System

Abstract

An object of a system according to an embodiment is to automatically select an optimum AI service on the basis of a request from a user and link the service.SOLUTION: A system according to an embodiment includes generated AI, a user tendency learning unit, a prompt generation unit, a AI service linking unit, and a data-providing unit. The generation AI analyzes the user's request and selects an optimal AI service. The user tendency learning unit learns the tendency of the user analyzed by the generated AI. The prompt generation unit generates an optimal prompt on the basis of the tendency learned by the user tendency learning unit. The AI service linking unit transmits the prompt generated by the prompt generation unit to each AI service, and links the prompt with the payment service. The data-providing unit provides each AI provider with learning data and processing resources.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem of a large number of AI services being available, making it difficult for users to select the most suitable service.

[0005] The system according to the embodiment aims to automatically select and link the most suitable AI service based on the user's request. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a user tendency learning unit, a prompt generation unit, an AI service linkage unit, and a data provision unit. The generation AI analyzes user requests and selects the optimal AI service. The user tendency learning unit learns the user tendencies analyzed by the generation AI. The prompt generation unit generates optimal prompts based on the tendencies learned by the user tendency learning unit. The AI ​​service linkage unit sends the prompts generated by the prompt generation unit to each AI service and links them. The data provision unit provides learning data and processing resources to each AI operator. [Effects of the Invention]

[0007] The system according to the embodiment can automatically select and link the most suitable AI services based on the user's requests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The generation AI service according to an embodiment of the present invention is a generation AI service that acts as a contact point for all of the numerous AI services currently available. This generation AI service learns user tendencies, automatically selects the most appropriate AI, and generates and sends the most appropriate prompts to each service. This allows the generation AI service to provide users with efficient and effective use of AI services.

[0029] A generation AI service according to an embodiment includes a generation AI, a user tendency learning unit, a prompt generation unit, an AI service linkage unit, and a data providing unit. The generation AI analyzes a user's request and selects the optimal AI service. For example, if a user requests "I want to edit an image," the generation AI selects an AI service specialized in image editing. The generation AI can also analyze the user's voice command and select the optimal AI service. The user tendency learning unit learns the user's tendencies analyzed by the generation AI. For example, if a user has performed many image edits in the past, the user tendency learning unit learns those tendencies and quickly selects the optimal AI service for subsequent image editing requests. The prompt generation unit generates an optimal prompt based on the tendencies learned by the user tendency learning unit. For example, the generation AI receives a prompt such as "Please summarize the main points of this passage" and extracts the main points of the answer to create a summary. The AI ​​service linkage unit transmits the prompt generated by the prompt generation unit to each AI service and links them. For example, if a user requests "I want an image edited and then text generated," the AI ​​service linking unit first selects an image editing AI service, and then selects and links a text generation AI service after the editing is complete. The data providing unit provides each AI provider with learning data and processing resources. For example, the generation AI service provides collected user data to the AI ​​provider, which then uses the data to improve the accuracy of the AI ​​model. Furthermore, by using the processing resources provided by the generation AI service, the AI ​​provider can utilize a high-performance computing environment. As a result, the generation AI service according to the embodiment can provide efficient and effective use of AI services by selecting and linking the optimal AI service based on the user's request.

[0030] The generation AI can analyze the user's voice commands and select the optimal AI service. For example, if the user verbally commands "edit an image," the generation AI analyzes the voice command and selects an AI service specialized in image editing. It uses voice recognition technology to accurately understand the user's intent and provide the appropriate service. The generation AI also has enhanced voice command analysis capabilities, allowing it to analyze each instruction and select the optimal AI service, even when the user issues multiple instructions at once. For example, it can handle multiple instructions such as "edit an image, then generate text." The generation AI also incorporates technology to remove background sounds and noise when analyzing the user's voice commands, achieving more accurate voice recognition. This allows the generation AI to select the optimal AI service based on the user's voice command.

[0031] Based on a user's past usage history, the generation AI can predict the next AI service they will need and prepare it in advance. For example, the generation AI can analyze the history of AI services that the user has frequently used in the past and predict the next service they will need. For example, if a user frequently edits images, the generation AI can prepare an image editing AI service in advance the next time they use the service. The generation AI can also predict the AI ​​service the user will use at a specific time of day based on their usage history and prepare the optimal service for that time in advance. For example, if a user uses a text generation AI service every morning, the generation AI can prepare that service in the morning. The generation AI can also analyze a user's usage history and find specific patterns. For example, if a user always generates text after editing images, the generation AI can learn that pattern and prepare both services in advance the next time they use the service. This allows the generation AI to predict the next AI service they will need based on the user's past usage history and prepare them in advance.

[0032] The generation AI can select region-specific AI services based on the user's geographical location information. For example, the generation AI analyzes the user's current location and provides AI services specialized for that region. For example, a tourist guide AI service is selected for a user who is in a tourist spot. The generation AI also selects an AI service that provides local weather and traffic information based on the geographical location information. For example, if the user is out and about, it provides weather forecasts and traffic congestion information. The generation AI also selects an AI service that provides region-specific events and services. For example, if the user is in a specific region, it selects an AI service that provides information on events being held in that region. This allows the generation AI to select region-specific AI services based on the user's geographical location information.

[0033] The generating AI can analyze the user's device information and select an AI service optimized for the device. For example, the generating AI analyzes the user's device information and provides an AI service optimized for that device. For example, for a user using a smartphone, it selects an AI service optimized for mobile devices. The generating AI also selects the optimal AI service based on the device's performance and specifications. For example, for a user using a high-performance device, it provides an AI service that consumes a lot of resources. The generating AI also analyzes the device's usage status and provides the optimal AI service. For example, if the battery is low, it selects an AI service that consumes less battery power. This allows the generating AI to select an AI service optimized for the device based on the user's device information.

[0034] The generative AI can analyze a user's social media activity and select the optimal AI service based on their interests. For example, the generative AI can analyze a user's social media posts to identify their interests. For example, if a user frequently posts about travel, it can select a travel-related AI service. The generative AI can also analyze the user's following and like history on social media to identify their interests. For example, if a user follows many cooking-related accounts, it can select a cooking recipe AI service. The generative AI can also monitor the user's social media activity in real time and select the optimal AI service based on changes in their interests. For example, if a user has recently become interested in sports, it can provide a sports-related AI service. This allows the generative AI to select the optimal AI service based on the user's social media activity.

[0035] The generation AI can analyze the user's biometric information and select the optimal AI service according to the stress level. For example, the generation AI can analyze the user's heart rate and electrodermal activity to estimate the stress level. For example, if the stress level is high, it can select an AI service specialized in relaxation. The generation AI can also monitor the user's health condition based on the biometric information and provide the optimal AI service. For example, if the heart rate is abnormally high, it can select a health management AI service. The generation AI can also analyze the user's biometric information in real time and select the optimal AI service according to changes in the stress level. For example, if stress suddenly increases, it can provide an AI service for refreshing. This allows the generation AI to select an AI service according to the user's stress level based on the user's biometric information.

[0036] The generation AI can analyze a user's purchasing history and select the optimal AI service based on their purchasing trends. For example, the generation AI analyzes a user's purchasing history and identifies their purchasing trends. For example, if the user frequently purchases health foods, it selects a health management AI service. The generation AI also identifies the user's interests based on the purchasing history and provides the optimal AI service. For example, if the user purchases many travel-related products, it selects a travel guide AI service. The generation AI also analyzes the user's purchasing history in real time and selects the optimal AI service according to changes in purchasing trends. For example, if the user has recently started purchasing sports equipment, it provides a sports-related AI service. This allows the generation AI to select the optimal AI service based on the user's purchasing history.

[0037] The generation AI can analyze the user's calendar information and select the optimal AI service based on the schedule. The generation AI, for example, analyzes the user's calendar information and provides the optimal AI service based on the schedule. For example, it selects a reminder AI service before a meeting. The generation AI also provides an AI service based on the calendar information that matches the user's schedule. For example, if there is a travel plan, it selects a travel guide AI service. The generation AI also analyzes the user's calendar information in real time and selects the optimal AI service based on changes in the schedule. For example, it provides an AI service to respond to sudden changes in plans. This allows the generation AI to select an AI service based on the schedule based on the user's calendar information.

[0038] The generation AI can automatically convert data formats between each AI service, realizing seamless collaboration. For example, the generation AI adds a function to automatically convert data formats between each AI service. For example, it converts output data from an image editing AI service into input data for a text generation AI service. The generation AI also strengthens the automatic data format conversion function to ensure data compatibility between different AI services. For example, it converts output data from a voice recognition AI service into input data for a translation AI service. The generation AI also automatically converts data formats between each AI service in real time, realizing seamless collaboration. For example, it automatically converts data when a user uses multiple AI services in succession. This allows the generation AI to automatically convert data formats between each AI service, realizing seamless collaboration.

[0039] The generative AI can integrate the processing results of each AI service and provide a single integrated result to the user. For example, the generative AI can integrate the processing results of each AI service and provide a single integrated result to the user. For example, it can integrate the results of an image editing AI service and a text generation AI service and provide it to the user. The generative AI can also integrate the processing results of each AI service in real time and provide a single integrated result to the user. For example, it can integrate the results of a speech recognition AI service and a translation AI service and provide it to the user. The generative AI can also analyze the processing results of each AI service and generate the optimal integrated result. For example, it can combine the results of multiple AI services and provide it to the user. This allows the generative AI to integrate the processing results of each AI service and provide a single integrated result to the user.

[0040] Generative AI can link AI services in different languages ​​to support international use. For example, generative AI links AI services in different languages ​​to support international use. For example, an English speech recognition AI service links with a Japanese translation AI service. Generative AI also ensures data compatibility between AI services in different languages ​​to achieve seamless linkage. For example, a French text generation AI service links with a Spanish speech synthesis AI service. Generative AI also links AI services in different languages ​​in real time to support international use. For example, if a user speaks multiple languages, it links AI services corresponding to each language. This allows generative AI to link AI services in different languages ​​to support international use.

[0041] Generative AI can link AI services from different industries to create new business models. For example, generative AI links AI services from different industries to create new business models. For example, linking AI services from the medical field with AI services from the financial field. Generative AI also ensures data compatibility between AI services from different industries to achieve seamless collaboration. For example, linking AI services from the education field with AI services from the entertainment field. Generative AI also links AI services from different industries in real time to create new business models. For example, when a user uses services that span multiple industries, it links AI services corresponding to each industry. In this way, generative AI can link AI services from different industries to create new business models.

[0042] The generating AI provides each AI provider with anonymized user data, allowing it to enrich its training data while protecting privacy. For example, the generating AI anonymizes user data and provides it to each AI provider. For example, it may provide user usage history data after deleting personal information. The generating AI also uses the anonymized data to allow each AI provider to enrich its training data. For example, it may analyze user behavior patterns and improve the accuracy of the AI ​​model. The generating AI also anonymizes user data in real time and provides it to each AI provider. For example, it may provide the latest data while protecting user privacy. This allows the generating AI to enrich its training data while protecting user privacy.

[0043] The generative AI provides each AI provider with training data specialized for a specific industry, thereby improving the accuracy of the industry-specific AI model. For example, the generative AI collects training data specialized for a specific industry and provides it to each AI provider. For example, it collects data from the medical field to improve the accuracy of a medical AI model. The generative AI also uses the industry-specific data to allow each AI provider to improve the accuracy of their AI model. For example, it uses data from the financial field to improve the accuracy of a financial AI model. The generative AI also collects data specialized for a specific industry in real time and provides it to each AI provider. For example, it uses the latest industry data to improve the accuracy of an AI model. This allows the generative AI to provide training data specialized for a specific industry and improve the accuracy of an industry-specific AI model.

[0044] The generative AI can provide each AI provider with user data from different regions and support the development of region-specific AI models. For example, the generative AI collects user data from different regions and provides it to each AI provider. For example, it collects data from the Asian region and supports the development of an Asia-specific AI model. The generative AI also uses the region-specific data to help each AI provider develop a region-specific AI model. For example, it uses data from the European region to develop a Europe-specific AI model. The generative AI also collects data from different regions in real time and provides it to each AI provider. For example, it uses the latest regional data to develop a region-specific AI model. This allows the generative AI to provide user data from different regions and support the development of region-specific AI models.

[0045] The generative AI can provide each AI provider with data collected from different devices and support the development of device-specific AI models. For example, the generative AI provides data collected from different devices. For example, it uses data collected from smartphones and tablets to develop device-specific AI models. The generative AI also allows each AI provider to develop device-specific AI models using device-specific data. For example, it uses data collected from wearable devices to develop wearable-specific AI models. The generative AI also collects data from different devices in real time and provides it to each AI provider. For example, it uses the latest device data to develop device-specific AI models. This allows the generative AI to provide data collected from different devices and support the development of device-specific AI models.

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

[0047] The generating AI can analyze the user's health data and select the optimal AI service based on their health condition. For example, it can analyze data obtained from the user's fitness tracker and select a fitness AI service if the user is not exercising enough. The generating AI can also analyze the user's food records and select a nutritional management AI service if the user's nutritional balance is unbalanced. Furthermore, the generating AI can analyze the user's sleep data and provide a sleep improvement AI service if the user's sleep quality is poor. This allows the generating AI to select the optimal AI service based on the user's health data.

[0048] The generation AI can analyze the user's hobbies and interests and provide relevant AI services. For example, if the user is interested in music, it can select a music generation AI service. If the user is interested in movies, the generation AI can also select a movie recommendation AI service. Furthermore, if the user is interested in reading, the generation AI can also provide a book recommendation AI service. This allows the generation AI to select the optimal AI service based on the user's hobbies and interests.

[0049] The generative AI can analyze the user's learning history and select the optimal AI educational service based on the user's learning progress. For example, if the user is studying mathematics, it can recommend the next topic to study. The generative AI can also analyze the user's learning style and provide the optimal learning method. Furthermore, the generative AI can monitor the user's learning progress based on the user's learning history and adjust the learning plan as needed. This allows the generative AI to select the optimal AI educational service based on the user's learning history.

[0050] The generation AI can analyze a user's purchasing history and select the optimal AI shopping service based on their purchasing trends. For example, if a user frequently purchases fashion items, it can select a fashion recommendation AI service. Alternatively, if a user purchases health foods, the generation AI can select a health food recommendation AI service. Furthermore, the generation AI can predict the next product the user is likely to purchase based on the user's purchasing history and provide an appropriate AI shopping service. This allows the generation AI to select the optimal AI shopping service based on the user's purchasing history.

[0051] The generating AI can analyze the user's exercise data and select the optimal AI fitness service based on their exercise habits. For example, if the user likes running, the generating AI can select a running training AI service. If the user likes yoga, the generating AI can also select a yoga lesson AI service. Furthermore, the generating AI can monitor the user's exercise progress based on the user's exercise data and adjust the exercise plan as needed. This allows the generating AI to select the optimal AI fitness service based on the user's exercise data.

[0052] The generation AI can analyze the user's travel history and select the optimal AI travel service based on their travel trends. For example, it can analyze data on places the user has visited in the past and accommodations to recommend the next place they should visit. The generation AI can also analyze the user's travel style and provide the optimal travel plan. Furthermore, the generation AI can monitor the user's travel progress based on the user's travel history and adjust the travel plan as needed. This allows the generation AI to select the optimal AI travel service based on the user's travel history.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The generating AI analyzes the user's request and selects the most appropriate AI service. For example, if the user requests "I want to edit an image," the generating AI selects an AI service that specializes in image editing. The generating AI can also analyze the user's voice commands and select the most appropriate AI service. Step 2: The user tendency learning unit learns the user's tendencies analyzed by the generation AI. For example, if the user has edited many images in the past, the user tendency learning unit will learn those tendencies and quickly select the optimal AI service for subsequent image editing requests. Step 3: The prompt generator generates optimal prompts based on the trends learned by the user trend learning unit. For example, the generator receives a prompt such as "Please summarize the main points of this passage," and extracts the main points of the answer to create a summary. Step 4: The AI ​​service linking unit sends the prompt generated by the prompt generation unit to each AI service and links them together. For example, if a user requests "I want you to edit an image and then generate text," the AI ​​service linking unit first selects the image editing AI service, and after the editing is complete, selects the text generation AI service and links them together. Step 5: The data provider provides each AI provider with training data and processing resources. For example, the generation AI service provides the AI ​​provider with user data collected, and uses that data to improve the accuracy of the AI ​​model. In addition, by using the processing resources provided by the generation AI service, the AI ​​provider can utilize a high-performance computing environment.

[0055] (Example 2) The generation AI service according to an embodiment of the present invention is a generation AI service that acts as a contact point for all of the numerous AI services currently available. This generation AI service learns user tendencies, automatically selects the most appropriate AI, and generates and sends the most appropriate prompts to each service. This allows the generation AI service to provide users with efficient and effective use of AI services.

[0056] A generation AI service according to an embodiment includes a generation AI, a user tendency learning unit, a prompt generation unit, an AI service linkage unit, and a data providing unit. The generation AI analyzes a user's request and selects the optimal AI service. For example, if a user requests "I want to edit an image," the generation AI selects an AI service specialized in image editing. The generation AI can also analyze the user's voice command and select the optimal AI service. The user tendency learning unit learns the user's tendencies analyzed by the generation AI. For example, if a user has performed many image edits in the past, the user tendency learning unit learns those tendencies and quickly selects the optimal AI service for subsequent image editing requests. The prompt generation unit generates an optimal prompt based on the tendencies learned by the user tendency learning unit. For example, the generation AI receives a prompt such as "Please summarize the main points of this passage" and extracts the main points of the answer to create a summary. The AI ​​service linkage unit transmits the prompt generated by the prompt generation unit to each AI service and links them. For example, if a user requests "I want an image edited and then text generated," the AI ​​service linking unit first selects an image editing AI service, and then selects and links a text generation AI service after the editing is complete. The data providing unit provides each AI provider with learning data and processing resources. For example, the generation AI service provides collected user data to the AI ​​provider, which then uses the data to improve the accuracy of the AI ​​model. Furthermore, by using the processing resources provided by the generation AI service, the AI ​​provider can utilize a high-performance computing environment. As a result, the generation AI service according to the embodiment can provide efficient and effective use of AI services by selecting and linking the optimal AI service based on the user's request.

[0057] The generation AI can analyze the user's voice commands and select the optimal AI service. For example, if the user verbally commands "edit an image," the generation AI analyzes the voice command and selects an AI service specialized in image editing. It uses voice recognition technology to accurately understand the user's intent and provide the appropriate service. The generation AI also has enhanced voice command analysis capabilities, allowing it to analyze each instruction and select the optimal AI service, even when the user issues multiple instructions at once. For example, it can handle multiple instructions such as "edit an image, then generate text." The generation AI also incorporates technology to remove background sounds and noise when analyzing the user's voice commands, achieving more accurate voice recognition. This allows the generation AI to select the optimal AI service based on the user's voice command.

[0058] Based on a user's past usage history, the generation AI can predict the next AI service they will need and prepare it in advance. For example, the generation AI can analyze the history of AI services that the user has frequently used in the past and predict the next service they will need. For example, if a user frequently edits images, the generation AI can prepare an image editing AI service in advance the next time they use the service. The generation AI can also predict the AI ​​service the user will use at a specific time of day based on their usage history and prepare the optimal service for that time in advance. For example, if a user uses a text generation AI service every morning, the generation AI can prepare that service in the morning. The generation AI can also analyze a user's usage history and find specific patterns. For example, if a user always generates text after editing images, the generation AI can learn that pattern and prepare both services in advance the next time they use the service. This allows the generation AI to predict the next AI service they will need based on the user's past usage history and prepare them in advance.

[0059] The generative AI can use its emotion estimation function to analyze the user's emotional state and select the optimal AI service based on that emotion. For example, the generative AI can analyze the user's facial expressions and vocal tone to estimate their emotional state. For example, if the user is feeling stressed, it can select an AI service specialized in relaxation. The generative AI can also use its emotion estimation function to select an AI service suitable for creative tasks when the user is in a positive emotional state. For example, if the user is happy, it can provide creative services such as image editing and music generation. The generative AI can also monitor the user's emotional state in real time and select the optimal AI service based on changes in emotion. For example, if the user is tired, it can provide an AI service that helps refresh them. This allows the generative AI to select the optimal AI service based on the user's emotional state.

[0060] The generation AI can select region-specific AI services based on the user's geographical location information. For example, the generation AI analyzes the user's current location and provides AI services specialized for that region. For example, a tourist guide AI service is selected for a user who is in a tourist spot. The generation AI also selects an AI service that provides local weather and traffic information based on the geographical location information. For example, if the user is out and about, it provides weather forecasts and traffic congestion information. The generation AI also selects an AI service that provides region-specific events and services. For example, if the user is in a specific region, it selects an AI service that provides information on events being held in that region. This allows the generation AI to select region-specific AI services based on the user's geographical location information.

[0061] The generating AI can analyze the user's device information and select an AI service optimized for the device. For example, the generating AI analyzes the user's device information and provides an AI service optimized for that device. For example, for a user using a smartphone, it selects an AI service optimized for mobile devices. The generating AI also selects the optimal AI service based on the device's performance and specifications. For example, for a user using a high-performance device, it provides an AI service that consumes a lot of resources. The generating AI also analyzes the device's usage status and provides the optimal AI service. For example, if the battery is low, it selects an AI service that consumes less battery power. This allows the generating AI to select an AI service optimized for the device based on the user's device information.

[0062] The generation AI uses its emotion estimation function to analyze the user's emotions in real time as they type and make suggestions to elicit positive emotions. For example, the generation AI analyzes the user's facial expressions and tone of voice as they type and estimates their emotional state in real time. For example, if the user is nervous, it makes suggestions to relax. The generation AI also uses its emotion estimation function to make suggestions to help the user have positive emotions. For example, if the user is feeling stressed, it suggests activities to refresh them. The generation AI also monitors the user's emotional state in real time and provides interactions to elicit positive emotions. For example, if the user is feeling depressed, it displays an encouraging message. This allows the generation AI to analyze the user's emotions in real time as they type and make suggestions to elicit positive emotions.

[0063] The generative AI can analyze a user's social media activity and select the optimal AI service based on their interests. For example, the generative AI can analyze a user's social media posts to identify their interests. For example, if a user frequently posts about travel, it can select a travel-related AI service. The generative AI can also analyze the user's following and like history on social media to identify their interests. For example, if a user follows many cooking-related accounts, it can select a cooking recipe AI service. The generative AI can also monitor the user's social media activity in real time and select the optimal AI service based on changes in their interests. For example, if a user has recently become interested in sports, it can provide a sports-related AI service. This allows the generative AI to select the optimal AI service based on the user's social media activity.

[0064] The generation AI can analyze the user's biometric information and select the optimal AI service according to the stress level. For example, the generation AI can analyze the user's heart rate and electrodermal activity to estimate the stress level. For example, if the stress level is high, it can select an AI service specialized in relaxation. The generation AI can also monitor the user's health condition based on the biometric information and provide the optimal AI service. For example, if the heart rate is abnormally high, it can select a health management AI service. The generation AI can also analyze the user's biometric information in real time and select the optimal AI service according to changes in the stress level. For example, if stress suddenly increases, it can provide an AI service for refreshing. This allows the generation AI to select an AI service according to the user's stress level based on the user's biometric information.

[0065] The generation AI can use the emotion estimation function to learn the user's emotional history and select the optimal AI service in response to emotional changes. For example, the generation AI analyzes the user's emotional history and learns patterns of emotional changes. For example, it provides a relaxation AI service during times when the user frequently feels stressed. The generation AI also uses the emotion estimation function to select the optimal AI service based on the user's emotional history. For example, it provides a creative AI service during times when the user has positive emotions. The generation AI also monitors the user's emotional history in real time and selects the optimal AI service in response to emotional changes. For example, it selects an AI service that provides encouraging messages if the user is feeling down. This allows the generation AI to select an AI service in response to emotional changes based on the user's emotional history.

[0066] The generation AI can analyze a user's purchasing history and select the optimal AI service based on their purchasing trends. For example, the generation AI analyzes a user's purchasing history and identifies their purchasing trends. For example, if the user frequently purchases health foods, it selects a health management AI service. The generation AI also identifies the user's interests based on the purchasing history and provides the optimal AI service. For example, if the user purchases many travel-related products, it selects a travel guide AI service. The generation AI also analyzes the user's purchasing history in real time and selects the optimal AI service according to changes in purchasing trends. For example, if the user has recently started purchasing sports equipment, it provides a sports-related AI service. This allows the generation AI to select the optimal AI service based on the user's purchasing history.

[0067] The generation AI can analyze the user's calendar information and select the optimal AI service based on the schedule. The generation AI, for example, analyzes the user's calendar information and provides the optimal AI service based on the schedule. For example, it selects a reminder AI service before a meeting. The generation AI also provides an AI service based on the calendar information that matches the user's schedule. For example, if there is a travel plan, it selects a travel guide AI service. The generation AI also analyzes the user's calendar information in real time and selects the optimal AI service based on changes in the schedule. For example, it provides an AI service to respond to sudden changes in plans. This allows the generation AI to select an AI service based on the schedule based on the user's calendar information.

[0068] The generation AI uses the emotion estimation function to generate prompts based on the user's emotions and can select the optimal AI service according to the emotions. For example, the generation AI uses the emotion estimation function to generate prompts based on the user's emotional state. For example, if the user is feeling stressed, it generates a prompt specialized for relaxation. The generation AI also analyzes the user's emotional state in real time and generates prompts according to the emotions. For example, if the user is feeling positive, it generates a creative prompt. The generation AI also generates prompts according to the user's emotions based on the emotion estimation data and selects the optimal AI service. For example, if the user is feeling down, it generates a prompt that provides an encouraging message. This allows the generation AI to generate the optimal prompt based on the user's emotions and select an AI service according to the emotions.

[0069] The generation AI can automatically convert data formats between each AI service, realizing seamless collaboration. For example, the generation AI adds a function to automatically convert data formats between each AI service. For example, it converts output data from an image editing AI service into input data for a text generation AI service. The generation AI also strengthens the automatic data format conversion function to ensure data compatibility between different AI services. For example, it converts output data from a voice recognition AI service into input data for a translation AI service. The generation AI also automatically converts data formats between each AI service in real time, realizing seamless collaboration. For example, it automatically converts data when a user uses multiple AI services in succession. This allows the generation AI to automatically convert data formats between each AI service, realizing seamless collaboration.

[0070] The generative AI can integrate the processing results of each AI service and provide a single integrated result to the user. For example, the generative AI can integrate the processing results of each AI service and provide a single integrated result to the user. For example, it can integrate the results of an image editing AI service and a text generation AI service and provide it to the user. The generative AI can also integrate the processing results of each AI service in real time and provide a single integrated result to the user. For example, it can integrate the results of a speech recognition AI service and a translation AI service and provide it to the user. The generative AI can also analyze the processing results of each AI service and generate the optimal integrated result. For example, it can combine the results of multiple AI services and provide it to the user. This allows the generative AI to integrate the processing results of each AI service and provide a single integrated result to the user.

[0071] The generative AI can use the emotion estimation function to analyze the user's emotional response to the processing results of each AI service and reflect it in the next process. For example, the generative AI can use the emotion estimation function to analyze the user's emotional response to the processing results of each AI service. For example, it can analyze whether the user is satisfied with the image editing results and reflect this in the next process. The generative AI can also monitor the user's emotional response in real time and reflect it in the processing results of each AI service. For example, if the user is dissatisfied with the text generation results, it can improve them in the next process. The generative AI can also optimize the processing results of each AI service based on the emotion estimation data. For example, it can analyze the user's emotional response and adjust the next process to elicit positive emotions. This allows the generative AI to analyze the user's emotional response to the processing results of each AI service and reflect it in the next process.

[0072] Generative AI can link AI services in different languages ​​to support international use. For example, generative AI links AI services in different languages ​​to support international use. For example, an English speech recognition AI service links with a Japanese translation AI service. Generative AI also ensures data compatibility between AI services in different languages ​​to achieve seamless linkage. For example, a French text generation AI service links with a Spanish speech synthesis AI service. Generative AI also links AI services in different languages ​​in real time to support international use. For example, if a user speaks multiple languages, it links AI services corresponding to each language. This allows generative AI to link AI services in different languages ​​to support international use.

[0073] Generative AI can link AI services from different industries to create new business models. For example, generative AI links AI services from different industries to create new business models. For example, linking AI services from the medical field with AI services from the financial field. Generative AI also ensures data compatibility between AI services from different industries to achieve seamless collaboration. For example, linking AI services from the education field with AI services from the entertainment field. Generative AI also links AI services from different industries in real time to create new business models. For example, when a user uses services that span multiple industries, it links AI services corresponding to each industry. In this way, generative AI can link AI services from different industries to create new business models.

[0074] The generation AI can use the emotion estimation function to monitor the user's emotional response to the linkage of each AI service in real time and continuously search for the optimal linkage. The generation AI, for example, uses the emotion estimation function to monitor the user's emotional response to the linkage of each AI service in real time. For example, it analyzes whether the user is satisfied with the linked services. The generation AI also optimizes the linkage of each AI service based on the user's emotional response. For example, if the user is dissatisfied, it adjusts the linkage method. The generation AI also collects emotion estimation data in real time and continuously improves the linkage of each AI service. For example, it analyzes the user's emotional response and searches for the optimal linkage method. This allows the generation AI to monitor the user's emotional response to the linkage of each AI service in real time and continuously search for the optimal linkage.

[0075] The generating AI provides each AI provider with anonymized user data, allowing it to enrich its training data while protecting privacy. For example, the generating AI anonymizes user data and provides it to each AI provider. For example, it may provide user usage history data after deleting personal information. The generating AI also uses the anonymized data to allow each AI provider to enrich its training data. For example, it may analyze user behavior patterns and improve the accuracy of the AI ​​model. The generating AI also anonymizes user data in real time and provides it to each AI provider. For example, it may provide the latest data while protecting user privacy. This allows the generating AI to enrich its training data while protecting user privacy.

[0076] The generative AI provides each AI provider with training data specialized for a specific industry, thereby improving the accuracy of the industry-specific AI model. For example, the generative AI collects training data specialized for a specific industry and provides it to each AI provider. For example, it collects data from the medical field to improve the accuracy of a medical AI model. The generative AI also uses the industry-specific data to allow each AI provider to improve the accuracy of their AI model. For example, it uses data from the financial field to improve the accuracy of a financial AI model. The generative AI also collects data specialized for a specific industry in real time and provides it to each AI provider. For example, it uses the latest industry data to improve the accuracy of an AI model. This allows the generative AI to provide training data specialized for a specific industry and improve the accuracy of an industry-specific AI model.

[0077] The generative AI can use the emotion estimation function to collect user emotional data and provide emotion-based learning data. The generative AI, for example, uses the emotion estimation function to collect user emotional data. For example, it analyzes the user's facial expressions and vocal tone to collect emotion data. Furthermore, the generative AI allows each AI provider to enrich emotion-based learning data based on the emotion data. For example, it analyzes the user's emotional reactions to improve the accuracy of emotion-based AI models. Furthermore, the generative AI collects user emotional data in real time and provides it to each AI provider. For example, it monitors the user's emotional changes in real time and provides the latest emotion data. This allows the generative AI to collect user emotional data and provide emotion-based learning data.

[0078] The generative AI can provide each AI provider with user data from different regions and support the development of region-specific AI models. For example, the generative AI collects user data from different regions and provides it to each AI provider. For example, it collects data from the Asian region and supports the development of an Asia-specific AI model. The generative AI also uses the region-specific data to help each AI provider develop a region-specific AI model. For example, it uses data from the European region to develop a Europe-specific AI model. The generative AI also collects data from different regions in real time and provides it to each AI provider. For example, it uses the latest regional data to develop a region-specific AI model. This allows the generative AI to provide user data from different regions and support the development of region-specific AI models.

[0079] The generative AI can provide each AI provider with data collected from different devices and support the development of device-specific AI models. For example, the generative AI provides data collected from different devices. For example, it uses data collected from smartphones and tablets to develop device-specific AI models. The generative AI also allows each AI provider to develop device-specific AI models using device-specific data. For example, it uses data collected from wearable devices to develop wearable-specific AI models. The generative AI also collects data from different devices in real time and provides it to each AI provider. For example, it uses the latest device data to develop device-specific AI models. This allows the generative AI to provide data collected from different devices and support the development of device-specific AI models.

[0080] The generative AI can use its emotion estimation function to collect user emotional data in real time and provide processing resources based on the emotions. The generative AI, for example, uses its emotion estimation function to collect user emotional data in real time. For example, it analyzes the user's facial expressions and vocal tone to collect emotional data. The generative AI then allows each AI provider to provide emotion-based processing resources based on the emotional data. For example, it analyzes the user's emotional reactions to improve the accuracy of emotion-based AI models. The generative AI also collects user emotional data in real time and provides it to each AI provider. For example, it monitors the user's emotional changes in real time and provides the latest emotional data. This allows the generative AI to collect user emotional data in real time and provide processing resources based on the emotions.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The generating AI can analyze the user's health data and select the optimal AI service based on their health condition. For example, it can analyze data obtained from the user's fitness tracker and select a fitness AI service if the user is not exercising enough. The generating AI can also analyze the user's food records and select a nutritional management AI service if the user's nutritional balance is unbalanced. Furthermore, the generating AI can analyze the user's sleep data and provide a sleep improvement AI service if the user's sleep quality is poor. This allows the generating AI to select the optimal AI service based on the user's health data.

[0083] The generation AI can analyze the user's hobbies and interests and provide relevant AI services. For example, if the user is interested in music, it can select a music generation AI service. If the user is interested in movies, the generation AI can also select a movie recommendation AI service. Furthermore, if the user is interested in reading, the generation AI can also provide a book recommendation AI service. This allows the generation AI to select the optimal AI service based on the user's hobbies and interests.

[0084] Using its emotion estimation function, the generative AI can provide entertainment content according to the user's emotional state. For example, if the user is feeling stressed, it can provide relaxation music or a meditation guide. The generative AI can also recommend fun movies or games if the user is in a positive emotional state. Furthermore, the generative AI can monitor the user's emotional state in real time and provide optimal entertainment content according to changes in emotion. This allows the generative AI to provide optimal entertainment content based on the user's emotional state.

[0085] The generative AI can analyze the user's learning history and select the optimal AI educational service based on the user's learning progress. For example, if the user is studying mathematics, it can recommend the next topic to study. The generative AI can also analyze the user's learning style and provide the optimal learning method. Furthermore, the generative AI can monitor the user's learning progress based on the user's learning history and adjust the learning plan as needed. This allows the generative AI to select the optimal AI educational service based on the user's learning history.

[0086] Using its emotion estimation function, the generative AI can provide feedback according to the user's emotional state. For example, if the user is feeling anxious about the progress of a project, the generative AI can send an encouraging message. Alternatively, if the user is happy about a success, the generative AI can provide a congratulatory message. Furthermore, the generative AI can monitor the user's emotional state in real time and provide appropriate feedback according to changes in emotion. This allows the generative AI to provide optimal feedback based on the user's emotional state.

[0087] The generation AI can analyze a user's purchasing history and select the optimal AI shopping service based on their purchasing trends. For example, if a user frequently purchases fashion items, it can select a fashion recommendation AI service. Alternatively, if a user purchases health foods, the generation AI can select a health food recommendation AI service. Furthermore, the generation AI can predict the next product the user is likely to purchase based on the user's purchasing history and provide an appropriate AI shopping service. This allows the generation AI to select the optimal AI shopping service based on the user's purchasing history.

[0088] Using its emotion estimation function, the generative AI can provide customer support that is tailored to the user's emotional state. For example, if the user is dissatisfied, it can respond quickly and courteously. The generative AI can also suggest additional services or products if the user is satisfied. Furthermore, the generative AI can monitor the user's emotional state in real time and provide optimal customer support in response to changes in emotion. This allows the generative AI to provide optimal customer support based on the user's emotional state.

[0089] The generating AI can analyze the user's exercise data and select the optimal AI fitness service based on their exercise habits. For example, if the user likes running, the generating AI can select a running training AI service. If the user likes yoga, the generating AI can also select a yoga lesson AI service. Furthermore, the generating AI can monitor the user's exercise progress based on the user's exercise data and adjust the exercise plan as needed. This allows the generating AI to select the optimal AI fitness service based on the user's exercise data.

[0090] Using its emotion estimation function, the generative AI can suggest relaxation methods according to the user's emotional state. For example, if the user is feeling stressed, it can provide guidance on meditation or deep breathing. If the user is tired, the generative AI can also recommend relaxing music or videos. Furthermore, the generative AI can monitor the user's emotional state in real time and suggest optimal relaxation methods according to changes in emotion. This allows the generative AI to suggest optimal relaxation methods based on the user's emotional state.

[0091] The generation AI can analyze the user's travel history and select the optimal AI travel service based on their travel trends. For example, it can analyze data on places the user has visited in the past and accommodations to recommend the next place they should visit. The generation AI can also analyze the user's travel style and provide the optimal travel plan. Furthermore, the generation AI can monitor the user's travel progress based on the user's travel history and adjust the travel plan as needed. This allows the generation AI to select the optimal AI travel service based on the user's travel history.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The generating AI analyzes the user's request and selects the most appropriate AI service. For example, if the user requests "I want to edit an image," the generating AI selects an AI service that specializes in image editing. The generating AI can also analyze the user's voice commands and select the most appropriate AI service. Step 2: The user tendency learning unit learns the user's tendencies analyzed by the generation AI. For example, if the user has edited many images in the past, the user tendency learning unit will learn those tendencies and quickly select the optimal AI service for subsequent image editing requests. Step 3: The prompt generator generates optimal prompts based on the trends learned by the user trend learning unit. For example, the generator receives a prompt such as "Please summarize the main points of this passage," and extracts the main points of the answer to create a summary. Step 4: The AI ​​service linking unit sends the prompt generated by the prompt generation unit to each AI service and links them together. For example, if a user requests "I want you to edit an image and then generate text," the AI ​​service linking unit first selects the image editing AI service, and after the editing is complete, selects the text generation AI service and links them together. Step 5: The data provider provides each AI provider with training data and processing resources. For example, the generation AI service provides the AI ​​provider with user data collected, and uses that data to improve the accuracy of the AI ​​model. In addition, by using the processing resources provided by the generation AI service, the AI ​​provider can utilize a high-performance computing environment.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Equipped with generative AI, The generated AI is A generation AI that analyzes user requests and selects the optimal AI service. a user tendency learning unit that learns the user tendency analyzed by the generation AI; a prompt generation unit that generates an optimal prompt based on the tendency learned by the user tendency learning unit; an AI service linking unit that transmits the prompt generated by the prompt generating unit to each AI service and links them; A data provider that provides learning data and processing resources to each AI operator. A system characterized by:

2. The generated AI is Analyzing the user's voice command and selecting the most suitable AI service 2. The system of claim 1.

3. The generated AI is Based on the user's past usage history, predict the AI ​​service that will be needed next and prepare it in advance.

2. The system of claim 1.

4. The generated AI is Analyzing the emotional state of the user and selecting the optimal AI service according to the emotion 2. The system of claim 1.

5. The generated AI is Selecting the AI ​​service specific to a region based on the geographic location information of the user 2. The system of claim 1.

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