enabling the creation of higher-quality expert AIs. In addition, a fee is charged for using the AIs, such as a subscription. Users can use expert AIs by paying a monthly fee. A portion of this fee is paid as compensation to the AI ​​creators and data providers.This creates an ecosystem for expert AI, allowing AI creators and data providers to earn rewards. This system enables the provision of generative AI-based expert AI for a wide range of applications, allowing users to utilize AI as if they were consulting with an expert. Furthermore, data providers can earn rewards by registering highly specialized data. This creates an ecosystem for expert AI, allowing AI creators and data providers to earn rewards. As a result, the expert AI store system provides expert AI for a wide range of applications, allowing users to utilize AI as if they were consulting with an expert.

[0029] The expert AI store system according to this embodiment comprises a generation unit, a publishing unit, a registration unit, and a collection unit. The generation unit creates expert AI using a generation AI. The generation unit, for example, uses the generation AI to create expert AI specialized in a specific field. For example, the generation unit can create AI specialized in the medical field. The generation unit can also create AI specialized in the legal field. Furthermore, the generation unit can also create AI specialized in the education field. For example, the generation unit can create AI specialized in the medical field by training it with medical data. The generation unit can also create AI specialized in the legal field by training it with legal data. The generation unit can also create AI specialized in the education field by training it with educational data. Some or all of the above-described processes in the generation unit are performed using the generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or an image generation AI, but is not limited to such examples. The publishing unit publishes the expert AI created by the generation unit in the store. The publishing unit can personalize the expert AI by, for example, designing its photo, illustration, personality, etc. The publishing unit can, for example, design a photo for the expert AI. Furthermore, the Public Section can also design illustrations of expert AI. In addition, the Public Section can also design the personality of the expert AI. For example, the Public Section can design high-resolution photographs of the expert AI. The Public Section can also design illustrations of the expert AI in various styles. The Public Section can also design the personality of the expert AI to suit the user's preferences. Some or all of the above processes in the Public Section may be performed using AI or not. For example, the Public Section can input the design of the expert AI's photograph or illustration into the AI ​​and have the AI ​​generate the design. The Registration Section provides a function for registering highly specialized data. For example, the Registration Section can allow data providers to register highly specialized data. For example, the Registration Section can register medical data. The Registration Section can also register legal data. Furthermore, the Registration Section can also register educational data. For example, the Registration Section can register medical data in a database.The registration unit can also register legal data in the database. The registration unit can also register educational data in the database. Some or all of the above processes in the registration unit may be performed using AI or not. For example, the registration unit can input data registered by data providers into the AI ​​and have the AI ​​perform the data registration. The collection unit collects fees for using the AI. The collection unit collects fees for using the AI, for example, through a subscription. The collection unit can collect monthly fees, for example. The collection unit can also collect annual fees. Furthermore, the collection unit can collect fees on a pay-per-use basis. For example, the collection unit can collect monthly fees by credit card. The collection unit can also collect annual fees by bank transfer. The collection unit can also collect fees on a pay-per-use basis using electronic money. Some or all of the above processes in the collection unit may be performed using AI or not. For example, the collection department can input the collection of usage fees into the AI ​​and have the AI ​​perform the collection. As a result, the expert AI store system according to this embodiment can create expert AI using the generation AI and publish it in the store, thereby providing AI services for a wide range of applications.

[0030] The generation unit creates expert AI using generative AI. For example, the generation unit uses generative AI to create expert AI specialized in a specific field. For instance, the generation unit can create AI specialized in the medical field. It can also create AI specialized in the legal field. Furthermore, it can create AI specialized in the educational field. For example, the generation unit creates AI specialized in the medical field by training it with medical data. It can also create AI specialized in the legal field by training it with legal data. It can also create AI specialized in the educational field by training it with educational data. Some or all of the above-described processes in the generation unit are performed using generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or image generation AI, but is not limited to these examples. The generation unit first collects a large amount of data related to a specific field and inputs it into the generative AI. For example, when creating AI specialized in the medical field, the generation unit collects medical data such as medical records, diagnostic data, and treatment protocols. This data is input into the generative AI, which learns from it to acquire knowledge specialized in the medical field. Similarly, when creating an AI specialized in the legal field, the generation unit collects legal data such as legal documents, precedents, and statutes, and inputs it into the generating AI. The AI ​​learns from this data and acquires knowledge specific to the legal field. In the same way when creating an AI specialized in the educational field, educational data such as educational curricula, teaching materials, and teaching methodologies are collected and input into the generating AI. The AI ​​learns from this data and acquires knowledge specific to the educational field. Based on the knowledge learned by the generating AI, the generation unit creates an expert AI. The expert AI possesses advanced knowledge and skills in a specific field and can provide users with expert advice and support. The generation unit evaluates the performance of the expert AI and improves its accuracy and reliability by training it with additional data as needed. This allows the generation unit to create and provide users with AI that possesses advanced expertise in diverse fields such as medicine, law, and education.

[0031] The publishing unit publishes the expert AI created by the generation unit to the store. The publishing unit can personalize the expert AI by designing, for example, its photo, illustration, and personality. For example, the publishing unit can design the photo of the expert AI. It can also design the illustration of the expert AI. Furthermore, the publishing unit can design the personality of the expert AI. For example, the publishing unit can design the photo of the expert AI in high resolution. The publishing unit can also design the illustration of the expert AI in various styles. The publishing unit can also design the personality of the expert AI to suit the user's preferences. Some or all of the above processing in the publishing unit may be performed using AI or not. For example, the publishing unit can input the design of the expert AI's photo or illustration into the AI ​​and have the AI ​​generate the design. The publishing unit first receives the basic information of the expert AI provided by the generation unit and uses this to design the appearance and personality of the expert AI. For example, in the case of an expert AI in the medical field, it can design photos and illustrations of doctors and nurses to give it a user-friendly appearance. For legal expert AI, photos and illustrations of lawyers and judges can be designed to give it a trustworthy appearance. For educational expert AI, photos and illustrations of teachers and educators can be designed to give it a friendly appearance. The publishing department flexibly adapts to user preferences and needs when designing the personality of the expert AI. For example, for medical expert AI, a kind and courteous personality can give patients a sense of security. For legal expert AI, a calm and logical personality can give users a sense of trust. For educational expert AI, a bright and friendly personality can make students feel comfortable with it. After designing the photos, illustrations, and personalities of the expert AI, the publishing department publishes them in the store. Users can browse the expert AIs published in the store and select one that suits their needs. The publishing department optimizes the store design and user interface so that users can easily search for and select expert AIs.This allows the publishing unit to effectively publish and provide to users the expert AI created by the generating unit.

[0032] The registration unit provides a function for registering highly specialized data. For example, the registration unit allows data providers to register highly specialized data. For example, the registration unit can register medical data. The registration unit can also register legal data. Furthermore, the registration unit can also register educational data. For example, the registration unit registers medical data into the database. The registration unit can also register legal data into the database. The registration unit can also register educational data into the database. Some or all of the above-described processes in the registration unit may be performed using AI or not. For example, the registration unit can input data registered by data providers into AI and have the AI ​​perform the data registration. The registration unit first receives highly specialized data provided by data providers and registers it in the database. For example, in the case of medical data, it receives data such as patient diagnostic records, treatment protocols, and medical images and registers it in the database. In the case of legal data, it receives data such as precedents, laws and regulations, and contracts and registers it in the database. In the case of educational data, it receives data such as teaching materials, educational curricula, and educational methodologies and registers it in the database. The registration department verifies data provided by data providers to ensure data quality. For example, in the case of medical data, it verifies the accuracy and consistency of the data and eliminates inaccurate or incomplete data. In the case of legal data, it verifies the reliability and timeliness of the data and eliminates outdated or unreliable data. In the case of educational data, it verifies the applicability and usefulness of the data and eliminates irrelevant or unreliable data. The registration department can utilize AI to streamline the data registration process. For example, data provided by data providers can be input into the AI, which then automatically verifies and registers the data. This allows the registration department to perform the data registration process quickly and efficiently. The registration department can centrally manage registered data and collaborate with other systems and departments as needed. This allows the registration department to effectively register highly specialized data and improve the overall performance of the system.

[0033] The collection department collects usage fees for the AI. The collection department collects usage fees, for example, through subscriptions. The collection department can collect monthly fees, for example. The collection department can also collect annual fees. Furthermore, the collection department can collect usage fees on a pay-per-use basis. For example, the collection department collects monthly fees by credit card. The collection department can also collect annual fees by bank transfer. The collection department can also collect usage fees by electronic money on a pay-per-use basis. Some or all of the above processes in the collection department may be performed using AI or not. For example, the collection department can input the usage fee collection into the AI ​​and have the AI ​​perform the collection. The collection department first calculates the usage fee based on the pricing plan selected by the user. For example, a user who has selected a monthly pricing plan will be billed a fixed fee each month. A user who has selected an annual pricing plan will be billed a fixed fee each year. For users who have selected a pay-as-you-go plan, charges will be calculated and billed according to their usage. The collection department will diversify the methods of collecting usage fees to improve user convenience. For example, it will offer multiple payment methods such as credit cards, bank transfers, and electronic money. Users can choose the payment method that suits their needs. The collection department can utilize AI to streamline the usage fee collection process. For example, the AI ​​can monitor user usage and automatically calculate usage fees. The AI ​​will automatically generate invoices for usage fees and send them to users. After the user makes a payment, the AI ​​will verify the payment and reflect it in the system. This allows the collection department to carry out the usage fee collection process quickly and efficiently. The collection department can centrally manage the usage fee collection status and collaborate with other systems and departments as needed. This allows the collection department to effectively manage the usage fee collection process and improve the overall system performance.

[0034] The generation unit can create AI specialized in specific fields by training it with highly specialized data. For example, the generation unit can create AI specialized in the medical field by training it with medical data. The generation unit can also create AI specialized in the legal field by training it with legal data. The generation unit can also create AI specialized in the educational field by training it with educational data. For example, the generation unit can create AI specialized in the medical field using medical data. The generation unit can also create AI specialized in the legal field using legal data. The generation unit can also create AI specialized in the educational field using educational data. In this way, by training it with highly specialized data, it is possible to create AI specialized in specific fields. Some or all of the above processing in the generation unit is performed using a generation AI. A generation AI is, for example, a text generation AI (e.g., LLM) or an image generation AI, but is not limited to such examples.

[0035] The public section can personalize expert AIs by designing their photos, illustrations, and personalities. For example, the public section can design photos of expert AIs. For example, the public section can design illustrations of expert AIs. For example, the public section can design the personalities of expert AIs. For example, the public section can design high-resolution photos of expert AIs. The public section can design illustrations of expert AIs in a variety of styles. The public section can also design the personalities of expert AIs to suit user preferences. In this way, expert AIs can be personalized by designing their photos, illustrations, and personalities. Some or all of the above-described processes in the public section may be performed using AI or not. For example, the public section can input the design of the expert AI's photos and illustrations into an AI and have the AI ​​generate the designs.

[0036] The registration unit can provide data providers with the functionality to register highly specialized data. For example, the registration unit can allow data providers to register medical data. For example, the registration unit can allow data providers to register legal data. For example, the registration unit can allow data providers to register educational data. For example, the registration unit allows data providers to register medical data in the database. For example, the registration unit allows data providers to register legal data in the database. For example, the registration unit allows data providers to register educational data in the database. This allows data providers to earn rewards by registering highly specialized data. Some or all of the above-described processes in the registration unit may be performed using AI or not. For example, the registration unit can input data registered by data providers into AI and have the AI ​​perform the data registration.

[0037] The collection unit can collect AI usage fees through subscriptions or other means, and pay a portion of those fees as compensation to the AI ​​creators and data providers. The collection unit can, for example, collect monthly fees. The collection unit can, for example, collect annual fees. The collection unit can, for example, collect usage fees on a pay-per-use basis. For example, the collection unit can collect monthly fees by credit card. The collection unit can also collect annual fees by bank transfer. The collection unit can also collect usage fees on a pay-per-use basis using electronic money. This allows for the creation of an ecosystem by collecting AI usage fees through subscriptions or other means, and paying a portion of those fees as compensation to the AI ​​creators and data providers. Some or all of the above-described processes in the collection unit may be performed using AI or not. For example, the collection unit can input the collection of usage fees into AI and have the AI ​​perform the collection.

[0038] The generation unit can analyze the user's past usage history and select the optimal generation method when generating expert AI. For example, the generation unit can select the optimal generation method for the generating AI based on data from expert AI that the user has used in the past. The generation unit can also generate expert AI specialized in a specific field from the user's past usage history. For example, the generation unit can analyze the user's past usage history and select the most effective generation method. In this way, the optimal generation method can be selected by analyzing the user's past usage history. Some or all of the above processing in the generation unit is performed using the generating AI.

[0039] The generation unit can incorporate the latest research findings in a specific field when generating expert AI. For example, the generation unit can generate expert AI by incorporating the latest research findings in the medical field. For example, the generation unit can also generate expert AI by incorporating the latest case precedents in the legal field. For example, the generation unit can also generate expert AI by incorporating the latest inventions in the technical field. This makes it possible to generate higher-quality expert AI by incorporating the latest research findings in a specific field. Some or all of the above processing in the generation unit is performed using the generated AI.

[0040] The generation unit can prioritize learning highly relevant data by considering the user's geographical location when generating expert AI. For example, if the user lives in a specific region, the generation unit can prioritize learning data related to that region. For example, if the user is traveling, the generation unit can prioritize learning data related to the travel destination. For example, if the user is planning to move, the generation unit can prioritize learning data related to the new place of residence. In this way, by considering the user's geographical location, highly relevant data can be prioritized for learning. Some or all of the above processing in the generation unit is performed using the generation AI.

[0041] The generation unit can analyze the user's social media activity and learn relevant data when generating expert AI. For example, the generation unit can learn data related to topics that the user frequently mentions on social media. The generation unit can also learn data related to topics that the user's social media followers are interested in. The generation unit can also learn data related to communities that the user participates in on social media. In this way, relevant data can be learned by analyzing the user's social media activity. Some or all of the above processing in the generation unit is performed using the generation AI.

[0042] The publishing unit can analyze a user's past usage history and select the optimal publishing method when publishing expert AI. For example, the publishing unit can select the optimal publishing method based on data from expert AI that the user has used in the past. For example, the publishing unit can publish expert AI specialized in a particular field based on a user's past usage history. For example, the publishing unit can analyze a user's past usage history and select the most effective publishing method. In this way, the optimal publishing method can be selected by analyzing a user's past usage history. Some or all of the above processing in the publishing unit may be performed using AI or not using AI.

[0043] The public access section can incorporate the latest research findings in specific fields when releasing expert AI. For example, the public access section can release expert AI incorporating the latest research findings in the medical field. For example, the public access section can release expert AI incorporating the latest case law in the legal field. For example, the public access section can release expert AI incorporating the latest inventions in the technical field. This allows for the release of higher-quality expert AI by incorporating the latest research findings in specific fields. Some or all of the above processing in the public access section may be performed using AI or not.

[0044] The publishing section can prioritize the publication of highly relevant data by considering the user's geographical location when publishing expert AI. For example, if the user lives in a specific region, the publishing section can prioritize the publication of data related to that region. For example, if the user is traveling, the publishing section can prioritize the publication of data related to the travel destination. For example, if the user is planning to move, the publishing section can prioritize the publication of data related to the new residence. In this way, highly relevant data can be prioritized by considering the user's geographical location. Some or all of the above processing in the publishing section may be performed using AI or not.

[0045] The public access unit can analyze a user's social media activity and publish relevant data when the expert AI is released. For example, the public access unit can publish data related to topics that the user frequently mentions on social media. For example, the public access unit can publish data related to topics that the user's social media followers are interested in. For example, the public access unit can publish data related to communities that the user participates in on social media. This allows relevant data to be published by analyzing the user's social media activity. Some or all of the processing described above in the public access unit may be performed using AI or not.

[0046] The registration unit can analyze the user's past registration history and select the optimal registration method when registering data. For example, the registration unit can select the optimal registration method based on data previously registered by the user. For example, the registration unit can also register data specialized in a specific field based on the user's past registration history. For example, the registration unit can analyze the user's past registration history and select the most effective registration method. In this way, the optimal registration method can be selected by analyzing the user's past registration history. Some or all of the above-described processes in the registration unit may be performed using AI or not.

[0047] The registration unit can prioritize registering highly relevant data by considering the user's geographical location information during data registration. For example, if the user lives in a specific region, the registration unit can prioritize registering data related to that region. For example, if the user is traveling, the registration unit can prioritize registering data related to their travel destination. For example, if the user is planning to move, the registration unit can prioritize registering data related to their new residence. In this way, highly relevant data can be prioritized by considering the user's geographical location information. Some or all of the above processing in the registration unit may be performed using AI or not.

[0048] The collection department can analyze a user's past usage history and select the most suitable collection method when collecting usage fees. For example, the collection department can select the most suitable collection method based on data of services the user has used in the past. For example, the collection department can also select a collection method specialized for a particular field based on the user's past usage history. For example, the collection department can analyze a user's past usage history and select the most effective collection method. In this way, the collection department can select the most suitable collection method by analyzing the user's past usage history. Some or all of the above processes in the collection department may be performed using AI or not.

[0049] The collection unit can prioritize collecting highly relevant data when collecting usage fees, taking into account the user's geographical location. For example, if a user lives in a specific region, the collection unit can prioritize collecting data related to that region. For example, if a user is traveling, the collection unit can prioritize collecting data related to their travel destination. For example, if a user is planning to move, the collection unit can prioritize collecting data related to their new residence. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the collection unit may be performed using AI or not.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The generation unit can analyze the user's past search history and select the optimal generation method when generating expert AI. For example, the generation unit can select the optimal generation method based on keywords the user has searched for in the past. The generation unit can also generate expert AI specializing in a specific field from the user's past search history. For example, the generation unit can analyze the user's past search history and select the most effective generation method. In this way, the optimal generation method can be selected by analyzing the user's past search history.

[0052] The publishing department can analyze past user feedback and select the optimal publishing method when publishing expert AI. For example, the publishing department can select the optimal publishing method based on feedback previously provided by users. For example, the publishing department can publish expert AI specializing in a specific field based on past user feedback. For example, the publishing department can analyze past user feedback and select the most effective publishing method. This allows the optimal publishing method to be selected by analyzing past user feedback.

[0053] The registration unit can analyze the user's past data provision history when registering data and select the optimal registration method. For example, the registration unit can select the optimal registration method based on data previously provided by the user. For example, the registration unit can also register data specialized in a specific field based on the user's past data provision history. For example, the registration unit can analyze the user's past data provision history and select the most effective registration method. In this way, the optimal registration method can be selected by analyzing the user's past data provision history.

[0054] The collection department can analyze a user's past payment history and select the most suitable collection method when collecting usage fees. For example, the collection department can select the most suitable collection method based on data of usage fees the user has paid in the past. For example, the collection department can also select a collection method specialized for a particular area based on the user's past payment history. For example, the collection department can analyze a user's past payment history and select the most effective collection method. In this way, the collection department can select the most suitable collection method by analyzing the user's past payment history.

[0055] The generation unit can prioritize learning highly relevant data by considering the user's geographical location when generating expert AI. For example, if the user lives in a specific region, the generation unit will prioritize learning data related to that region. For example, if the user is traveling, the generation unit can prioritize learning data related to the travel destination. For example, if the user is planning to move, the generation unit can prioritize learning data related to the new place of residence. In this way, by considering the user's geographical location, the generation unit can prioritize learning highly relevant data.

[0056] The public access system can prioritize the publication of highly relevant data when publishing expert AI, taking into account the user's geographical location. For example, if a user lives in a specific region, the public access system can prioritize the publication of data related to that region. For example, if a user is traveling, the public access system can prioritize the publication of data related to their travel destination. For example, if a user is planning to move, the public access system can prioritize the publication of data related to their new residence. This allows for the prioritization of highly relevant data by considering the user's geographical location.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The generation unit creates expert AI using generative AI. The generation unit creates expert AI specialized in specific fields, such as medicine, law, or education. The generation unit creates AI specialized in each field by training it with medical data, legal data, and educational data. Some or all of the processing in the generation unit is performed using generative AI (e.g., text generation AI or image generation AI). Step 2: The publishing unit publishes the expert AI created by the generating unit to the store. The publishing unit can personalize the expert AI by designing its photo, illustrations, personality, etc. Some or all of the processing in the publishing unit may be done using AI or not. Step 3: The registration unit provides a function for registering highly specialized data. For example, the registration unit registers medical data, legal data, and educational data into the database. Some or all of the processing in the registration unit may be performed using AI, or it may not be performed using AI. Step 4: The collection department collects fees for using the AI. The collection department collects fees, for example, through a subscription. The collection department can collect fees on a monthly, annual, or usage-based basis. Some or all of the processing in the collection department may be performed using AI, or not using AI.

[0059] (Example of form 2) The expert AI store system according to an embodiment of the present invention is a system that provides AI services for a wide range of applications by creating expert AIs that have been trained on highly specialized data based on generative AI and publishing them in a store. This expert AI store system uses large-scale models such as generative AI to create expert AIs that have been trained on highly specialized data. These expert AIs can be given individuality by designing things like photos, illustrations, and personalities. Next, the created expert AIs are published in a store and made available to a wide range of people. The store also provides a function to register highly specialized data, and data providers can earn rewards by registering data. In addition, a usage fee is collected for the use of the AI, such as through a subscription, and a portion of the usage fee is paid as rewards to the AI ​​creators and data providers. This builds an ecosystem for expert AIs. For example, expert AIs are created using large-scale models such as generative AIs. In this case, by training the AIs on highly specialized data, AIs specialized in specific fields can be created. For example, AIs specialized in the medical field or AIs specialized in the legal field can be considered. This allows users to use the AI ​​as if they were consulting with an expert. Next, the created expert AIs are published in a store. In the store, users can search for and use expert AIs. For example, a user can search for an AI specialized in the medical field and receive health advice. The store also allows users to personalize expert AIs by designing their photos, illustrations, and personalities. This enables users to choose expert AIs that suit their preferences. Furthermore, the store provides a function for registering highly specialized data. Data providers can earn rewards by registering their highly specialized data. For example, by registering medical data or legal data, expert AIs can be created using that data. This allows for the collection of highly specialized data, enabling the creation of higher-quality expert AIs. In addition, a fee is charged for using the AIs, such as a subscription. Users can use expert AIs by paying a monthly fee. A portion of this fee is paid as compensation to the AI ​​creators and data providers.This creates an ecosystem for expert AI, allowing AI creators and data providers to earn rewards. This system enables the provision of generative AI-based expert AI for a wide range of applications, allowing users to utilize AI as if they were consulting with an expert. Furthermore, data providers can earn rewards by registering highly specialized data. This creates an ecosystem for expert AI, allowing AI creators and data providers to earn rewards. As a result, the expert AI store system provides expert AI for a wide range of applications, allowing users to utilize AI as if they were consulting with an expert.

[0060] The expert AI store system according to this embodiment comprises a generation unit, a publishing unit, a registration unit, and a collection unit. The generation unit creates expert AI using a generation AI. The generation unit, for example, uses the generation AI to create expert AI specialized in a specific field. For example, the generation unit can create AI specialized in the medical field. The generation unit can also create AI specialized in the legal field. Furthermore, the generation unit can also create AI specialized in the education field. For example, the generation unit can create AI specialized in the medical field by training it with medical data. The generation unit can also create AI specialized in the legal field by training it with legal data. The generation unit can also create AI specialized in the education field by training it with educational data. Some or all of the above-described processes in the generation unit are performed using the generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or an image generation AI, but is not limited to such examples. The publishing unit publishes the expert AI created by the generation unit in the store. The publishing unit can personalize the expert AI by, for example, designing its photo, illustration, personality, etc. The publishing unit can, for example, design a photo for the expert AI. Furthermore, the Public Section can also design illustrations of expert AI. In addition, the Public Section can also design the personality of the expert AI. For example, the Public Section can design high-resolution photographs of the expert AI. The Public Section can also design illustrations of the expert AI in various styles. The Public Section can also design the personality of the expert AI to suit the user's preferences. Some or all of the above processes in the Public Section may be performed using AI or not. For example, the Public Section can input the design of the expert AI's photograph or illustration into the AI ​​and have the AI ​​generate the design. The Registration Section provides a function for registering highly specialized data. For example, the Registration Section can allow data providers to register highly specialized data. For example, the Registration Section can register medical data. The Registration Section can also register legal data. Furthermore, the Registration Section can also register educational data. For example, the Registration Section can register medical data in a database.The registration unit can also register legal data in the database. The registration unit can also register educational data in the database. Some or all of the above processes in the registration unit may be performed using AI or not. For example, the registration unit can input data registered by data providers into the AI ​​and have the AI ​​perform the data registration. The collection unit collects fees for using the AI. The collection unit collects fees for using the AI, for example, through a subscription. The collection unit can collect monthly fees, for example. The collection unit can also collect annual fees. Furthermore, the collection unit can collect fees on a pay-per-use basis. For example, the collection unit can collect monthly fees by credit card. The collection unit can also collect annual fees by bank transfer. The collection unit can also collect fees on a pay-per-use basis using electronic money. Some or all of the above processes in the collection unit may be performed using AI or not. For example, the collection department can input the collection of usage fees into the AI ​​and have the AI ​​perform the collection. As a result, the expert AI store system according to this embodiment can create expert AI using the generation AI and publish it in the store, thereby providing AI services for a wide range of applications.

[0061] The generation unit creates expert AI using generative AI. For example, the generation unit uses generative AI to create expert AI specialized in a specific field. For instance, the generation unit can create AI specialized in the medical field. It can also create AI specialized in the legal field. Furthermore, it can create AI specialized in the educational field. For example, the generation unit creates AI specialized in the medical field by training it with medical data. It can also create AI specialized in the legal field by training it with legal data. It can also create AI specialized in the educational field by training it with educational data. Some or all of the above-described processes in the generation unit are performed using generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or image generation AI, but is not limited to these examples. The generation unit first collects a large amount of data related to a specific field and inputs it into the generative AI. For example, when creating AI specialized in the medical field, the generation unit collects medical data such as medical records, diagnostic data, and treatment protocols. This data is input into the generative AI, which learns from it to acquire knowledge specialized in the medical field. Similarly, when creating an AI specialized in the legal field, the generation unit collects legal data such as legal documents, precedents, and statutes, and inputs it into the generating AI. The AI ​​learns from this data and acquires knowledge specific to the legal field. In the same way when creating an AI specialized in the educational field, educational data such as educational curricula, teaching materials, and teaching methodologies are collected and input into the generating AI. The AI ​​learns from this data and acquires knowledge specific to the educational field. Based on the knowledge learned by the generating AI, the generation unit creates an expert AI. The expert AI possesses advanced knowledge and skills in a specific field and can provide users with expert advice and support. The generation unit evaluates the performance of the expert AI and improves its accuracy and reliability by training it with additional data as needed. This allows the generation unit to create and provide users with AI that possesses advanced expertise in diverse fields such as medicine, law, and education.

[0062] The publishing unit publishes the expert AI created by the generation unit to the store. The publishing unit can personalize the expert AI by designing, for example, its photo, illustration, and personality. For example, the publishing unit can design the photo of the expert AI. It can also design the illustration of the expert AI. Furthermore, the publishing unit can design the personality of the expert AI. For example, the publishing unit can design the photo of the expert AI in high resolution. The publishing unit can also design the illustration of the expert AI in various styles. The publishing unit can also design the personality of the expert AI to suit the user's preferences. Some or all of the above processing in the publishing unit may be performed using AI or not. For example, the publishing unit can input the design of the expert AI's photo or illustration into the AI ​​and have the AI ​​generate the design. The publishing unit first receives the basic information of the expert AI provided by the generation unit and uses this to design the appearance and personality of the expert AI. For example, in the case of an expert AI in the medical field, it can design photos and illustrations of doctors and nurses to give it a user-friendly appearance. For legal expert AI, photos and illustrations of lawyers and judges can be designed to give it a trustworthy appearance. For educational expert AI, photos and illustrations of teachers and educators can be designed to give it a friendly appearance. The publishing department flexibly adapts to user preferences and needs when designing the personality of the expert AI. For example, for medical expert AI, a kind and courteous personality can give patients a sense of security. For legal expert AI, a calm and logical personality can give users a sense of trust. For educational expert AI, a bright and friendly personality can make students feel comfortable with it. After designing the photos, illustrations, and personalities of the expert AI, the publishing department publishes them in the store. Users can browse the expert AIs published in the store and select one that suits their needs. The publishing department optimizes the store design and user interface so that users can easily search for and select expert AIs.This allows the publishing unit to effectively publish and provide to users the expert AI created by the generating unit.

[0063] The registration unit provides a function for registering highly specialized data. For example, the registration unit allows data providers to register highly specialized data. For example, the registration unit can register medical data. The registration unit can also register legal data. Furthermore, the registration unit can also register educational data. For example, the registration unit registers medical data into the database. The registration unit can also register legal data into the database. The registration unit can also register educational data into the database. Some or all of the above-described processes in the registration unit may be performed using AI or not. For example, the registration unit can input data registered by data providers into AI and have the AI ​​perform the data registration. The registration unit first receives highly specialized data provided by data providers and registers it in the database. For example, in the case of medical data, it receives data such as patient diagnostic records, treatment protocols, and medical images and registers it in the database. In the case of legal data, it receives data such as precedents, laws and regulations, and contracts and registers it in the database. In the case of educational data, it receives data such as teaching materials, educational curricula, and educational methodologies and registers it in the database. The registration department verifies data provided by data providers to ensure data quality. For example, in the case of medical data, it verifies the accuracy and consistency of the data and eliminates inaccurate or incomplete data. In the case of legal data, it verifies the reliability and timeliness of the data and eliminates outdated or unreliable data. In the case of educational data, it verifies the applicability and usefulness of the data and eliminates irrelevant or unreliable data. The registration department can utilize AI to streamline the data registration process. For example, data provided by data providers can be input into the AI, which then automatically verifies and registers the data. This allows the registration department to perform the data registration process quickly and efficiently. The registration department can centrally manage registered data and collaborate with other systems and departments as needed. This allows the registration department to effectively register highly specialized data and improve the overall performance of the system.

[0064] The collection department collects usage fees for the AI. The collection department collects usage fees, for example, through subscriptions. The collection department can collect monthly fees, for example. The collection department can also collect annual fees. Furthermore, the collection department can collect usage fees on a pay-per-use basis. For example, the collection department collects monthly fees by credit card. The collection department can also collect annual fees by bank transfer. The collection department can also collect usage fees by electronic money on a pay-per-use basis. Some or all of the above processes in the collection department may be performed using AI or not. For example, the collection department can input the usage fee collection into the AI ​​and have the AI ​​perform the collection. The collection department first calculates the usage fee based on the pricing plan selected by the user. For example, a user who has selected a monthly pricing plan will be billed a fixed fee each month. A user who has selected an annual pricing plan will be billed a fixed fee each year. For users who have selected a pay-as-you-go plan, charges will be calculated and billed according to their usage. The collection department will diversify the methods of collecting usage fees to improve user convenience. For example, it will offer multiple payment methods such as credit cards, bank transfers, and electronic money. Users can choose the payment method that suits their needs. The collection department can utilize AI to streamline the usage fee collection process. For example, the AI ​​can monitor user usage and automatically calculate usage fees. The AI ​​will automatically generate invoices for usage fees and send them to users. After the user makes a payment, the AI ​​will verify the payment and reflect it in the system. This allows the collection department to carry out the usage fee collection process quickly and efficiently. The collection department can centrally manage the usage fee collection status and collaborate with other systems and departments as needed. This allows the collection department to effectively manage the usage fee collection process and improve the overall system performance.

[0065] The generation unit can create AI specialized in specific fields by training it with highly specialized data. For example, the generation unit can create AI specialized in the medical field by training it with medical data. The generation unit can also create AI specialized in the legal field by training it with legal data. The generation unit can also create AI specialized in the educational field by training it with educational data. For example, the generation unit can create AI specialized in the medical field using medical data. The generation unit can also create AI specialized in the legal field using legal data. The generation unit can also create AI specialized in the educational field using educational data. In this way, by training it with highly specialized data, it is possible to create AI specialized in specific fields. Some or all of the above processing in the generation unit is performed using a generation AI. A generation AI is, for example, a text generation AI (e.g., LLM) or an image generation AI, but is not limited to such examples.

[0066] The public section can personalize expert AIs by designing their photos, illustrations, and personalities. For example, the public section can design photos of expert AIs. For example, the public section can design illustrations of expert AIs. For example, the public section can design the personalities of expert AIs. For example, the public section can design high-resolution photos of expert AIs. The public section can design illustrations of expert AIs in a variety of styles. The public section can also design the personalities of expert AIs to suit user preferences. In this way, expert AIs can be personalized by designing their photos, illustrations, and personalities. Some or all of the above-described processes in the public section may be performed using AI or not. For example, the public section can input the design of the expert AI's photos and illustrations into an AI and have the AI ​​generate the designs.

[0067] The registration unit can provide data providers with the functionality to register highly specialized data. For example, the registration unit can allow data providers to register medical data. For example, the registration unit can allow data providers to register legal data. For example, the registration unit can allow data providers to register educational data. For example, the registration unit allows data providers to register medical data in the database. For example, the registration unit allows data providers to register legal data in the database. For example, the registration unit allows data providers to register educational data in the database. This allows data providers to earn rewards by registering highly specialized data. Some or all of the above-described processes in the registration unit may be performed using AI or not. For example, the registration unit can input data registered by data providers into AI and have the AI ​​perform the data registration.

[0068] The collection unit can collect AI usage fees through subscriptions or other means, and pay a portion of those fees as compensation to the AI ​​creators and data providers. The collection unit can, for example, collect monthly fees. The collection unit can, for example, collect annual fees. The collection unit can, for example, collect usage fees on a pay-per-use basis. For example, the collection unit can collect monthly fees by credit card. The collection unit can also collect annual fees by bank transfer. The collection unit can also collect usage fees on a pay-per-use basis using electronic money. This allows for the creation of an ecosystem by collecting AI usage fees through subscriptions or other means, and paying a portion of those fees as compensation to the AI ​​creators and data providers. Some or all of the above-described processes in the collection unit may be performed using AI or not. For example, the collection unit can input the collection of usage fees into AI and have the AI ​​perform the collection.

[0069] The generation unit can estimate the user's emotions and adjust the timing of expert AI generation based on the estimated user emotions. For example, if the user is stressed, the generation unit will generate expert AI at a time when the generating AI can relax. For example, if the user is concentrating, the generation unit can also generate expert AI at a time when the generating AI can make the most of that concentration. For example, if the user is tired, the generation unit can also generate expert AI after the user has rested. In this way, by adjusting the timing of expert AI generation based on the user's emotions, expert AI can be generated at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit is performed using the generation AI.

[0070] The generation unit can analyze the user's past usage history and select the optimal generation method when generating expert AI. For example, the generation unit can select the optimal generation method for the generating AI based on data from expert AI that the user has used in the past. The generation unit can also generate expert AI specialized in a specific field from the user's past usage history. For example, the generation unit can analyze the user's past usage history and select the most effective generation method. In this way, the optimal generation method can be selected by analyzing the user's past usage history. Some or all of the above processing in the generation unit is performed using the generating AI.

[0071] The generation unit can incorporate the latest research findings in a specific field when generating expert AI. For example, the generation unit can generate expert AI by incorporating the latest research findings in the medical field. For example, the generation unit can also generate expert AI by incorporating the latest case precedents in the legal field. For example, the generation unit can also generate expert AI by incorporating the latest inventions in the technical field. This makes it possible to generate higher-quality expert AI by incorporating the latest research findings in a specific field. Some or all of the above processing in the generation unit is performed using the generated AI.

[0072] The generation unit can estimate the user's emotions and determine the priority of expert AIs to generate based on the estimated user emotions. For example, if the user needs urgent consultation, the generation unit will prioritize generating expert AIs. For example, if the user is relaxed, the generation unit can also generate expert AIs that can be prioritized and not prioritized. For example, if the user is excited, the generation unit can also prioritize generating expert AIs that can take advantage of that excitement. In this way, by determining the priority of expert AIs based on the user's emotions, more appropriate expert AIs can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit is performed using a generation AI.

[0073] The generation unit can prioritize learning highly relevant data by considering the user's geographical location when generating expert AI. For example, if the user lives in a specific region, the generation unit can prioritize learning data related to that region. For example, if the user is traveling, the generation unit can prioritize learning data related to the travel destination. For example, if the user is planning to move, the generation unit can prioritize learning data related to the new place of residence. In this way, by considering the user's geographical location, highly relevant data can be prioritized for learning. Some or all of the above processing in the generation unit is performed using the generation AI.

[0074] The generation unit can analyze the user's social media activity and learn relevant data when generating expert AI. For example, the generation unit can learn data related to topics that the user frequently mentions on social media. The generation unit can also learn data related to topics that the user's social media followers are interested in. The generation unit can also learn data related to communities that the user participates in on social media. In this way, relevant data can be learned by analyzing the user's social media activity. Some or all of the above processing in the generation unit is performed using the generation AI.

[0075] The public access unit can estimate the user's emotions and adjust the timing of the expert AI's release based on the estimated emotions. For example, if the user is stressed, the public access unit may release the expert AI at a time when the user can relax. For example, if the user is focused, the public access unit may release the expert AI at a time when the user can leverage that focus. For example, if the user is tired, the public access unit may release the expert AI after the user has rested. By adjusting the timing of the expert AI's release based on the user's emotions, the expert AI can be released at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the public access unit may be performed using AI or not using AI.

[0076] The publishing unit can analyze a user's past usage history and select the optimal publishing method when publishing expert AI. For example, the publishing unit can select the optimal publishing method based on data from expert AI that the user has used in the past. For example, the publishing unit can publish expert AI specialized in a particular field based on a user's past usage history. For example, the publishing unit can analyze a user's past usage history and select the most effective publishing method. In this way, the optimal publishing method can be selected by analyzing a user's past usage history. Some or all of the above processing in the publishing unit may be performed using AI or not using AI.

[0077] The public access section can incorporate the latest research findings in specific fields when releasing expert AI. For example, the public access section can release expert AI incorporating the latest research findings in the medical field. For example, the public access section can release expert AI incorporating the latest case law in the legal field. For example, the public access section can release expert AI incorporating the latest inventions in the technical field. This allows for the release of higher-quality expert AI by incorporating the latest research findings in specific fields. Some or all of the above processing in the public access section may be performed using AI or not.

[0078] The public access unit can estimate the user's emotions and determine the priority of expert AIs to access based on the estimated emotions. For example, if the user needs urgent advice, the public access unit will prioritize accessing expert AIs. For example, if the user is relaxed, the public access unit may also prioritize accessing expert AIs that can be prioritized and delayed. For example, if the user is excited, the public access unit may also prioritize accessing expert AIs that can leverage that excitement. This allows for the access of more appropriate expert AIs by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AIs (e.g., LLMs) or multimodal generation AIs. Some or all of the processing described above in the public access unit may be performed using AI or not.

[0079] The publishing section can prioritize the publication of highly relevant data by considering the user's geographical location when publishing expert AI. For example, if the user lives in a specific region, the publishing section can prioritize the publication of data related to that region. For example, if the user is traveling, the publishing section can prioritize the publication of data related to the travel destination. For example, if the user is planning to move, the publishing section can prioritize the publication of data related to the new residence. In this way, highly relevant data can be prioritized by considering the user's geographical location. Some or all of the above processing in the publishing section may be performed using AI or not.

[0080] The public access unit can analyze a user's social media activity and publish relevant data when the expert AI is released. For example, the public access unit can publish data related to topics that the user frequently mentions on social media. For example, the public access unit can publish data related to topics that the user's social media followers are interested in. For example, the public access unit can publish data related to communities that the user participates in on social media. This allows relevant data to be published by analyzing the user's social media activity. Some or all of the processing described above in the public access unit may be performed using AI or not.

[0081] The registration unit can estimate the user's emotions and adjust the timing of data registration based on the estimated emotions. For example, if the user is feeling stressed, the registration unit may register data at a time when the user can relax. For example, if the user is concentrating, the registration unit may register data at a time when the user can make the most of that concentration. For example, if the user is tired, the registration unit may register data after a rest. In this way, by adjusting the timing of data registration based on the user's emotions, data can be registered at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the registration unit may be performed using AI or not using AI.

[0082] The registration unit can analyze the user's past registration history and select the optimal registration method when registering data. For example, the registration unit can select the optimal registration method based on data previously registered by the user. For example, the registration unit can also register data specialized in a specific field based on the user's past registration history. For example, the registration unit can analyze the user's past registration history and select the most effective registration method. In this way, the optimal registration method can be selected by analyzing the user's past registration history. Some or all of the above-described processes in the registration unit may be performed using AI or not.

[0083] The registration unit can estimate the user's emotions and determine the priority of data to register based on the estimated user emotions. For example, if the user needs to register urgent data, the registration unit will prioritize registering that data. For example, if the user is relaxed, the registration unit can also register data that can be postponed. For example, if the user is excited, the registration unit can prioritize registering data that can take advantage of that excitement. In this way, by determining the priority of data based on the user's emotions, more appropriate data can be registered preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the registration unit may be performed using AI or not using AI.

[0084] The registration unit can prioritize registering highly relevant data by considering the user's geographical location information during data registration. For example, if the user lives in a specific region, the registration unit can prioritize registering data related to that region. For example, if the user is traveling, the registration unit can prioritize registering data related to their travel destination. For example, if the user is planning to move, the registration unit can prioritize registering data related to their new residence. In this way, highly relevant data can be prioritized by considering the user's geographical location information. Some or all of the above processing in the registration unit may be performed using AI or not.

[0085] The collection unit can estimate the user's emotions and adjust the timing of fee collection based on the estimated emotions. For example, if the user is stressed, the collection unit can collect the fee at a time when the user can relax. For example, if the user is focused, the collection unit can collect the fee at a time when the user can make the most of that focus. For example, if the user is tired, the collection unit can collect the fee after the user has rested. In this way, by adjusting the timing of fee collection based on the user's emotions, fees can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the collection unit may be performed using AI or not using AI.

[0086] The collection department can analyze a user's past usage history and select the most suitable collection method when collecting usage fees. For example, the collection department can select the most suitable collection method based on data of services the user has used in the past. For example, the collection department can also select a collection method specialized for a particular field based on the user's past usage history. For example, the collection department can analyze a user's past usage history and select the most effective collection method. In this way, the collection department can select the most suitable collection method by analyzing the user's past usage history. Some or all of the above processes in the collection department may be performed using AI or not.

[0087] The collection unit can estimate the user's emotions and determine the priority of usage fees to be collected based on the estimated user emotions. For example, the collection unit may prioritize collecting usage fees if the user has an urgent need for payment. For example, the collection unit may also prioritize collecting usage fees that can be postponed if the user is relaxed. For example, the collection unit may also prioritize collecting usage fees that can capitalize on the user's excitement if the user is excited. In this way, by prioritizing usage fees based on the user's emotions, more appropriate usage fees can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the collection unit may be performed using AI or not using AI.

[0088] The collection unit can prioritize collecting highly relevant data when collecting usage fees, taking into account the user's geographical location. For example, if a user lives in a specific region, the collection unit can prioritize collecting data related to that region. For example, if a user is traveling, the collection unit can prioritize collecting data related to their travel destination. For example, if a user is planning to move, the collection unit can prioritize collecting data related to their new residence. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the collection unit may be performed using AI or not.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The generation unit can analyze the user's past search history and select the optimal generation method when generating expert AI. For example, the generation unit can select the optimal generation method based on keywords the user has searched for in the past. The generation unit can also generate expert AI specializing in a specific field from the user's past search history. For example, the generation unit can analyze the user's past search history and select the most effective generation method. In this way, the optimal generation method can be selected by analyzing the user's past search history.

[0091] The generation unit can estimate the user's emotions when generating expert AI and adjust the content of the expert AI based on the estimated user emotions. For example, if the user is feeling anxious, the generation unit can generate expert AI with content that provides reassurance. For example, if the user is excited, the generation unit can also generate expert AI with content that capitalizes on that excitement. For example, if the user is depressed, the generation unit can also generate expert AI that includes encouraging content. In this way, by adjusting the content of the expert AI based on the user's emotions, it is possible to generate expert AI with more appropriate content.

[0092] The publishing department can analyze past user feedback and select the optimal publishing method when publishing expert AI. For example, the publishing department can select the optimal publishing method based on feedback previously provided by users. For example, the publishing department can publish expert AI specializing in a specific field based on past user feedback. For example, the publishing department can analyze past user feedback and select the most effective publishing method. This allows the optimal publishing method to be selected by analyzing past user feedback.

[0093] The public access unit can estimate the user's emotions when releasing expert AI and adjust the content of the expert AI based on the estimated emotions. For example, if the user is feeling anxious, the public access unit can release expert AI with reassuring content. For example, if the user is excited, the public access unit can release expert AI with content that capitalizes on that excitement. For example, if the user is depressed, the public access unit can release expert AI with encouraging content. In this way, by adjusting the content of expert AI based on the user's emotions, it is possible to release expert AI with more appropriate content.

[0094] The registration unit can analyze the user's past data provision history when registering data and select the optimal registration method. For example, the registration unit can select the optimal registration method based on data previously provided by the user. For example, the registration unit can also register data specialized in a specific field based on the user's past data provision history. For example, the registration unit can analyze the user's past data provision history and select the most effective registration method. In this way, the optimal registration method can be selected by analyzing the user's past data provision history.

[0095] The registration unit can estimate the user's emotions when registering data and adjust the content of the data to be registered based on the estimated emotions. For example, if the user is feeling anxious, the registration unit can register data that provides a sense of security. For example, if the user is excited, the registration unit can register data that capitalizes on that excitement. For example, if the user is depressed, the registration unit can register data that includes encouraging content. In this way, by adjusting the content of the data based on the user's emotions, more appropriate data can be registered.

[0096] The collection department can analyze a user's past payment history and select the most suitable collection method when collecting usage fees. For example, the collection department can select the most suitable collection method based on data of usage fees the user has paid in the past. For example, the collection department can also select a collection method specialized for a particular area based on the user's past payment history. For example, the collection department can analyze a user's past payment history and select the most effective collection method. In this way, the collection department can select the most suitable collection method by analyzing the user's past payment history.

[0097] The collection department can estimate the user's emotions when collecting usage fees and adjust the content of the fees based on those estimated emotions. For example, if the user is feeling anxious, the collection department can collect fees for content that provides a sense of security. For example, if the user is excited, the collection department can collect fees for content that capitalizes on that excitement. For example, if the user is depressed, the collection department can collect fees that include encouraging content. By adjusting the content of usage fees based on the user's emotions, the collection department can collect more appropriate fees.

[0098] The generation unit can prioritize learning highly relevant data by considering the user's geographical location when generating expert AI. For example, if the user lives in a specific region, the generation unit will prioritize learning data related to that region. For example, if the user is traveling, the generation unit can prioritize learning data related to the travel destination. For example, if the user is planning to move, the generation unit can prioritize learning data related to the new place of residence. In this way, by considering the user's geographical location, the generation unit can prioritize learning highly relevant data.

[0099] The public access system can prioritize the publication of highly relevant data when publishing expert AI, taking into account the user's geographical location. For example, if a user lives in a specific region, the public access system can prioritize the publication of data related to that region. For example, if a user is traveling, the public access system can prioritize the publication of data related to their travel destination. For example, if a user is planning to move, the public access system can prioritize the publication of data related to their new residence. This allows for the prioritization of highly relevant data by considering the user's geographical location.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The generation unit creates expert AI using generative AI. The generation unit creates expert AI specialized in specific fields, such as medicine, law, or education. The generation unit creates AI specialized in each field by training it with medical data, legal data, and educational data. Some or all of the processing in the generation unit is performed using generative AI (e.g., text generation AI or image generation AI). Step 2: The publishing unit publishes the expert AI created by the generating unit to the store. The publishing unit can personalize the expert AI by designing its photo, illustrations, personality, etc. Some or all of the processing in the publishing unit may be done using AI or not. Step 3: The registration unit provides a function for registering highly specialized data. For example, the registration unit registers medical data, legal data, and educational data into the database. Some or all of the processing in the registration unit may be performed using AI, or it may not be performed using AI. Step 4: The collection department collects fees for using the AI. The collection department collects fees, for example, through a subscription. The collection department can collect fees on a monthly, annual, or usage-based basis. Some or all of the processing in the collection department may be performed using AI, or not using AI.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] Each of the multiple elements described above, including the generation unit, publication unit, registration unit, and collection unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The publication unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The registration unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements described above, including the generation unit, publication unit, registration unit, and collection unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The publication unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The registration unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The collection unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the generation unit, publication unit, registration unit, and collection unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The publication unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The registration unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The collection unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the generation unit, publication unit, registration unit, and collection unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The publication unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The registration unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The collection unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0155] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0158] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0164] 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.

[0165] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0166] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0167] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0168] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] (Note 1) A generation unit that uses generative AI to create expert AI, The publishing unit publishes the expert AI created by the generation unit to the store, The registration section registers highly specialized data, It includes a collection department that collects fees for the use of AI. A system characterized by the following features. (Note 2) The generating unit is By training the AI ​​with highly specialized data, we can create AI that specializes in specific fields. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned public section is, Personalize the AI ​​expert by designing its photos, illustrations, and personality. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned registration unit is This feature allows data providers to register highly specialized data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection department, The company collects fees for using the AI ​​through subscriptions, and pays a portion of those fees as compensation to the AI ​​creators and data providers. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is It estimates the user's emotions and adjusts the timing of expert AI generation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is When generating expert AI, the system analyzes the user's past usage history to select the optimal generation method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is When generating expert AI, incorporate the latest research findings in specific fields. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is It estimates the user's emotions and determines the priority of expert AI generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating expert AI, the system prioritizes learning from highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating expert AI, the user's social media activity is analyzed and relevant data is used for training. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned public section is, It estimates user sentiment and adjusts the timing of the release of expert AI based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned public section is, When releasing the expert AI, we analyze the user's past usage history to select the optimal release method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned public section is, When releasing expert AI, incorporate the latest research findings in specific fields. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned public section is, It estimates user sentiment and prioritizes the publication of expert AI based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned public section is, When releasing expert AI, the system prioritizes the release of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned public section is, When the expert AI is released, it will analyze users' social media activity and publish relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of data registration based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned registration unit is When registering data, the system analyzes the user's past registration history and selects the optimal registration method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned registration unit is The system estimates the user's emotions and determines the priority of data to register based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned registration unit is When registering data, the system prioritizes registering highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection department, The system estimates the user's emotions and adjusts the timing of fee collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collection department, When collecting usage fees, the system analyzes the user's past usage history and selects the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection department, It estimates user sentiment and determines the priority of fees to be charged based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection department, When collecting usage fees, the system prioritizes collecting fees for highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A generation unit that uses generational AI to create expert AI, A publishing unit publishes the expert AI created by the generation unit to the store, The registration section registers highly specialized data, It includes a collection department that collects fees for the use of AI. A system characterized by the following features.

2. The generating unit is By training the AI ​​with highly specialized data, we can create an AI that specializes in a particular field. The system according to feature 1.

3. The aforementioned public section is, Personalize expert AI by designing its photos, illustrations, and personality. The system according to feature 1.

4. The aforementioned registration unit is This feature allows data providers to register highly specialized data. The system according to feature 1.

5. The aforementioned collection department, The company collects usage fees for its AI through subscriptions, and pays a portion of these fees as compensation to the AI's creators and data providers. The system according to feature 1.

6. The generating unit is It estimates the user's emotions and adjusts the timing of expert AI generation based on the estimated user emotions. The system according to feature 1.

7. The generating unit is When generating expert AI, the system analyzes the user's past usage history to select the optimal generation method. The system according to feature 1.

8. The generating unit is When generating expert AI, incorporate the latest research findings in specific fields. The system according to feature 1.

9. The generating unit is It estimates the user's emotions and determines the priority of expert AI generated based on those estimated emotions. The system according to feature 1.

10. The generating unit is When generating expert AI, the system prioritizes learning from highly relevant data, taking into account the user's geographical location. The system according to feature 1.

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