System

Abstract

An object of the system according to the embodiment is to centrally perform customization, sales, trial use, and management of a usage fee of a generated AI.SOLUTION: A system according to an embodiment includes a generation unit, a customization unit, a sales unit, a trial unit, and a management unit. The generation unit creates a generation AI. The customization unit customizes the generated AI created by the generation unit based on a theme or a request. The sales unit sells the generated AI customized by the customization unit on the Internet. The trial unit allows the purchaser to try the prototype of the generated AI sold by the sales unit. The management unit manages a usage fee of the generated AI tried by the trial unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, customization, sales, trials, and management of usage fees for generative AI were not managed centrally, making efficient operation difficult.

[0005] The system of the embodiment aims to centrally manage the customization, sales, trial use, and usage fees of generation AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a customization unit, a sales unit, a trial unit, and a management unit. The generation unit creates a generated AI. The customization unit customizes the generated AI created by the generation unit based on a theme or request. The sales unit sells the generated AI customized by the customization unit on the Internet. The trial unit allows purchasers to try out prototypes of the generated AI sold by the sales unit. The management unit manages the usage fees for the generated AI tried out by the trial unit. [Effects of the Invention]

[0007] The system according to the embodiment allows for the customization, sales, trial use, and management of usage fees of generated AI in a unified manner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A generative AI market system according to an embodiment of the present invention is a system for creating, customizing, selling, trialing, and managing usage fees for generative AI. The generative AI market system provides a mechanism whereby engineers create generative AI based on a theme or request, and buyers can try out prototypes and, if they like them, pay a usage fee. For example, in a generative AI market system, engineers create generative AI for a specific industry and sell it online. Buyers can try out the prototype and begin using it only after they are satisfied. Furthermore, the generative AI market system provides generative AIs by theme or request, providing a variety of generative AIs tailored to user needs. This allows the generative AI market system to consistently create, customize, sell, trial, and manage generative AIs. For example, engineers can create generative AIs based on a new theme and add them to the market, thereby providing a variety of generative AIs tailored to user needs. This realizes a new business model that replaces existing e-commerce sites.

[0029] The generating AI market system according to the embodiment includes a generating unit, a customization unit, a sales unit, a trial unit, and a management unit. The generating unit creates a generating AI. For example, the generating unit creates a generating AI based on a specific theme or request. The generating unit can also analyze the generating AI's algorithm and select an optimal generating algorithm. The generating unit can also adjust the generating speed and energy consumption of the generating AI to generate it efficiently. The customization unit customizes the generating AI created by the generating unit based on a theme or request. For example, the customization unit adds functions to the generating AI or changes its design. The customization unit can also estimate a user's emotions and adjust the customization content based on the estimated emotions. The sales unit sells the generating AI customized by the customization unit over the Internet. The sales unit sells the generating AI, for example, through an online marketplace or a dedicated sales site. The sales unit can also estimate a user's emotions and adjust a sales strategy based on the estimated emotions. The trial unit allows a purchaser to try out a prototype of the generating AI sold by the sales unit. The trial unit, for example, sets a trial period and trial conditions, allowing the purchaser to check the performance of the generation AI. The trial unit can also estimate the user's emotions and adjust the trial content based on the estimated emotions. The management unit manages the usage fees for the generation AI tried by the trial unit. The management unit can set, for example, a monthly fee or a fee based on usage volume, and manage the usage fees. The management unit can also estimate the user's emotions and adjust the usage fee management method based on the estimated emotions. As a result, the generation AI market system according to the embodiment can consistently create, customize, sell, try out, and manage the generation AI. For example, the generation unit can analyze the generation AI's algorithm and select the optimal generation algorithm. The customization unit can customize the generation AI based on a theme or request. The sales unit can sell the generation AI over the Internet. The trial unit allows the purchaser to try out a prototype of the generation AI. The management unit can manage the usage fees for the generation AI.This allows the Generative AI Market System to consistently create, customize, sell, try out, and manage generative AI.

[0030] The generative AI market system includes an analysis unit that analyzes the generative AI's algorithm. The analysis unit analyzes the generative AI's algorithm. The analysis unit analyzes the generative AI's algorithm, for example, a neural network or a genetic algorithm. The analysis unit can also evaluate the performance of the generative AI's algorithm and select an optimal algorithm. Furthermore, the analysis unit can identify areas for improvement in the generative AI's algorithm and optimize the algorithm. This enables the generative AI market system to analyze the generative AI's algorithm. For example, the analysis unit can analyze the generative AI's algorithm and select an optimal algorithm. The analysis unit can evaluate the performance of the generative AI's algorithm and optimize the algorithm. This enables the generative AI market system to analyze the generative AI's algorithm.

[0031] The generative AI market system includes an evaluation unit that evaluates the generative AI. The evaluation unit evaluates the generative AI. For example, the evaluation unit performs performance evaluation of the generative AI and user satisfaction evaluation. The evaluation unit can also set evaluation criteria for the generative AI and identify areas for improvement of the generative AI based on the evaluation results. Furthermore, the evaluation unit can also feed back the evaluation results of the generative AI to improve the quality of the generative AI. This enables the generative AI market system to evaluate the generative AI. For example, the evaluation unit can evaluate the performance of the generative AI and identify areas for improvement of the generative AI based on the evaluation results. The evaluation unit can set evaluation criteria for the generative AI and feed back the evaluation results. This enables the generative AI market system to evaluate the generative AI.

[0032] The generative AI market system includes a monitoring unit that monitors the usage status of the generative AI. The monitoring unit monitors the usage status of the generative AI. For example, the monitoring unit monitors the frequency of use and error rate of the generative AI. The monitoring unit can also monitor the usage status of the generative AI in real time and issue an alert if an abnormality is detected. Furthermore, the monitoring unit can analyze the usage status of the generative AI and identify areas for improvement of the generative AI based on the usage status. This enables the generative AI market system to monitor the usage status of the generative AI. For example, the monitoring unit can monitor the frequency of use and error rate of the generative AI and issue an alert if an abnormality is detected. The monitoring unit can analyze the usage status of the generative AI and identify areas for improvement of the generative AI based on the usage status. This enables the generative AI market system to monitor the usage status of the generative AI.

[0033] The generation unit can create a generation AI according to a specific theme or request. For example, the generation unit can create a generation AI according to a theme related to a specific industry or specific requests from a user. For example, the generation unit can create a generation AI that supports diagnostic support and treatment planning for the medical industry. The generation unit can also create a generation AI that supports learning support and teaching material creation for the education industry. Furthermore, the generation unit can create a generation AI that improves content generation and user experience for the entertainment industry. This enables the generation unit to create a generation AI according to a specific theme or request. For example, the generation unit can create a generation AI that supports diagnostic support and treatment planning for the medical industry. The generation unit can create a generation AI that supports learning support and teaching material creation for the education industry. The generation unit can create a generation AI that improves content generation and user experience for the entertainment industry. This enables the generation unit to create a generation AI according to a specific theme or request.

[0034] The customization unit can customize the generation AI based on a theme or request. The customization unit, for example, adds functions to the generation AI or changes its design. The customization unit can, for example, add functions to the generation AI for a specific industry. The customization unit can also change the design of the generation AI based on a user's specific request. The customization unit can also perform customization to improve the performance of the generation AI. This enables the customization unit to customize the generation AI based on a theme or request. For example, the customization unit can add functions to the generation AI for a specific industry. The customization unit can change the design of the generation AI based on a user's specific request. The customization unit can perform customization to improve the performance of the generation AI. This enables the customization unit to customize the generation AI based on a theme or request.

[0035] The sales department can sell the generated AI on the Internet. The sales department can sell the generated AI through, for example, an online marketplace or a dedicated sales site. The sales department can set a sales price for the generated AI and sell it on the Internet. The sales department can also monitor the sales status of the generated AI and adjust the sales strategy. Furthermore, the sales department can estimate user emotions and adjust the sales strategy based on the estimated emotions. This enables the sales department to sell the generated AI on the Internet. For example, the sales department can sell the generated AI through an online marketplace or a dedicated sales site. The sales department can set a sales price for the generated AI and sell it on the Internet. The sales department can monitor the sales status of the generated AI and adjust the sales strategy. This enables the sales department to sell the generated AI on the Internet.

[0036] The trial unit allows a purchaser to try out a prototype of the generative AI. The trial unit, for example, sets a trial period and trial conditions, allowing the purchaser to check the performance of the generative AI. The trial unit, for example, provides a prototype of the generative AI, allowing the purchaser to try it out. The trial unit can also provide feedback on the trial results and identify areas for improvement in the generative AI. Furthermore, the trial unit can estimate a user's emotions and adjust the content of the trial based on the estimated emotions. This allows the trial unit to try out a prototype of the generative AI. For example, the trial unit can set a trial period and trial conditions, allowing the purchaser to check the performance of the generative AI. The trial unit can provide a prototype of the generative AI, allowing the purchaser to try it out. The trial unit can provide feedback on the trial results and identify areas for improvement in the generative AI. This allows the trial unit to try out a prototype of the generative AI.

[0037] The management unit can manage the usage fees for the generation AI. For example, the management unit can set a monthly fee or a fee based on usage volume and manage the usage fees. For example, the management unit can set a payment method for the usage fee and manage the usage fees. The management unit can also monitor the payment status of the usage fee and issue an alert if a payment is not made. Furthermore, the management unit can estimate the user's emotions and adjust the method of managing the usage fee based on the estimated emotions. This enables the management unit to manage the usage fees for the generation AI. For example, the management unit can set a monthly fee or a fee based on usage volume and manage the usage fees. The management unit can set a payment method for the usage fee and manage the usage fees. The management unit can monitor the payment status of the usage fee and issue an alert if a payment is not made. This enables the management unit to manage the usage fees for the generation AI.

[0038] At the time of generation, the generation unit can select the optimal generation algorithm by referring to past performance data of the generation AI. For example, the generation unit preferentially selects an algorithm of a generation AI that has received high evaluations in the past. For example, the generation unit analyzes past performance data of the generation AI and selects the most efficient algorithm. The generation unit can also refer to past failure data of the generation AI and select an algorithm that will avoid similar failures. This enables the generation unit to select the optimal generation algorithm based on past performance data. For example, the generation unit can preferentially select an algorithm of a generation AI that has received high evaluations in the past. The generation unit can analyze past performance data of the generation AI and select the most efficient algorithm. The generation unit can refer to past failure data of the generation AI and select an algorithm that will avoid similar failures. This enables the generation unit to select the optimal generation algorithm based on past performance data.

[0039] The generation unit can create a generation AI specialized for a specific industry or application at the time of generation. For example, the generation unit creates a generation AI that supports diagnostic support and treatment planning for the medical industry. For example, the generation unit creates a generation AI that supports learning support and teaching material creation for the education industry. The generation unit can also create a generation AI that improves content generation and user experience for the entertainment industry. This enables the generation unit to create a generation AI specialized for a specific industry or application. For example, the generation unit can create a generation AI that supports diagnostic support and treatment planning for the medical industry. The generation unit can create a generation AI that supports learning support and teaching material creation for the education industry. The generation unit can create a generation AI that improves content generation and user experience for the entertainment industry. This enables the generation unit to create a generation AI specialized for a specific industry or application.

[0040] The generation unit can adjust the generation algorithm by reflecting user feedback at the time of generation. For example, the generation unit collects feedback from users and adjusts parameters of the generation algorithm. For example, the generation unit identifies and adjusts points to be improved in the generation algorithm based on user evaluations. The generation unit can also analyze the user's usage history and optimize the generation algorithm. This enables the generation unit to adjust the generation algorithm based on user feedback. For example, the generation unit can collect feedback from users and adjust parameters of the generation algorithm. For example, the generation unit can identify and adjust points to be improved in the generation algorithm based on user evaluations. The generation unit can analyze the user's usage history and optimize the generation algorithm. This enables the generation unit to adjust the generation algorithm based on user feedback.

[0041] The generation unit can integrate information from different data sources to create the generative AI at the time of generation. The generation unit, for example, integrates information from multiple databases to improve the accuracy of the generative AI. The generation unit, for example, integrates information from social media and news sites to improve the real-time performance of the generative AI. The generation unit can also integrate a user's past usage history and feedback to personalize the generative AI. This enables the generation unit to create a generative AI that integrates information from different data sources. For example, the generation unit can integrate information from multiple databases to improve the accuracy of the generative AI. The generation unit can integrate information from social media and news sites to improve the real-time performance of the generative AI. The generation unit can integrate a user's past usage history and feedback to personalize the generative AI. This enables the generation unit to create a generative AI that integrates information from different data sources.

[0042] The generation unit can adjust the generation speed of the generated AI during generation to efficiently generate the generated AI. For example, the generation unit can increase the generation speed in response to a user request and quickly provide the generated AI. For example, the generation unit can adjust the generation speed to efficiently use resources. The generation unit can also adjust the generation speed to efficiently generate the generated AI while maintaining its quality. This enables the generation unit to adjust the generation speed of the generated AI. For example, the generation unit can increase the generation speed in response to a user request and quickly provide the generated AI. The generation unit can adjust the generation speed to efficiently use resources. The generation unit can adjust the generation speed to efficiently generate the generated AI while maintaining its quality. This enables the generation unit to adjust the generation speed of the generated AI.

[0043] The generation unit can optimize the energy consumption of the generation AI during generation. The generation unit reduces energy consumption, for example, by improving the efficiency of the generation algorithm. The generation unit minimizes energy consumption, for example, by optimizing the generation process. The generation unit can also use highly energy-efficient hardware to optimize the energy consumption of the generation AI. This enables the generation unit to optimize the energy consumption of the generation AI. For example, the generation unit can reduce energy consumption by improving the efficiency of the generation algorithm. The generation unit can minimize energy consumption by optimizing the generation process. The generation unit can use highly energy-efficient hardware to optimize the energy consumption of the generation AI. This enables the generation unit to optimize the energy consumption of the generation AI.

[0044] During customization, the customization unit can select an optimal customization method by referring to the user's past customization history. The customization unit, for example, analyzes the user's past customization history and suggests an optimal customization method. The customization unit, for example, preferentially displays customization options previously selected by the user. The customization unit can also analyze preferences from the user's past customization history and select an optimal customization method. This enables the customization unit to select an optimal customization method based on the user's past customization history. For example, the customization unit can analyze the user's past customization history and suggest an optimal customization method. The customization unit can preferentially display customization options previously selected by the user. The customization unit can analyze preferences from the user's past customization history and select an optimal customization method. This enables the customization unit to select an optimal customization method based on the user's past customization history.

[0045] During customization, the customization unit can perform customization specialized for a specific industry or application. For example, the customization unit performs customization to support diagnostic support and treatment planning for the medical industry. For example, the customization unit performs customization to support learning support and teaching material creation for the education industry. The customization unit can also perform customization to improve content generation and user experience for the entertainment industry. This enables the customization unit to perform customization specialized for a specific industry or application. For example, the customization unit can perform customization to support diagnostic support and treatment planning for the medical industry. The customization unit can perform customization to support learning support and teaching material creation for the education industry. The customization unit can perform customization to improve content generation and user experience for the entertainment industry. This enables the customization unit to perform customization specialized for a specific industry or application.

[0046] The customization unit can adjust the customization content by reflecting user feedback during customization. For example, the customization unit collects feedback from the user and adjusts parameters of the customization content. For example, the customization unit identifies and adjusts points to be improved in the customization content based on user evaluations. The customization unit can also analyze the user's usage history and optimize the customization content. This enables the customization unit to adjust the customization content based on user feedback. For example, the customization unit can collect feedback from the user and adjust parameters of the customization content. For example, the customization unit can identify and adjust points to be improved in the customization content based on user evaluations. The customization unit can analyze the user's usage history and optimize the customization content. This enables the customization unit to adjust the customization content based on user feedback.

[0047] The customization unit can integrate information from different data sources during customization. The customization unit, for example, integrates information from multiple databases to improve the accuracy of customization. The customization unit, for example, integrates information from social media and news sites to improve the real-time nature of customization. The customization unit can also integrate the user's past usage history and feedback to realize personalized customization. This enables the customization unit to perform customization by integrating information from different data sources. For example, the customization unit can integrate information from multiple databases to improve the accuracy of customization. The customization unit can integrate information from social media and news sites to improve the real-time nature of customization. The customization unit can integrate the user's past usage history and feedback to realize personalized customization. This enables the customization unit to perform customization by integrating information from different data sources.

[0048] The customization unit can adjust the speed of customization during customization to perform customization efficiently. For example, the customization unit can increase the speed of customization in response to a user request and provide customization quickly. For example, the customization unit can adjust the speed of customization to efficiently utilize resources. The customization unit can also adjust the speed of customization to perform customization efficiently while maintaining the quality of customization. This enables the customization unit to adjust the speed of customization. For example, the customization unit can increase the speed of customization in response to a user request and provide customization quickly. The customization unit can adjust the speed of customization to efficiently utilize resources. The customization unit can adjust the speed of customization to perform customization efficiently while maintaining the quality of customization. This enables the customization unit to adjust the speed of customization.

[0049] The customization unit can optimize the energy consumption of the customization during customization. The customization unit reduces the energy consumption by, for example, improving the efficiency of the customization algorithm. The customization unit can minimize the energy consumption by, for example, optimizing the customization process. The customization unit can also optimize the energy consumption of the customization by using highly energy-efficient hardware. This enables the customization unit to optimize the energy consumption of the customization. For example, the customization unit can reduce the energy consumption by improving the efficiency of the customization algorithm. The customization unit can minimize the energy consumption by optimizing the customization process. The customization unit can use highly energy-efficient hardware. This enables the customization unit to optimize the energy consumption of the customization.

[0050] At the time of sale, the sales department can select the optimal sales method by referring to past sales data. For example, the sales department can prioritize selecting sales methods that have received high evaluations in the past. For example, the sales department can analyze past sales data and select the most efficient sales method. The sales department can also reference data on past sales failures to select a sales method that will avoid similar failures. This enables the sales department to select the optimal sales method based on past sales data. For example, the sales department can prioritize selecting sales methods that have received high evaluations in the past. The sales department can analyze past sales data and select the most efficient sales method. The sales department can reference data on past sales failures to select a sales method that will avoid similar failures. This enables the sales department to select the optimal sales method based on past sales data.

[0051] The sales department can develop sales strategies specialized for specific industries and applications when selling. For example, the sales department sells a generative AI that supports diagnostic support and treatment planning to the medical industry. For example, the sales department sells a generative AI that supports learning support and material creation to the education industry. The sales department can also sell a generative AI that improves content generation and user experience to the entertainment industry. This enables the sales department to develop sales strategies specialized for specific industries and applications. For example, the sales department can sell a generative AI that supports diagnostic support and treatment planning to the medical industry. The sales department can sell a generative AI that supports learning support and material creation to the education industry. The sales department can sell a generative AI that improves content generation and user experience to the entertainment industry. This enables the sales department to develop sales strategies specialized for specific industries and applications.

[0052] The sales department can adjust the sales method by reflecting user feedback at the time of sale. For example, the sales department collects feedback from users and adjusts the parameters of the sales method. For example, the sales department identifies areas for improvement in the sales method based on user evaluations and makes adjustments. The sales department can also analyze the user's usage history and optimize the sales method. This enables the sales department to adjust the sales method based on user feedback. For example, the sales department can collect feedback from users and adjust the parameters of the sales method. For example, the sales department can identify areas for improvement in the sales method based on user evaluations and make adjustments. The sales department can analyze the user's usage history and optimize the sales method. This enables the sales department to adjust the sales method based on user feedback.

[0053] The sales department can integrate information from different data sources at the time of sale to formulate a sales strategy. The sales department, for example, integrates information from multiple databases to improve the accuracy of the sales strategy. The sales department, for example, integrates information from social media and news sites to improve the real-time nature of the sales strategy. The sales department can also integrate users' past usage histories and feedback to personalize the sales strategy. This enables the sales department to formulate a sales strategy that integrates information from different data sources. For example, the sales department can integrate information from multiple databases to improve the accuracy of the sales strategy. The sales department can integrate information from social media and news sites to improve the real-time nature of the sales strategy. The sales department can integrate users' past usage histories and feedback to personalize the sales strategy. This enables the sales department to formulate a sales strategy that integrates information from different data sources.

[0054] The sales department can adjust the sales speed at the time of sale to perform the sale efficiently. For example, the sales department can increase the sales speed and quickly provide the generated AI in response to a user request. For example, the sales department can adjust the sales speed to efficiently use resources. The sales department can also adjust the sales speed to efficiently sell while maintaining the quality of the generated AI. This allows the sales department to adjust the sales speed. For example, the sales department can increase the sales speed and quickly provide the generated AI in response to a user request. The sales department can adjust the sales speed to efficiently use resources. The sales department can adjust the sales speed to efficiently sell while maintaining the quality of the generated AI. This allows the sales department to adjust the sales speed.

[0055] The sales department can optimize its energy consumption during sales. For example, the sales department reduces its energy consumption by improving the efficiency of its sales algorithm. For example, the sales department minimizes its energy consumption by optimizing its sales process. Furthermore, the sales department can also use highly energy-efficient hardware to optimize its energy consumption during sales. This enables the sales department to optimize its energy consumption during sales. For example, the sales department can reduce its energy consumption by improving the efficiency of its sales algorithm. The sales department can minimize its energy consumption by optimizing its sales process. The sales department can use highly energy-efficient hardware to optimize its energy consumption during sales. This enables the sales department to optimize its energy consumption during sales.

[0056] During a trial, the trial unit can select an optimal trial method by referring to past trial data. For example, the trial unit preferentially selects trial methods that have received high ratings in the past. For example, the trial unit analyzes past trial data and selects the most efficient trial method. The trial unit can also refer to past trial failure data and select a trial method that will avoid similar failures. This enables the trial unit to select an optimal trial method based on past trial data. For example, the trial unit can preferentially select trial methods that have received high ratings in the past. The trial unit can analyze past trial data and select the most efficient trial method. The trial unit can refer to past trial failure data and select a trial method that will avoid similar failures. This enables the trial unit to select an optimal trial method based on past trial data.

[0057] During the trial, the trial unit can conduct trials specialized for a specific industry or application. For example, the trial unit can conduct trials of a generative AI that supports diagnostic support and treatment planning for the medical industry. For example, the trial unit can conduct trials of a generative AI that supports learning support and material creation for the education industry. The trial unit can also conduct trials of a generative AI that improves content generation and user experience for the entertainment industry. This enables the trial unit to conduct trials specialized for a specific industry or application. For example, the trial unit can conduct trials of a generative AI that supports diagnostic support and treatment planning for the medical industry. The trial unit can conduct trials of a generative AI that supports learning support and material creation for the education industry. The trial unit can conduct trials of a generative AI that improves content generation and user experience for the entertainment industry. This enables the trial unit to conduct trials specialized for a specific industry or application.

[0058] The trial unit can adjust the trial content by reflecting user feedback during the trial. For example, the trial unit collects feedback from the user and adjusts parameters of the trial content. For example, the trial unit identifies and adjusts areas for improvement in the trial content based on the user's evaluation. The trial unit can also analyze the user's usage history and optimize the trial content. This enables the trial unit to adjust the trial content based on user feedback. For example, the trial unit can collect feedback from the user and adjust parameters of the trial content. For example, the trial unit can identify and adjust areas for improvement in the trial content based on the user's evaluation. The trial unit can analyze the user's usage history and optimize the trial content. This enables the trial unit to adjust the trial content based on user feedback.

[0059] The trial unit can integrate information from different data sources during a trial to perform the trial. The trial unit, for example, integrates information from multiple databases to improve the accuracy of the trial. The trial unit, for example, integrates information from social media and news sites to improve the real-time nature of the trial. The trial unit can also integrate the user's past usage history and feedback to personalize the trial. This enables the trial unit to perform a trial that integrates information from different data sources. For example, the trial unit can integrate information from multiple databases to improve the accuracy of the trial. The trial unit can integrate information from social media and news sites to improve the real-time nature of the trial. The trial unit can integrate the user's past usage history and feedback to personalize the trial. This enables the trial unit to perform a trial that integrates information from different data sources.

[0060] The trial unit can adjust the trial speed during trial to perform the trial efficiently. For example, the trial unit can increase the trial speed in response to a user request and quickly provide the generated AI. For example, the trial unit can adjust the trial speed to efficiently use resources. The trial unit can also adjust the trial speed to efficiently perform the trial while maintaining the quality of the generated AI. This enables the trial unit to adjust the trial speed. For example, the trial unit can increase the trial speed in response to a user request and quickly provide the generated AI. The trial unit can adjust the trial speed to efficiently use resources. The trial unit can adjust the trial speed to efficiently perform the trial while maintaining the quality of the generated AI. This enables the trial unit to adjust the trial speed.

[0061] The trial unit can optimize the energy consumption of the trial during the trial. The trial unit reduces the energy consumption, for example, by improving the efficiency of the trial algorithm. The trial unit minimizes the energy consumption, for example, by optimizing the trial process. The trial unit can also optimize the energy consumption of the trial by using highly energy-efficient hardware. This enables the trial unit to optimize the energy consumption of the trial. For example, the trial unit can reduce the energy consumption by improving the efficiency of the trial algorithm. The trial unit can minimize the energy consumption by optimizing the trial process. The trial unit can use highly energy-efficient hardware. This enables the trial unit to optimize the energy consumption of the trial.

[0062] During management, the management unit can select the optimal management method by referring to past usage data. For example, the management unit prioritizes the selection of management methods that have received high ratings in the past. For example, the management unit analyzes past usage data and selects the most efficient management method. The management unit can also reference data of past management failures to select a management method that will avoid similar failures. This enables the management unit to select the optimal management method based on past usage data. For example, the management unit can prioritize the selection of management methods that have received high ratings in the past. The management unit can analyze past usage data and select the most efficient management method. The management unit can reference data of past management failures to select a management method that will avoid similar failures. This enables the management unit to select the optimal management method based on past usage data.

[0063] During management, the management unit can develop management methods specialized for specific industries or applications. For example, the management unit develops a management method for generative AI that supports diagnostic support and treatment planning for the medical industry. For example, the management unit develops a management method for generative AI that supports learning support and material creation for the education industry. The management unit can also develop a management method for generative AI that improves content generation and user experience for the entertainment industry. This enables the management unit to develop management methods specialized for specific industries or applications. For example, the management unit can develop a management method for generative AI that supports diagnostic support and treatment planning for the medical industry. The management unit can develop a management method for generative AI that supports learning support and material creation for the education industry. The management unit can develop a management method for generative AI that improves content generation and user experience for the entertainment industry. This enables the management unit to develop management methods specialized for specific industries or applications.

[0064] The management unit can adjust the management method by reflecting user feedback during management. For example, the management unit collects feedback from users and adjusts parameters of the management method. For example, the management unit identifies and adjusts points to be improved in the management method based on user evaluations. The management unit can also analyze the user's usage history and optimize the management method. This enables the management unit to adjust the management method based on user feedback. For example, the management unit can collect feedback from users and adjust parameters of the management method. The management unit can identify and adjust points to be improved in the management method based on user evaluations. The management unit can analyze the user's usage history and optimize the management method. This enables the management unit to adjust the management method based on user feedback.

[0065] During management, the management unit can integrate information from different data sources to develop a management method. The management unit, for example, integrates information from multiple databases to improve the accuracy of the management method. The management unit, for example, integrates information from social media and news sites to improve the real-time nature of the management method. The management unit can also integrate users' past usage histories and feedback to personalize the management method. This enables the management unit to develop a management method that integrates information from different data sources. For example, the management unit can integrate information from multiple databases to improve the accuracy of the management method. The management unit can integrate information from social media and news sites to improve the real-time nature of the management method. The management unit can integrate users' past usage histories and feedback to personalize the management method. This enables the management unit to develop a management method that integrates information from different data sources.

[0066] The management unit can adjust the speed of management during management to perform it efficiently. For example, the management unit can speed up the management speed in response to a user request and quickly provide the generated AI. For example, the management unit can adjust the management speed to efficiently use resources. The management unit can also adjust the management speed to perform management efficiently while maintaining the quality of the generated AI. This enables the management unit to adjust the speed of management. For example, the management unit can speed up the management speed in response to a user request and quickly provide the generated AI. The management unit can adjust the management speed to efficiently use resources. The management unit can adjust the management speed to perform management efficiently while maintaining the quality of the generated AI. This enables the management unit to adjust the speed of management.

[0067] The management unit can optimize the energy consumption of the management during management. The management unit reduces energy consumption, for example, by improving the efficiency of the management algorithm. The management unit minimizes energy consumption, for example, by optimizing the management process. The management unit can also optimize the energy consumption of the management by using highly energy-efficient hardware. This makes it possible for the management unit to optimize the energy consumption of the management. For example, the management unit can reduce energy consumption by improving the efficiency of the management algorithm. The management unit can minimize energy consumption by optimizing the management process. The management unit can use highly energy-efficient hardware to optimize the energy consumption of the management. This makes it possible for the management unit to optimize the energy consumption of the management.

[0068] During analysis, the analysis unit can select the optimal analysis method by referring to past analysis data. For example, the analysis unit preferentially selects analysis methods that have received high evaluations in the past. For example, the analysis unit analyzes past analysis data and selects the most efficient analysis method. The analysis unit can also refer to data on past analysis failures and select an analysis method that will avoid similar failures. This enables the analysis unit to select the optimal analysis method based on past analysis data. For example, the analysis unit can preferentially select analysis methods that have received high evaluations in the past. The analysis unit can analyze past analysis data and select the most efficient analysis method. The analysis unit can refer to data on past analysis failures and select an analysis method that will avoid similar failures. This enables the analysis unit to select the optimal analysis method based on past analysis data.

[0069] During analysis, the analysis unit can perform analysis specialized for a specific industry or application. For example, the analysis unit performs analysis to support diagnostic support and treatment planning for the medical industry. For example, the analysis unit performs analysis to support learning support and teaching material creation for the education industry. The analysis unit can also perform analysis to improve content generation and user experience for the entertainment industry. This enables the analysis unit to perform analysis specialized for a specific industry or application. For example, the analysis unit can perform analysis to support diagnostic support and treatment planning for the medical industry. The analysis unit can perform analysis to support learning support and teaching material creation for the education industry. The analysis unit can perform analysis to improve content generation and user experience for the entertainment industry. This enables the analysis unit to perform analysis specialized for a specific industry or application.

[0070] The analysis unit can adjust the analysis content by reflecting user feedback during analysis. The analysis unit, for example, collects feedback from the user and adjusts parameters of the analysis content. The analysis unit, for example, identifies and adjusts areas to be improved in the analysis content based on user evaluations. The analysis unit can also analyze the user's usage history and optimize the analysis content. This enables the analysis unit to adjust the analysis content based on user feedback. For example, the analysis unit can collect feedback from the user and adjust parameters of the analysis content. The analysis unit, for example, identifies and adjust areas to be improved in the analysis content based on user evaluations. The analysis unit, for example, can analyze the user's usage history and optimize the analysis content. This enables the analysis unit to adjust the analysis content based on user feedback.

[0071] The analysis unit can integrate information from different data sources during analysis. The analysis unit, for example, integrates information from multiple databases to improve the accuracy of the analysis. The analysis unit, for example, integrates information from social media and news sites to improve the real-time nature of the analysis. The analysis unit can also integrate the user's past usage history and feedback to personalize the analysis. This enables the analysis unit to integrate information from different data sources. For example, the analysis unit can integrate information from multiple databases to improve the accuracy of the analysis. The analysis unit can integrate information from social media and news sites to improve the real-time nature of the analysis. The analysis unit can integrate the user's past usage history and feedback to personalize the analysis. This enables the analysis unit to integrate information from different data sources.

[0072] The analysis unit can adjust the speed of analysis during analysis to perform the analysis efficiently. For example, the analysis unit can increase the analysis speed in response to a user request and quickly provide a generated AI. For example, the analysis unit can adjust the analysis speed to efficiently use resources. The analysis unit can also adjust the analysis speed to perform the analysis efficiently while maintaining the quality of the generated AI. This enables the analysis unit to adjust the speed of analysis. For example, the analysis unit can increase the analysis speed in response to a user request and quickly provide a generated AI. The analysis unit can adjust the analysis speed to efficiently use resources. The analysis unit can adjust the analysis speed to perform the analysis efficiently while maintaining the quality of the generated AI. This enables the analysis unit to adjust the speed of analysis.

[0073] The analysis unit can optimize the energy consumption of the analysis during analysis. The analysis unit reduces the energy consumption by, for example, improving the efficiency of the analysis algorithm. The analysis unit minimizes the energy consumption by, for example, optimizing the analysis process. The analysis unit can also optimize the energy consumption of the analysis by using highly energy-efficient hardware. This enables the analysis unit to optimize the energy consumption of the analysis. For example, the analysis unit can reduce the energy consumption by improving the efficiency of the analysis algorithm. The analysis unit can minimize the energy consumption by optimizing the analysis process. The analysis unit can use highly energy-efficient hardware to optimize the energy consumption of the analysis. This enables the analysis unit to optimize the energy consumption of the analysis.

[0074] During evaluation, the evaluation unit can select the optimal evaluation method by referring to past evaluation data. For example, the evaluation unit prioritizes selecting evaluation methods that have received high evaluations in the past. For example, the evaluation unit analyzes past evaluation data and selects the most efficient evaluation method. The evaluation unit can also reference data of past evaluation failures to select an evaluation method that will avoid similar failures. This enables the evaluation unit to select the optimal evaluation method based on the past evaluation data. For example, the evaluation unit can prioritize selecting evaluation methods that have received high evaluations in the past. The evaluation unit can analyze past evaluation data and select the most efficient evaluation method. The evaluation unit can reference data of past evaluation failures to select an evaluation method that will avoid similar failures. This enables the evaluation unit to select the optimal evaluation method based on the past evaluation data.

[0075] During evaluation, the evaluation unit can perform evaluation specialized for a specific industry or application. For example, the evaluation unit performs evaluation to support diagnostic support and treatment planning for the medical industry. For example, the evaluation unit performs evaluation to support learning support and teaching material creation for the education industry. The evaluation unit can also perform evaluation to improve content generation and user experience for the entertainment industry. This enables the evaluation unit to perform evaluation specialized for a specific industry or application. For example, the evaluation unit can perform evaluation to support diagnostic support and treatment planning for the medical industry. The evaluation unit can perform evaluation to support learning support and teaching material creation for the education industry. The evaluation unit can perform evaluation to improve content generation and user experience for the entertainment industry. This enables the evaluation unit to perform evaluation specialized for a specific industry or application.

[0076] The evaluation unit can adjust the evaluation content by reflecting user feedback at the time of evaluation. The evaluation unit, for example, collects feedback from users and adjusts parameters of the evaluation content. The evaluation unit, for example, identifies and adjusts points to be improved in the evaluation content based on the user's evaluation. The evaluation unit can also analyze the user's usage history and optimize the evaluation content. This enables the evaluation unit to adjust the evaluation content based on user feedback. For example, the evaluation unit can collect feedback from users and adjust parameters of the evaluation content. The evaluation unit can identify and adjust points to be improved in the evaluation content based on the user's evaluation. The evaluation unit can analyze the user's usage history and optimize the evaluation content. This enables the evaluation unit to adjust the evaluation content based on user feedback.

[0077] The evaluation unit can integrate information from different data sources when evaluating. The evaluation unit, for example, integrates information from multiple databases to improve the accuracy of the evaluation. The evaluation unit, for example, integrates information from social media and news sites to improve the real-time nature of the evaluation. The evaluation unit can also integrate the user's past usage history and feedback to personalize the evaluation. This enables the evaluation unit to integrate information from different data sources. For example, the evaluation unit can integrate information from multiple databases to improve the accuracy of the evaluation. The evaluation unit can integrate information from social media and news sites to improve the real-time nature of the evaluation. The evaluation unit can integrate the user's past usage history and feedback to personalize the evaluation. This enables the evaluation unit to integrate information from different data sources.

[0078] The evaluation unit can adjust the evaluation speed during evaluation to perform the evaluation efficiently. For example, the evaluation unit can increase the evaluation speed in response to a user request and quickly provide a generated AI. For example, the evaluation unit can adjust the evaluation speed to efficiently use resources. The evaluation unit can also adjust the evaluation speed to efficiently perform evaluation while maintaining the quality of the generated AI. This enables the evaluation unit to adjust the evaluation speed. For example, the evaluation unit can increase the evaluation speed in response to a user request and quickly provide a generated AI. The evaluation unit can adjust the evaluation speed to efficiently use resources. The evaluation unit can adjust the evaluation speed to efficiently perform evaluation while maintaining the quality of the generated AI. This enables the evaluation unit to adjust the evaluation speed.

[0079] The evaluation unit can optimize the energy consumption of the evaluation during the evaluation. The evaluation unit reduces the energy consumption by, for example, improving the efficiency of the evaluation algorithm. The evaluation unit can minimize the energy consumption by, for example, optimizing the evaluation process. The evaluation unit can also optimize the energy consumption of the evaluation by using highly energy-efficient hardware. This enables the evaluation unit to optimize the energy consumption of the evaluation. For example, the evaluation unit can reduce the energy consumption by improving the efficiency of the evaluation algorithm. The evaluation unit can minimize the energy consumption by optimizing the evaluation process. The evaluation unit can use highly energy-efficient hardware to optimize the energy consumption of the evaluation. This enables the evaluation unit to optimize the energy consumption of the evaluation.

[0080] During monitoring, the monitoring unit can select the optimal monitoring method by referring to past monitoring data. For example, the monitoring unit preferentially selects monitoring methods that have received high ratings in the past. For example, the monitoring unit analyzes past monitoring data and selects the most efficient monitoring method. The monitoring unit can also refer to data on past monitoring failures and select a monitoring method that will avoid similar failures. This enables the monitoring unit to select the optimal monitoring method based on past monitoring data. For example, the monitoring unit can preferentially select monitoring methods that have received high ratings in the past. The monitoring unit can analyze past monitoring data and select the most efficient monitoring method. The monitoring unit can refer to data on past monitoring failures and select a monitoring method that will avoid similar failures. This enables the monitoring unit to select the optimal monitoring method based on past monitoring data.

[0081] The monitoring unit can perform monitoring specialized for a specific industry or application during monitoring. For example, the monitoring unit performs monitoring to support diagnostic support and treatment planning for the medical industry. For example, the monitoring unit performs monitoring to support learning support and teaching material creation for the education industry. The monitoring unit can also perform monitoring to improve content generation and user experience for the entertainment industry. This enables the monitoring unit to perform monitoring specialized for a specific industry or application. For example, the monitoring unit can perform monitoring to support diagnostic support and treatment planning for the medical industry. The monitoring unit can perform monitoring to support learning support and teaching material creation for the education industry. The monitoring unit can perform monitoring to improve content generation and user experience for the entertainment industry. This enables the monitoring unit to perform monitoring specialized for a specific industry or application.

[0082] The monitoring unit can adjust the monitoring content by reflecting user feedback during monitoring. The monitoring unit, for example, collects feedback from the user and adjusts parameters of the monitoring content. The monitoring unit, for example, identifies and adjusts areas for improvement in the monitoring content based on user evaluations. The monitoring unit can also analyze the user's usage history and optimize the monitoring content. This enables the monitoring unit to adjust the monitoring content based on user feedback. For example, the monitoring unit can collect feedback from the user and adjust parameters of the monitoring content. The monitoring unit can identify and adjust areas for improvement in the monitoring content based on user evaluations. The monitoring unit can analyze the user's usage history and optimize the monitoring content. This enables the monitoring unit to adjust the monitoring content based on user feedback.

[0083] The monitoring unit can integrate information from different data sources during monitoring. The monitoring unit, for example, integrates information from multiple databases to improve the accuracy of monitoring. The monitoring unit, for example, integrates information from social media and news sites to improve the real-time nature of monitoring. The monitoring unit can also integrate the user's past usage history and feedback to realize personalized monitoring. This enables the monitoring unit to perform monitoring that integrates information from different data sources. For example, the monitoring unit can integrate information from multiple databases to improve the accuracy of monitoring. The monitoring unit can integrate information from social media and news sites to improve the real-time nature of monitoring. The monitoring unit can integrate the user's past usage history and feedback to realize personalized monitoring. This enables the monitoring unit to perform monitoring that integrates information from different data sources.

[0084] The monitoring unit can adjust the monitoring speed during monitoring to perform the monitoring efficiently. For example, the monitoring unit can increase the monitoring speed in response to a user request and quickly provide the generated AI. For example, the monitoring unit can adjust the monitoring speed to efficiently use resources. The monitoring unit can also adjust the monitoring speed to perform monitoring efficiently while maintaining the quality of the generated AI. This enables the monitoring unit to adjust the monitoring speed. For example, the monitoring unit can increase the monitoring speed in response to a user request and quickly provide the generated AI. The monitoring unit can adjust the monitoring speed to efficiently use resources. The monitoring unit can adjust the monitoring speed to perform monitoring efficiently while maintaining the quality of the generated AI. This enables the monitoring unit to adjust the monitoring speed.

[0085] The monitoring unit can optimize the energy consumption of the monitoring during monitoring. The monitoring unit reduces the energy consumption by, for example, improving the efficiency of the monitoring algorithm. The monitoring unit can minimize the energy consumption by, for example, optimizing the monitoring process. The monitoring unit can also optimize the energy consumption of the monitoring by using highly energy-efficient hardware. This enables the monitoring unit to optimize the energy consumption of the monitoring. For example, the monitoring unit can reduce the energy consumption by, for example, improving the efficiency of the monitoring algorithm. The monitoring unit can minimize the energy consumption by optimizing the monitoring process. The monitoring unit can use highly energy-efficient hardware to optimize the energy consumption of the monitoring. This enables the monitoring unit to optimize the energy consumption of the monitoring.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The generation AI market system can further include a behavior analysis unit that analyzes user behavior history. The behavior analysis unit analyzes, for example, which generation AIs users frequently use and which functions they use most frequently. This can be used to customize the generation AI or add new functions. The behavior analysis unit can also make suggestions to promote the use of the generation AI based on user behavior patterns. For example, if the generation AI is frequently used during a specific time period, promotions can be tailored to that time period. Furthermore, the behavior analysis unit can share user behavior data with other elements to help improve the performance of the generation AI and the user experience.

[0088] The generative AI market system may further include a feedback collection unit that collects user feedback in real time. For example, the feedback collection unit may display a pop-up window while the user is using the generative AI and conduct a simple survey. The feedback collection unit may also automatically send an email requesting feedback after the user has used the generative AI. This allows for the rapid collection of user opinions and feedback, which can be used to improve the generative AI. Furthermore, the feedback collection unit may share the collected feedback with other elements and reflect it in customizing the generative AI or developing new functions.

[0089] The generation AI market system can further include a recommendation unit that recommends generation AIs based on the user's behavioral history. The recommendation unit, for example, analyzes the history of generation AIs that the user has used in the past and recommends similar generation AIs. The recommendation unit can also propose new generation AIs based on the user's behavioral patterns. Furthermore, the recommendation unit can adjust the recommended content of the generation AI based on user feedback. This enables the recommendation unit to recommend the optimal generation AI based on the user's behavioral history.

[0090] The generation AI market system can further include a performance optimization unit that monitors user usage in real time and optimizes the performance of the generation AI. The performance optimization unit monitors, for example, the frequency of use and error rate of the generation AI and adjusts performance as necessary. The performance optimization unit can also analyze user usage and optimize resource allocation for the generation AI. Furthermore, the performance optimization unit can make adjustments to improve the performance of the generation AI based on user feedback. This enables the performance optimization unit to optimize the performance of the generation AI in real time.

[0091] The generation AI market system can further include a customization recommendation unit that customizes the generation AI based on the user's behavioral history. The customization recommendation unit can, for example, suggest new customization options based on customization options previously selected by the user. The customization recommendation unit can also analyze the user's behavioral patterns and suggest the optimal customization method. Furthermore, the customization recommendation unit can adjust the customization content based on user feedback. This enables the customization recommendation unit to suggest the optimal customization based on the user's behavioral history.

[0092] The generation AI market system can further include a training data optimization unit that optimizes the training data of the generation AI based on the user's behavioral history. The training data optimization unit, for example, analyzes data that the user has used in the past and selects optimal training data. The training data optimization unit can also add new training data based on the user's behavioral patterns. Furthermore, the training data optimization unit can adjust the content of the training data based on user feedback. This enables the training data optimization unit to select optimal training data based on the user's behavioral history.

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

[0094] Step 1: The generator creates a generator AI. The generator creates a generator AI based on a specific theme or request, and can analyze the generator AI's algorithm to select the optimal one. The generator can also adjust the generator AI's generation speed and energy consumption to generate more efficiently. Step 2: The customization unit customizes the generated AI created by the generation unit based on themes and requests. The customization unit adds functions to the generated AI or changes its design. It can also estimate the user's emotions and adjust the customization content based on the estimated emotions. Step 3: The sales department sells the generative AI customized by the customization department on the Internet. The sales department sells the generative AI through online marketplaces or dedicated sales sites. The sales department can also estimate user emotions and adjust sales strategies based on the estimated emotions. Step 4: The trial department allows purchasers to try out the prototype of the generative AI sold by the sales department. The trial department sets the trial period and trial conditions, allowing purchasers to check the performance of the generative AI. The trial department can also estimate the user's emotions and adjust the trial content based on the estimated emotions. Step 5: The management unit manages the usage fees for the generative AI tested by the trial unit. The management unit sets monthly fees and fees based on usage volume and manages the usage fees. It can also estimate the user's emotions and adjust the usage fee management method based on the estimated emotions.

[0095] (Example 2) A generative AI market system according to an embodiment of the present invention is a system for creating, customizing, selling, trialing, and managing usage fees for generative AI. The generative AI market system provides a mechanism whereby engineers create generative AI based on a theme or request, and buyers can try out prototypes and, if they like them, pay a usage fee. For example, in a generative AI market system, engineers create generative AI for a specific industry and sell it online. Buyers can try out the prototype and begin using it only after they are satisfied. Furthermore, the generative AI market system provides generative AIs by theme or request, providing a variety of generative AIs tailored to user needs. This allows the generative AI market system to consistently create, customize, sell, trial, and manage generative AIs. For example, engineers can create generative AIs based on a new theme and add them to the market, thereby providing a variety of generative AIs tailored to user needs. This realizes a new business model that replaces existing e-commerce sites.

[0096] The generating AI market system according to the embodiment includes a generating unit, a customization unit, a sales unit, a trial unit, and a management unit. The generating unit creates a generating AI. For example, the generating unit creates a generating AI based on a specific theme or request. The generating unit can also analyze the generating AI's algorithm and select an optimal generating algorithm. The generating unit can also adjust the generating speed and energy consumption of the generating AI to generate it efficiently. The customization unit customizes the generating AI created by the generating unit based on a theme or request. For example, the customization unit adds functions to the generating AI or changes its design. The customization unit can also estimate a user's emotions and adjust the customization content based on the estimated emotions. The sales unit sells the generating AI customized by the customization unit over the Internet. The sales unit sells the generating AI, for example, through an online marketplace or a dedicated sales site. The sales unit can also estimate a user's emotions and adjust a sales strategy based on the estimated emotions. The trial unit allows a purchaser to try out a prototype of the generating AI sold by the sales unit. The trial unit, for example, sets a trial period and trial conditions, allowing the purchaser to check the performance of the generation AI. The trial unit can also estimate the user's emotions and adjust the trial content based on the estimated emotions. The management unit manages the usage fees for the generation AI tried by the trial unit. The management unit can set, for example, a monthly fee or a fee based on usage volume, and manage the usage fees. The management unit can also estimate the user's emotions and adjust the usage fee management method based on the estimated emotions. As a result, the generation AI market system according to the embodiment can consistently create, customize, sell, try out, and manage the generation AI. For example, the generation unit can analyze the generation AI's algorithm and select the optimal generation algorithm. The customization unit can customize the generation AI based on a theme or request. The sales unit can sell the generation AI over the Internet. The trial unit allows the purchaser to try out a prototype of the generation AI. The management unit can manage the usage fees for the generation AI.This allows the Generative AI Market System to consistently create, customize, sell, try out, and manage generative AI.

[0097] The generative AI market system includes an analysis unit that analyzes the generative AI's algorithm. The analysis unit analyzes the generative AI's algorithm. The analysis unit analyzes the generative AI's algorithm, for example, a neural network or a genetic algorithm. The analysis unit can also evaluate the performance of the generative AI's algorithm and select an optimal algorithm. Furthermore, the analysis unit can identify areas for improvement in the generative AI's algorithm and optimize the algorithm. This enables the generative AI market system to analyze the generative AI's algorithm. For example, the analysis unit can analyze the generative AI's algorithm and select an optimal algorithm. The analysis unit can evaluate the performance of the generative AI's algorithm and optimize the algorithm. This enables the generative AI market system to analyze the generative AI's algorithm.

[0098] The generative AI market system includes an evaluation unit that evaluates the generative AI. The evaluation unit evaluates the generative AI. For example, the evaluation unit performs performance evaluation of the generative AI and user satisfaction evaluation. The evaluation unit can also set evaluation criteria for the generative AI and identify areas for improvement of the generative AI based on the evaluation results. Furthermore, the evaluation unit can also feed back the evaluation results of the generative AI to improve the quality of the generative AI. This enables the generative AI market system to evaluate the generative AI. For example, the evaluation unit can evaluate the performance of the generative AI and identify areas for improvement of the generative AI based on the evaluation results. The evaluation unit can set evaluation criteria for the generative AI and feed back the evaluation results. This enables the generative AI market system to evaluate the generative AI.

[0099] The generative AI market system includes a monitoring unit that monitors the usage status of the generative AI. The monitoring unit monitors the usage status of the generative AI. For example, the monitoring unit monitors the frequency of use and error rate of the generative AI. The monitoring unit can also monitor the usage status of the generative AI in real time and issue an alert if an abnormality is detected. Furthermore, the monitoring unit can analyze the usage status of the generative AI and identify areas for improvement of the generative AI based on the usage status. This enables the generative AI market system to monitor the usage status of the generative AI. For example, the monitoring unit can monitor the frequency of use and error rate of the generative AI and issue an alert if an abnormality is detected. The monitoring unit can analyze the usage status of the generative AI and identify areas for improvement of the generative AI based on the usage status. This enables the generative AI market system to monitor the usage status of the generative AI.

[0100] The generation unit can create a generation AI according to a specific theme or request. For example, the generation unit can create a generation AI according to a theme related to a specific industry or specific requests from a user. For example, the generation unit can create a generation AI that supports diagnostic support and treatment planning for the medical industry. The generation unit can also create a generation AI that supports learning support and teaching material creation for the education industry. Furthermore, the generation unit can create a generation AI that improves content generation and user experience for the entertainment industry. This enables the generation unit to create a generation AI according to a specific theme or request. For example, the generation unit can create a generation AI that supports diagnostic support and treatment planning for the medical industry. The generation unit can create a generation AI that supports learning support and teaching material creation for the education industry. The generation unit can create a generation AI that improves content generation and user experience for the entertainment industry. This enables the generation unit to create a generation AI according to a specific theme or request.

[0101] The customization unit can customize the generation AI based on a theme or request. The customization unit, for example, adds functions to the generation AI or changes its design. The customization unit can, for example, add functions to the generation AI for a specific industry. The customization unit can also change the design of the generation AI based on a user's specific request. The customization unit can also perform customization to improve the performance of the generation AI. This enables the customization unit to customize the generation AI based on a theme or request. For example, the customization unit can add functions to the generation AI for a specific industry. The customization unit can change the design of the generation AI based on a user's specific request. The customization unit can perform customization to improve the performance of the generation AI. This enables the customization unit to customize the generation AI based on a theme or request.

[0102] The sales department can sell the generated AI on the Internet. The sales department can sell the generated AI through, for example, an online marketplace or a dedicated sales site. The sales department can set a sales price for the generated AI and sell it on the Internet. The sales department can also monitor the sales status of the generated AI and adjust the sales strategy. Furthermore, the sales department can estimate user emotions and adjust the sales strategy based on the estimated emotions. This enables the sales department to sell the generated AI on the Internet. For example, the sales department can sell the generated AI through an online marketplace or a dedicated sales site. The sales department can set a sales price for the generated AI and sell it on the Internet. The sales department can monitor the sales status of the generated AI and adjust the sales strategy. This enables the sales department to sell the generated AI on the Internet.

[0103] The trial unit allows a purchaser to try out a prototype of the generative AI. The trial unit, for example, sets a trial period and trial conditions, allowing the purchaser to check the performance of the generative AI. The trial unit, for example, provides a prototype of the generative AI, allowing the purchaser to try it out. The trial unit can also provide feedback on the trial results and identify areas for improvement in the generative AI. Furthermore, the trial unit can estimate a user's emotions and adjust the content of the trial based on the estimated emotions. This allows the trial unit to try out a prototype of the generative AI. For example, the trial unit can set a trial period and trial conditions, allowing the purchaser to check the performance of the generative AI. The trial unit can provide a prototype of the generative AI, allowing the purchaser to try it out. The trial unit can provide feedback on the trial results and identify areas for improvement in the generative AI. This allows the trial unit to try out a prototype of the generative AI.

[0104] The management unit can manage the usage fees for the generation AI. For example, the management unit can set a monthly fee or a fee based on usage volume and manage the usage fees. For example, the management unit can set a payment method for the usage fee and manage the usage fees. The management unit can also monitor the payment status of the usage fee and issue an alert if a payment is not made. Furthermore, the management unit can estimate the user's emotions and adjust the method of managing the usage fee based on the estimated emotions. This enables the management unit to manage the usage fees for the generation AI. For example, the management unit can set a monthly fee or a fee based on usage volume and manage the usage fees. The management unit can set a payment method for the usage fee and manage the usage fees. The management unit can monitor the payment status of the usage fee and issue an alert if a payment is not made. This enables the management unit to manage the usage fees for the generation AI.

[0105] The generation unit can estimate the user's emotions and select a theme for the AI ​​to be generated based on the estimated user emotions. The generation unit estimates emotions using, for example, facial expression recognition or voice analysis of the user. For example, if the user is relaxed, the generation unit selects a theme related to relaxation or entertainment. Furthermore, if the user is stressed, the generation unit can select a theme related to stress reduction or relaxation. Furthermore, if the user is excited, the generation unit can select a theme related to action or adventure. This enables the generation unit to select a theme for the AI ​​based on the user's emotions. For example, the generation unit can estimate the user's emotions using facial expression recognition or voice analysis of the user and select a theme related to relaxation or entertainment. If the user is stressed, the generation unit can select a theme related to stress reduction or relaxation. If the user is excited, the generation unit can select a theme related to action or adventure. This enables the generation unit to select a theme for the AI ​​based on the user's emotions.

[0106] At the time of generation, the generation unit can select the optimal generation algorithm by referring to past performance data of the generation AI. For example, the generation unit preferentially selects an algorithm of a generation AI that has received high evaluations in the past. For example, the generation unit analyzes past performance data of the generation AI and selects the most efficient algorithm. The generation unit can also refer to past failure data of the generation AI and select an algorithm that will avoid similar failures. This enables the generation unit to select the optimal generation algorithm based on past performance data. For example, the generation unit can preferentially select an algorithm of a generation AI that has received high evaluations in the past. The generation unit can analyze past performance data of the generation AI and select the most efficient algorithm. The generation unit can refer to past failure data of the generation AI and select an algorithm that will avoid similar failures. This enables the generation unit to select the optimal generation algorithm based on past performance data.

[0107] The generation unit can create a generation AI specialized for a specific industry or application at the time of generation. For example, the generation unit creates a generation AI that supports diagnostic support and treatment planning for the medical industry. For example, the generation unit creates a generation AI that supports learning support and teaching material creation for the education industry. The generation unit can also create a generation AI that improves content generation and user experience for the entertainment industry. This enables the generation unit to create a generation AI specialized for a specific industry or application. For example, the generation unit can create a generation AI that supports diagnostic support and treatment planning for the medical industry. The generation unit can create a generation AI that supports learning support and teaching material creation for the education industry. The generation unit can create a generation AI that improves content generation and user experience for the entertainment industry. This enables the generation unit to create a generation AI specialized for a specific industry or application.

[0108] The generation unit can adjust the generation algorithm by reflecting user feedback at the time of generation. For example, the generation unit collects feedback from users and adjusts parameters of the generation algorithm. For example, the generation unit identifies and adjusts points to be improved in the generation algorithm based on user evaluations. The generation unit can also analyze the user's usage history and optimize the generation algorithm. This enables the generation unit to adjust the generation algorithm based on user feedback. For example, the generation unit can collect feedback from users and adjust parameters of the generation algorithm. For example, the generation unit can identify and adjust points to be improved in the generation algorithm based on user evaluations. The generation unit can analyze the user's usage history and optimize the generation algorithm. This enables the generation unit to adjust the generation algorithm based on user feedback.

[0109] The generation unit can estimate the user's emotions and adjust the functions of the generated AI based on the estimated user emotions. The generation unit can estimate emotions using, for example, facial expression recognition or voice analysis of the user. For example, when the user is relaxed, the generation unit can simplify the functions of the generation AI to emphasize ease of use. Furthermore, when the user is stressed, the generation unit can minimize the functions of the generation AI to reduce the burden of operation. Furthermore, when the user is excited, the generation unit can diversify the functions of the generation AI to increase entertainment value. This enables the generation unit to adjust the functions of the AI ​​based on the user's emotions. For example, the generation unit can estimate emotions using facial expression recognition or voice analysis of the user and simplify the functions of the generation AI. When the user is stressed, the generation unit can minimize the functions of the generation AI to reduce the burden of operation. When the user is excited, the generation unit can diversify the functions of the generation AI to increase entertainment value. This enables the generation unit to adjust the functions of the AI ​​based on the user's emotions.

[0110] The generation unit can integrate information from different data sources to create the generative AI at the time of generation. The generation unit, for example, integrates information from multiple databases to improve the accuracy of the generative AI. The generation unit, for example, integrates information from social media and news sites to improve the real-time performance of the generative AI. The generation unit can also integrate a user's past usage history and feedback to personalize the generative AI. This enables the generation unit to create a generative AI that integrates information from different data sources. For example, the generation unit can integrate information from multiple databases to improve the accuracy of the generative AI. The generation unit can integrate information from social media and news sites to improve the real-time performance of the generative AI. The generation unit can integrate a user's past usage history and feedback to personalize the generative AI. This enables the generation unit to create a generative AI that integrates information from different data sources.

[0111] The generation unit can adjust the generation speed of the generated AI during generation to efficiently generate the generated AI. For example, the generation unit can increase the generation speed in response to a user request and quickly provide the generated AI. For example, the generation unit can adjust the generation speed to efficiently use resources. The generation unit can also adjust the generation speed to efficiently generate the generated AI while maintaining its quality. This enables the generation unit to adjust the generation speed of the generated AI. For example, the generation unit can increase the generation speed in response to a user request and quickly provide the generated AI. The generation unit can adjust the generation speed to efficiently use resources. The generation unit can adjust the generation speed to efficiently generate the generated AI while maintaining its quality. This enables the generation unit to adjust the generation speed of the generated AI.

[0112] The generation unit can optimize the energy consumption of the generation AI during generation. The generation unit reduces energy consumption, for example, by improving the efficiency of the generation algorithm. The generation unit minimizes energy consumption, for example, by optimizing the generation process. The generation unit can also use highly energy-efficient hardware to optimize the energy consumption of the generation AI. This enables the generation unit to optimize the energy consumption of the generation AI. For example, the generation unit can reduce energy consumption by improving the efficiency of the generation algorithm. The generation unit can minimize energy consumption by optimizing the generation process. The generation unit can use highly energy-efficient hardware to optimize the energy consumption of the generation AI. This enables the generation unit to optimize the energy consumption of the generation AI.

[0113] The customization unit can estimate the user's emotions and adjust the customization content based on the estimated user's emotions. The customization unit estimates the emotions using, for example, facial expression recognition or voice analysis of the user. For example, if the user is relaxed, the customization unit increases the customization options to increase the degree of freedom. Furthermore, if the user is feeling stressed, the customization unit can reduce the customization options and provide simple settings. Furthermore, if the user is excited, the customization unit can diversify the customization options to increase entertainment value. This enables the customization unit to adjust the customization content based on the user's emotions. For example, the customization unit can estimate the emotions using facial expression recognition or voice analysis of the user and increase the customization options. If the user is feeling stressed, the customization unit can reduce the customization options and provide simple settings. If the user is excited, the customization unit can diversify the customization options to increase entertainment value. This enables the customization unit to adjust the customization content based on the user's emotions.

[0114] During customization, the customization unit can select an optimal customization method by referring to the user's past customization history. The customization unit, for example, analyzes the user's past customization history and suggests an optimal customization method. The customization unit, for example, preferentially displays customization options previously selected by the user. The customization unit can also analyze preferences from the user's past customization history and select an optimal customization method. This enables the customization unit to select an optimal customization method based on the user's past customization history. For example, the customization unit can analyze the user's past customization history and suggest an optimal customization method. The customization unit can preferentially display customization options previously selected by the user. The customization unit can analyze preferences from the user's past customization history and select an optimal customization method. This enables the customization unit to select an optimal customization method based on the user's past customization history.

[0115] During customization, the customization unit can perform customization specialized for a specific industry or application. For example, the customization unit performs customization to support diagnostic support and treatment planning for the medical industry. For example, the customization unit performs customization to support learning support and teaching material creation for the education industry. The customization unit can also perform customization to improve content generation and user experience for the entertainment industry. This enables the customization unit to perform customization specialized for a specific industry or application. For example, the customization unit can perform customization to support diagnostic support and treatment planning for the medical industry. The customization unit can perform customization to support learning support and teaching material creation for the education industry. The customization unit can perform customization to improve content generation and user experience for the entertainment industry. This enables the customization unit to perform customization specialized for a specific industry or application.

[0116] The customization unit can adjust the customization content by reflecting user feedback during customization. For example, the customization unit collects feedback from the user and adjusts parameters of the customization content. For example, the customization unit identifies and adjusts points to be improved in the customization content based on user evaluations. The customization unit can also analyze the user's usage history and optimize the customization content. This enables the customization unit to adjust the customization content based on user feedback. For example, the customization unit can collect feedback from the user and adjust parameters of the customization content. For example, the customization unit can identify and adjust points to be improved in the customization content based on user evaluations. The customization unit can analyze the user's usage history and optimize the customization content. This enables the customization unit to adjust the customization content based on user feedback.

[0117] The customization unit can estimate a user's emotion and determine a customization priority based on the estimated user's emotion. The customization unit estimates the emotion using, for example, facial expression recognition or voice analysis of the user. The customization unit allows the user to freely set the customization priority when, for example, the user is relaxed. The customization unit can also preferentially display important customization items when the user is stressed. The customization unit can also preferentially display highly entertaining customization items when the user is excited. This enables the customization unit to freely set the customization priority based on the user's emotion. For example, the customization unit can estimate a user's emotion using facial expression recognition or voice analysis of the user and allow the user to freely set the customization priority. The customization unit can preferentially display important customization items when the user is stressed. The customization unit can preferentially display highly entertaining customization items when the user is excited. This enables the customization unit to freely set the customization priority based on the user's emotion.

[0118] The customization unit can integrate information from different data sources during customization. The customization unit, for example, integrates information from multiple databases to improve the accuracy of customization. The customization unit, for example, integrates information from social media and news sites to improve the real-time nature of customization. The customization unit can also integrate the user's past usage history and feedback to realize personalized customization. This enables the customization unit to perform customization by integrating information from different data sources. For example, the customization unit can integrate information from multiple databases to improve the accuracy of customization. The customization unit can integrate information from social media and news sites to improve the real-time nature of customization. The customization unit can integrate the user's past usage history and feedback to realize personalized customization. This enables the customization unit to perform customization by integrating information from different data sources.

[0119] The customization unit can adjust the speed of customization during customization to perform customization efficiently. For example, the customization unit can increase the speed of customization in response to a user request and provide customization quickly. For example, the customization unit can adjust the speed of customization to efficiently utilize resources. The customization unit can also adjust the speed of customization to perform customization efficiently while maintaining the quality of customization. This enables the customization unit to adjust the speed of customization. For example, the customization unit can increase the speed of customization in response to a user request and provide customization quickly. The customization unit can adjust the speed of customization to efficiently utilize resources. The customization unit can adjust the speed of customization to perform customization efficiently while maintaining the quality of customization. This enables the customization unit to adjust the speed of customization.

[0120] The customization unit can optimize the energy consumption of the customization during customization. The customization unit reduces the energy consumption by, for example, improving the efficiency of the customization algorithm. The customization unit can minimize the energy consumption by, for example, optimizing the customization process. The customization unit can also optimize the energy consumption of the customization by using highly energy-efficient hardware. This enables the customization unit to optimize the energy consumption of the customization. For example, the customization unit can reduce the energy consumption by improving the efficiency of the customization algorithm. The customization unit can minimize the energy consumption by optimizing the customization process. The customization unit can use highly energy-efficient hardware. This enables the customization unit to optimize the energy consumption of the customization.

[0121] The sales department can estimate the user's emotions and adjust a sales strategy based on the estimated user's emotions. The sales department estimates the user's emotions, for example, using facial expression recognition or voice analysis. For example, if the user is relaxed, the sales department can prioritize selling generative AIs related to relaxation and entertainment. Furthermore, if the user is stressed, the sales department can prioritize selling generative AIs related to stress reduction and relaxation. Furthermore, if the user is excited, the sales department can prioritize selling generative AIs related to action and adventure. This enables the sales department to adjust a sales strategy based on the user's emotions. For example, the sales department can estimate the user's emotions using facial expression recognition or voice analysis and prioritize selling generative AIs related to relaxation and entertainment. If the user is stressed, the sales department can prioritize selling generative AIs related to stress reduction and relaxation. If the user is excited, the sales department can prioritize selling generative AIs related to action and adventure. This enables the sales department to adjust a sales strategy based on the user's emotions.

[0122] At the time of sale, the sales department can select the optimal sales method by referring to past sales data. For example, the sales department can prioritize selecting sales methods that have received high evaluations in the past. For example, the sales department can analyze past sales data and select the most efficient sales method. The sales department can also reference data on past sales failures to select a sales method that will avoid similar failures. This enables the sales department to select the optimal sales method based on past sales data. For example, the sales department can prioritize selecting sales methods that have received high evaluations in the past. The sales department can analyze past sales data and select the most efficient sales method. The sales department can reference data on past sales failures to select a sales method that will avoid similar failures. This enables the sales department to select the optimal sales method based on past sales data.

[0123] The sales department can develop sales strategies specialized for specific industries and applications when selling. For example, the sales department sells a generative AI that supports diagnostic support and treatment planning to the medical industry. For example, the sales department sells a generative AI that supports learning support and material creation to the education industry. The sales department can also sell a generative AI that improves content generation and user experience to the entertainment industry. This enables the sales department to develop sales strategies specialized for specific industries and applications. For example, the sales department can sell a generative AI that supports diagnostic support and treatment planning to the medical industry. The sales department can sell a generative AI that supports learning support and material creation to the education industry. The sales department can sell a generative AI that improves content generation and user experience to the entertainment industry. This enables the sales department to develop sales strategies specialized for specific industries and applications.

[0124] The sales department can adjust the sales method by reflecting user feedback at the time of sale. For example, the sales department collects feedback from users and adjusts the parameters of the sales method. For example, the sales department identifies areas for improvement in the sales method based on user evaluations and makes adjustments. The sales department can also analyze the user's usage history and optimize the sales method. This enables the sales department to adjust the sales method based on user feedback. For example, the sales department can collect feedback from users and adjust the parameters of the sales method. For example, the sales department can identify areas for improvement in the sales method based on user evaluations and make adjustments. The sales department can analyze the user's usage history and optimize the sales method. This enables the sales department to adjust the sales method based on user feedback.

[0125] The sales department can estimate the user's emotions and determine sales priorities based on the estimated user's emotions. The sales department can estimate the user's emotions using, for example, facial expression recognition or voice analysis. For example, if the user is relaxed, the sales department can prioritize selling generation AIs related to relaxation and entertainment. Furthermore, if the user is stressed, the sales department can prioritize selling generation AIs related to stress reduction and relaxation. Furthermore, if the user is excited, the sales department can prioritize selling generation AIs related to action and adventure. This enables the sales department to determine sales priorities based on the user's emotions. For example, the sales department can estimate the user's emotions using facial expression recognition or voice analysis and prioritize selling generation AIs related to relaxation and entertainment. If the user is stressed, the sales department can prioritize selling generation AIs related to stress reduction and relaxation. If the user is excited, the sales department can prioritize selling generation AIs related to action and adventure. This enables the sales department to determine sales priorities based on the user's emotions.

[0126] The sales department can integrate information from different data sources at the time of sale to formulate a sales strategy. The sales department, for example, integrates information from multiple databases to improve the accuracy of the sales strategy. The sales department, for example, integrates information from social media and news sites to improve the real-time nature of the sales strategy. The sales department can also integrate users' past usage histories and feedback to personalize the sales strategy. This enables the sales department to formulate a sales strategy that integrates information from different data sources. For example, the sales department can integrate information from multiple databases to improve the accuracy of the sales strategy. The sales department can integrate information from social media and news sites to improve the real-time nature of the sales strategy. The sales department can integrate users' past usage histories and feedback to personalize the sales strategy. This enables the sales department to formulate a sales strategy that integrates information from different data sources.

[0127] The sales department can adjust the sales speed at the time of sale to perform the sale efficiently. For example, the sales department can increase the sales speed and quickly provide the generated AI in response to a user request. For example, the sales department can adjust the sales speed to efficiently use resources. The sales department can also adjust the sales speed to efficiently sell while maintaining the quality of the generated AI. This allows the sales department to adjust the sales speed. For example, the sales department can increase the sales speed and quickly provide the generated AI in response to a user request. The sales department can adjust the sales speed to efficiently use resources. The sales department can adjust the sales speed to efficiently sell while maintaining the quality of the generated AI. This allows the sales department to adjust the sales speed.

[0128] The sales department can optimize its energy consumption during sales. For example, the sales department reduces its energy consumption by improving the efficiency of its sales algorithm. For example, the sales department minimizes its energy consumption by optimizing its sales process. Furthermore, the sales department can also use highly energy-efficient hardware to optimize its energy consumption during sales. This enables the sales department to optimize its energy consumption during sales. For example, the sales department can reduce its energy consumption by improving the efficiency of its sales algorithm. The sales department can minimize its energy consumption by optimizing its sales process. The sales department can use highly energy-efficient hardware to optimize its energy consumption during sales. This enables the sales department to optimize its energy consumption during sales.

[0129] The trial unit can estimate the user's emotions and adjust the trial content based on the estimated user's emotions. The trial unit can estimate the user's emotions using, for example, facial expression recognition or voice analysis. For example, if the user is relaxed, the trial unit can increase the trial options to increase the degree of freedom. Furthermore, if the user is stressed, the trial unit can reduce the trial options and provide simple settings. Furthermore, if the user is excited, the trial unit can diversify the trial options to increase the entertainment value. This enables the trial unit to adjust the trial content based on the user's emotions. For example, the trial unit can estimate the user's emotions using facial expression recognition or voice analysis and increase the trial options. If the user is stressed, the trial unit can reduce the trial options and provide simple settings. If the user is excited, the trial unit can diversify the trial options to increase the entertainment value. This enables the trial unit to adjust the trial content based on the user's emotions.

[0130] During a trial, the trial unit can select an optimal trial method by referring to past trial data. For example, the trial unit preferentially selects trial methods that have received high ratings in the past. For example, the trial unit analyzes past trial data and selects the most efficient trial method. The trial unit can also refer to past trial failure data and select a trial method that will avoid similar failures. This enables the trial unit to select an optimal trial method based on past trial data. For example, the trial unit can preferentially select trial methods that have received high ratings in the past. The trial unit can analyze past trial data and select the most efficient trial method. The trial unit can refer to past trial failure data and select a trial method that will avoid similar failures. This enables the trial unit to select an optimal trial method based on past trial data.

[0131] During the trial, the trial unit can conduct trials specialized for a specific industry or application. For example, the trial unit can conduct trials of a generative AI that supports diagnostic support and treatment planning for the medical industry. For example, the trial unit can conduct trials of a generative AI that supports learning support and material creation for the education industry. The trial unit can also conduct trials of a generative AI that improves content generation and user experience for the entertainment industry. This enables the trial unit to conduct trials specialized for a specific industry or application. For example, the trial unit can conduct trials of a generative AI that supports diagnostic support and treatment planning for the medical industry. The trial unit can conduct trials of a generative AI that supports learning support and material creation for the education industry. The trial unit can conduct trials of a generative AI that improves content generation and user experience for the entertainment industry. This enables the trial unit to conduct trials specialized for a specific industry or application.

[0132] The trial unit can adjust the trial content by reflecting user feedback during the trial. For example, the trial unit collects feedback from the user and adjusts parameters of the trial content. For example, the trial unit identifies and adjusts areas for improvement in the trial content based on the user's evaluation. The trial unit can also analyze the user's usage history and optimize the trial content. This enables the trial unit to adjust the trial content based on user feedback. For example, the trial unit can collect feedback from the user and adjust parameters of the trial content. For example, the trial unit can identify and adjust areas for improvement in the trial content based on the user's evaluation. The trial unit can analyze the user's usage history and optimize the trial content. This enables the trial unit to adjust the trial content based on user feedback.

[0133] The trial unit can estimate the user's emotions and determine the priority of trials based on the estimated user's emotions. The trial unit can estimate the emotions using, for example, facial expression recognition or voice analysis of the user. The trial unit can allow the user to freely set the priority of trials, for example, when the user is relaxed. Furthermore, the trial unit can prioritize displaying important trial items when the user is stressed. Furthermore, the trial unit can prioritize displaying highly entertaining trial items when the user is excited. This allows the trial unit to determine the priority of trials based on the user's emotions. For example, the trial unit can estimate the user's emotions using facial expression recognition or voice analysis of the user and allow the user to freely set the priority of trials. When the user is stressed, the trial unit can prioritize displaying important trial items. When the user is excited, the trial unit can prioritize displaying highly entertaining trial items. This allows the trial unit to determine the priority of trials based on the user's emotions.

[0134] The trial unit can integrate information from different data sources during a trial to perform the trial. The trial unit, for example, integrates information from multiple databases to improve the accuracy of the trial. The trial unit, for example, integrates information from social media and news sites to improve the real-time nature of the trial. The trial unit can also integrate the user's past usage history and feedback to personalize the trial. This enables the trial unit to perform a trial that integrates information from different data sources. For example, the trial unit can integrate information from multiple databases to improve the accuracy of the trial. The trial unit can integrate information from social media and news sites to improve the real-time nature of the trial. The trial unit can integrate the user's past usage history and feedback to personalize the trial. This enables the trial unit to perform a trial that integrates information from different data sources.

[0135] The trial unit can adjust the trial speed during trial to perform the trial efficiently. For example, the trial unit can increase the trial speed in response to a user request and quickly provide the generated AI. For example, the trial unit can adjust the trial speed to efficiently use resources. The trial unit can also adjust the trial speed to efficiently perform the trial while maintaining the quality of the generated AI. This enables the trial unit to adjust the trial speed. For example, the trial unit can increase the trial speed in response to a user request and quickly provide the generated AI. The trial unit can adjust the trial speed to efficiently use resources. The trial unit can adjust the trial speed to efficiently perform the trial while maintaining the quality of the generated AI. This enables the trial unit to adjust the trial speed.

[0136] The trial unit can optimize the energy consumption of the trial during the trial. The trial unit reduces the energy consumption, for example, by improving the efficiency of the trial algorithm. The trial unit minimizes the energy consumption, for example, by optimizing the trial process. The trial unit can also optimize the energy consumption of the trial by using highly energy-efficient hardware. This enables the trial unit to optimize the energy consumption of the trial. For example, the trial unit can reduce the energy consumption by improving the efficiency of the trial algorithm. The trial unit can minimize the energy consumption by optimizing the trial process. The trial unit can use highly energy-efficient hardware. This enables the trial unit to optimize the energy consumption of the trial.

[0137] The management unit can estimate the user's emotions and adjust the usage fee management method based on the estimated user's emotions. The management unit estimates the user's emotions using, for example, facial expression recognition or voice analysis. The management unit can flexibly set the usage fee payment method when the user is relaxed. Furthermore, the management unit can simplify the usage fee payment method when the user is stressed, thereby reducing the burden on the user. Furthermore, the management unit can diversify the usage fee payment methods when the user is excited, thereby increasing the entertainment value. This enables the management unit to adjust the usage fee management method based on the user's emotions. For example, the management unit can estimate the user's emotions using facial expression recognition or voice analysis and enable flexibly set the usage fee payment method. When the user is stressed, the management unit can simplify the usage fee payment method when the user is stressed, thereby reducing the burden on the user. When the user is excited, the management unit can diversify the usage fee payment methods when the user is excited, thereby increasing the entertainment value. This enables the management unit to adjust the usage fee management method based on the user's emotions.

[0138] During management, the management unit can select the optimal management method by referring to past usage data. For example, the management unit prioritizes the selection of management methods that have received high ratings in the past. For example, the management unit analyzes past usage data and selects the most efficient management method. The management unit can also reference data of past management failures to select a management method that will avoid similar failures. This enables the management unit to select the optimal management method based on past usage data. For example, the management unit can prioritize the selection of management methods that have received high ratings in the past. The management unit can analyze past usage data and select the most efficient management method. The management unit can reference data of past management failures to select a management method that will avoid similar failures. This enables the management unit to select the optimal management method based on past usage data.

[0139] During management, the management unit can develop management methods specialized for specific industries or applications. For example, the management unit develops a management method for generative AI that supports diagnostic support and treatment planning for the medical industry. For example, the management unit develops a management method for generative AI that supports learning support and material creation for the education industry. The management unit can also develop a management method for generative AI that improves content generation and user experience for the entertainment industry. This enables the management unit to develop management methods specialized for specific industries or applications. For example, the management unit can develop a management method for generative AI that supports diagnostic support and treatment planning for the medical industry. The management unit can develop a management method for generative AI that supports learning support and material creation for the education industry. The management unit can develop a management method for generative AI that improves content generation and user experience for the entertainment industry. This enables the management unit to develop management methods specialized for specific industries or applications.

[0140] The management unit can adjust the management method by reflecting user feedback during management. For example, the management unit collects feedback from users and adjusts parameters of the management method. For example, the management unit identifies and adjusts points to be improved in the management method based on user evaluations. The management unit can also analyze the user's usage history and optimize the management method. This enables the management unit to adjust the management method based on user feedback. For example, the management unit can collect feedback from users and adjust parameters of the management method. The management unit can identify and adjust points to be improved in the management method based on user evaluations. The management unit can analyze the user's usage history and optimize the management method. This enables the management unit to adjust the management method based on user feedback.

[0141] The management unit can estimate the user's emotions and determine the priority of usage fees based on the estimated user emotions. The management unit estimates the emotions using, for example, facial expression recognition or voice analysis of the user. The management unit allows the user to freely set the priority of usage fees, for example, when the user is relaxed. The management unit can also preferentially display important usage fee items when the user is stressed. Furthermore, the management unit can also preferentially display usage fee items that are highly entertaining when the user is excited. This enables the management unit to freely determine the priority of usage fees based on the user's emotions. For example, the management unit can estimate the user's emotions using facial expression recognition or voice analysis of the user and allow the user to freely set the priority of usage fees. The management unit can preferentially display important usage fee items when the user is stressed. The management unit can preferentially display usage fee items when the user is excited. This enables the management unit to freely determine the priority of usage fees based on the user's emotions.

[0142] During management, the management unit can integrate information from different data sources to develop a management method. The management unit, for example, integrates information from multiple databases to improve the accuracy of the management method. The management unit, for example, integrates information from social media and news sites to improve the real-time nature of the management method. The management unit can also integrate users' past usage histories and feedback to personalize the management method. This enables the management unit to develop a management method that integrates information from different data sources. For example, the management unit can integrate information from multiple databases to improve the accuracy of the management method. The management unit can integrate information from social media and news sites to improve the real-time nature of the management method. The management unit can integrate users' past usage histories and feedback to personalize the management method. This enables the management unit to develop a management method that integrates information from different data sources.

[0143] The management unit can adjust the speed of management during management to perform it efficiently. For example, the management unit can speed up the management speed in response to a user request and quickly provide the generated AI. For example, the management unit can adjust the management speed to efficiently use resources. The management unit can also adjust the management speed to perform management efficiently while maintaining the quality of the generated AI. This enables the management unit to adjust the speed of management. For example, the management unit can speed up the management speed in response to a user request and quickly provide the generated AI. The management unit can adjust the management speed to efficiently use resources. The management unit can adjust the management speed to perform management efficiently while maintaining the quality of the generated AI. This enables the management unit to adjust the speed of management.

[0144] The management unit can optimize the energy consumption of the management during management. The management unit reduces energy consumption, for example, by improving the efficiency of the management algorithm. The management unit minimizes energy consumption, for example, by optimizing the management process. The management unit can also optimize the energy consumption of the management by using highly energy-efficient hardware. This makes it possible for the management unit to optimize the energy consumption of the management. For example, the management unit can reduce energy consumption by improving the efficiency of the management algorithm. The management unit can minimize energy consumption by optimizing the management process. The management unit can use highly energy-efficient hardware to optimize the energy consumption of the management. This makes it possible for the management unit to optimize the energy consumption of the management.

[0145] The analysis unit can estimate the user's emotions and adjust the analysis content based on the estimated user's emotions. The analysis unit estimates the emotions using, for example, facial expression recognition or voice analysis of the user. For example, if the user is relaxed, the analysis unit increases the analysis options to increase flexibility. Furthermore, if the user is feeling stressed, the analysis unit can reduce the analysis options and provide simple settings. Furthermore, if the user is excited, the analysis unit can diversify the analysis options to increase entertainment value. This enables the analysis unit to adjust the analysis content based on the user's emotions. For example, the analysis unit can estimate the emotions using facial expression recognition or voice analysis of the user and increase the analysis options. If the user is feeling stressed, the analysis unit can reduce the analysis options and provide simple settings. If the user is excited, the analysis unit can diversify the analysis options to increase entertainment value. This enables the analysis unit to adjust the analysis content based on the user's emotions.

[0146] During analysis, the analysis unit can select the optimal analysis method by referring to past analysis data. For example, the analysis unit preferentially selects analysis methods that have received high evaluations in the past. For example, the analysis unit analyzes past analysis data and selects the most efficient analysis method. The analysis unit can also refer to data on past analysis failures and select an analysis method that will avoid similar failures. This enables the analysis unit to select the optimal analysis method based on past analysis data. For example, the analysis unit can preferentially select analysis methods that have received high evaluations in the past. The analysis unit can analyze past analysis data and select the most efficient analysis method. The analysis unit can refer to data on past analysis failures and select an analysis method that will avoid similar failures. This enables the analysis unit to select the optimal analysis method based on past analysis data.

[0147] During analysis, the analysis unit can perform analysis specialized for a specific industry or application. For example, the analysis unit performs analysis to support diagnostic support and treatment planning for the medical industry. For example, the analysis unit performs analysis to support learning support and teaching material creation for the education industry. The analysis unit can also perform analysis to improve content generation and user experience for the entertainment industry. This enables the analysis unit to perform analysis specialized for a specific industry or application. For example, the analysis unit can perform analysis to support diagnostic support and treatment planning for the medical industry. The analysis unit can perform analysis to support learning support and teaching material creation for the education industry. The analysis unit can perform analysis to improve content generation and user experience for the entertainment industry. This enables the analysis unit to perform analysis specialized for a specific industry or application.

[0148] The analysis unit can adjust the analysis content by reflecting user feedback during analysis. The analysis unit, for example, collects feedback from the user and adjusts parameters of the analysis content. The analysis unit, for example, identifies and adjusts areas to be improved in the analysis content based on user evaluations. The analysis unit can also analyze the user's usage history and optimize the analysis content. This enables the analysis unit to adjust the analysis content based on user feedback. For example, the analysis unit can collect feedback from the user and adjust parameters of the analysis content. The analysis unit, for example, identifies and adjust areas to be improved in the analysis content based on user evaluations. The analysis unit, for example, can analyze the user's usage history and optimize the analysis content. This enables the analysis unit to adjust the analysis content based on user feedback.

[0149] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user's emotions. The analysis unit, for example, estimates the emotions using facial expression recognition or voice analysis of the user. The analysis unit allows the user to freely set the analysis priorities, for example, when the user is relaxed. The analysis unit can also prioritize displaying important analysis items when the user is stressed. Furthermore, the analysis unit can also prioritize displaying analysis items that are highly entertaining when the user is excited. This allows the analysis unit to freely determine the analysis priorities based on the user's emotions. For example, the analysis unit can estimate the user's emotions using facial expression recognition or voice analysis of the user and allow the user to freely set the analysis priorities. The analysis unit can prioritize displaying important analysis items when the user is stressed. The analysis unit can prioritize displaying analysis items when the user is excited. This allows the analysis unit to freely determine the analysis priorities based on the user's emotions.

[0150] The analysis unit can integrate information from different data sources during analysis. The analysis unit, for example, integrates information from multiple databases to improve the accuracy of the analysis. The analysis unit, for example, integrates information from social media and news sites to improve the real-time nature of the analysis. The analysis unit can also integrate the user's past usage history and feedback to personalize the analysis. This enables the analysis unit to integrate information from different data sources. For example, the analysis unit can integrate information from multiple databases to improve the accuracy of the analysis. The analysis unit can integrate information from social media and news sites to improve the real-time nature of the analysis. The analysis unit can integrate the user's past usage history and feedback to personalize the analysis. This enables the analysis unit to integrate information from different data sources.

[0151] The analysis unit can adjust the speed of analysis during analysis to perform the analysis efficiently. For example, the analysis unit can increase the analysis speed in response to a user request and quickly provide a generated AI. For example, the analysis unit can adjust the analysis speed to efficiently use resources. The analysis unit can also adjust the analysis speed to perform the analysis efficiently while maintaining the quality of the generated AI. This enables the analysis unit to adjust the speed of analysis. For example, the analysis unit can increase the analysis speed in response to a user request and quickly provide a generated AI. The analysis unit can adjust the analysis speed to efficiently use resources. The analysis unit can adjust the analysis speed to perform the analysis efficiently while maintaining the quality of the generated AI. This enables the analysis unit to adjust the speed of analysis.

[0152] The analysis unit can optimize the energy consumption of the analysis during analysis. The analysis unit reduces the energy consumption by, for example, improving the efficiency of the analysis algorithm. The analysis unit minimizes the energy consumption by, for example, optimizing the analysis process. The analysis unit can also optimize the energy consumption of the analysis by using highly energy-efficient hardware. This enables the analysis unit to optimize the energy consumption of the analysis. For example, the analysis unit can reduce the energy consumption by improving the efficiency of the analysis algorithm. The analysis unit can minimize the energy consumption by optimizing the analysis process. The analysis unit can use highly energy-efficient hardware to optimize the energy consumption of the analysis. This enables the analysis unit to optimize the energy consumption of the analysis.

[0153] The evaluation unit can estimate the user's emotions and adjust the content of the evaluation based on the estimated user's emotions. The evaluation unit estimates the emotions using, for example, facial expression recognition or voice analysis of the user. For example, if the user is relaxed, the evaluation unit increases the number of evaluation options to increase the degree of freedom. Furthermore, if the user is feeling stressed, the evaluation unit can reduce the number of evaluation options and provide simple settings. Furthermore, if the user is excited, the evaluation unit can diversify the evaluation options to increase the entertainment value. This enables the evaluation unit to adjust the content of the evaluation based on the user's emotions. For example, the evaluation unit can estimate the emotions using facial expression recognition or voice analysis of the user and increase the number of evaluation options. If the user is feeling stressed, the evaluation unit can reduce the number of evaluation options and provide simple settings. If the user is excited, the evaluation unit can diversify the evaluation options to increase the entertainment value. This enables the evaluation unit to adjust the content of the evaluation based on the user's emotions.

[0154] During evaluation, the evaluation unit can select the optimal evaluation method by referring to past evaluation data. For example, the evaluation unit prioritizes selecting evaluation methods that have received high evaluations in the past. For example, the evaluation unit analyzes past evaluation data and selects the most efficient evaluation method. The evaluation unit can also reference data of past evaluation failures to select an evaluation method that will avoid similar failures. This enables the evaluation unit to select the optimal evaluation method based on the past evaluation data. For example, the evaluation unit can prioritize selecting evaluation methods that have received high evaluations in the past. The evaluation unit can analyze past evaluation data and select the most efficient evaluation method. The evaluation unit can reference data of past evaluation failures to select an evaluation method that will avoid similar failures. This enables the evaluation unit to select the optimal evaluation method based on the past evaluation data.

[0155] During evaluation, the evaluation unit can perform evaluation specialized for a specific industry or application. For example, the evaluation unit performs evaluation to support diagnostic support and treatment planning for the medical industry. For example, the evaluation unit performs evaluation to support learning support and teaching material creation for the education industry. The evaluation unit can also perform evaluation to improve content generation and user experience for the entertainment industry. This enables the evaluation unit to perform evaluation specialized for a specific industry or application. For example, the evaluation unit can perform evaluation to support diagnostic support and treatment planning for the medical industry. The evaluation unit can perform evaluation to support learning support and teaching material creation for the education industry. The evaluation unit can perform evaluation to improve content generation and user experience for the entertainment industry. This enables the evaluation unit to perform evaluation specialized for a specific industry or application.

[0156] The evaluation unit can adjust the evaluation content by reflecting user feedback at the time of evaluation. The evaluation unit, for example, collects feedback from users and adjusts parameters of the evaluation content. The evaluation unit, for example, identifies and adjusts points to be improved in the evaluation content based on the user's evaluation. The evaluation unit can also analyze the user's usage history and optimize the evaluation content. This enables the evaluation unit to adjust the evaluation content based on user feedback. For example, the evaluation unit can collect feedback from users and adjust parameters of the evaluation content. The evaluation unit can identify and adjust points to be improved in the evaluation content based on the user's evaluation. The evaluation unit can analyze the user's usage history and optimize the evaluation content. This enables the evaluation unit to adjust the evaluation content based on user feedback.

[0157] The evaluation unit can estimate the user's emotions and determine the priority of the evaluations based on the estimated user's emotions. The evaluation unit estimates the emotions using, for example, facial expression recognition or voice analysis of the user. The evaluation unit allows the user to freely set the priority of the evaluations, for example, when the user is relaxed. Furthermore, the evaluation unit can preferentially display important evaluation items when the user is stressed. Furthermore, the evaluation unit can preferentially display evaluation items with a high level of entertainment value when the user is excited. This enables the evaluation unit to determine the priority of the evaluations based on the user's emotions. For example, the evaluation unit can estimate the emotions using facial expression recognition or voice analysis of the user and allow the user to freely set the priority of the evaluations. When the user is stressed, the evaluation unit can preferentially display important evaluation items. When the user is excited, the evaluation unit can preferentially display evaluation items with a high level of entertainment value. This enables the evaluation unit to determine the priority of the evaluations based on the user's emotions.

[0158] The evaluation unit can integrate information from different data sources when evaluating. The evaluation unit, for example, integrates information from multiple databases to improve the accuracy of the evaluation. The evaluation unit, for example, integrates information from social media and news sites to improve the real-time nature of the evaluation. The evaluation unit can also integrate the user's past usage history and feedback to personalize the evaluation. This enables the evaluation unit to integrate information from different data sources. For example, the evaluation unit can integrate information from multiple databases to improve the accuracy of the evaluation. The evaluation unit can integrate information from social media and news sites to improve the real-time nature of the evaluation. The evaluation unit can integrate the user's past usage history and feedback to personalize the evaluation. This enables the evaluation unit to integrate information from different data sources.

[0159] The evaluation unit can adjust the evaluation speed during evaluation to perform the evaluation efficiently. For example, the evaluation unit can increase the evaluation speed in response to a user request and quickly provide a generated AI. For example, the evaluation unit can adjust the evaluation speed to efficiently use resources. The evaluation unit can also adjust the evaluation speed to efficiently perform evaluation while maintaining the quality of the generated AI. This enables the evaluation unit to adjust the evaluation speed. For example, the evaluation unit can increase the evaluation speed in response to a user request and quickly provide a generated AI. The evaluation unit can adjust the evaluation speed to efficiently use resources. The evaluation unit can adjust the evaluation speed to efficiently perform evaluation while maintaining the quality of the generated AI. This enables the evaluation unit to adjust the evaluation speed.

[0160] The evaluation unit can optimize the energy consumption of the evaluation during the evaluation. The evaluation unit reduces the energy consumption by, for example, improving the efficiency of the evaluation algorithm. The evaluation unit can minimize the energy consumption by, for example, optimizing the evaluation process. The evaluation unit can also optimize the energy consumption of the evaluation by using highly energy-efficient hardware. This enables the evaluation unit to optimize the energy consumption of the evaluation. For example, the evaluation unit can reduce the energy consumption by improving the efficiency of the evaluation algorithm. The evaluation unit can minimize the energy consumption by optimizing the evaluation process. The evaluation unit can use highly energy-efficient hardware to optimize the energy consumption of the evaluation. This enables the evaluation unit to optimize the energy consumption of the evaluation.

[0161] The monitoring unit can estimate the user's emotions and adjust the monitoring content based on the estimated user's emotions. The monitoring unit estimates the emotions using, for example, facial expression recognition or voice analysis of the user. For example, if the user is relaxed, the monitoring unit can increase the monitoring options to increase flexibility. Furthermore, if the user is feeling stressed, the monitoring unit can reduce the monitoring options and provide simple settings. Furthermore, if the user is excited, the monitoring unit can diversify the monitoring options to increase entertainment value. This enables the monitoring unit to adjust the monitoring content based on the user's emotions. For example, the monitoring unit can estimate the emotions using facial expression recognition or voice analysis of the user and increase the monitoring options. If the user is feeling stressed, the monitoring unit can reduce the monitoring options and provide simple settings. If the user is excited, the monitoring unit can diversify the monitoring options to increase entertainment value. This enables the monitoring unit to adjust the monitoring content based on the user's emotions.

[0162] During monitoring, the monitoring unit can select the optimal monitoring method by referring to past monitoring data. For example, the monitoring unit preferentially selects monitoring methods that have received high ratings in the past. For example, the monitoring unit analyzes past monitoring data and selects the most efficient monitoring method. The monitoring unit can also refer to data on past monitoring failures and select a monitoring method that will avoid similar failures. This enables the monitoring unit to select the optimal monitoring method based on past monitoring data. For example, the monitoring unit can preferentially select monitoring methods that have received high ratings in the past. The monitoring unit can analyze past monitoring data and select the most efficient monitoring method. The monitoring unit can refer to data on past monitoring failures and select a monitoring method that will avoid similar failures. This enables the monitoring unit to select the optimal monitoring method based on past monitoring data.

[0163] The monitoring unit can perform monitoring specialized for a specific industry or application during monitoring. For example, the monitoring unit performs monitoring to support diagnostic support and treatment planning for the medical industry. For example, the monitoring unit performs monitoring to support learning support and teaching material creation for the education industry. The monitoring unit can also perform monitoring to improve content generation and user experience for the entertainment industry. This enables the monitoring unit to perform monitoring specialized for a specific industry or application. For example, the monitoring unit can perform monitoring to support diagnostic support and treatment planning for the medical industry. The monitoring unit can perform monitoring to support learning support and teaching material creation for the education industry. The monitoring unit can perform monitoring to improve content generation and user experience for the entertainment industry. This enables the monitoring unit to perform monitoring specialized for a specific industry or application.

[0164] The monitoring unit can adjust the monitoring content by reflecting user feedback during monitoring. The monitoring unit, for example, collects feedback from the user and adjusts parameters of the monitoring content. The monitoring unit, for example, identifies and adjusts areas for improvement in the monitoring content based on user evaluations. The monitoring unit can also analyze the user's usage history and optimize the monitoring content. This enables the monitoring unit to adjust the monitoring content based on user feedback. For example, the monitoring unit can collect feedback from the user and adjust parameters of the monitoring content. The monitoring unit can identify and adjust areas for improvement in the monitoring content based on user evaluations. The monitoring unit can analyze the user's usage history and optimize the monitoring content. This enables the monitoring unit to adjust the monitoring content based on user feedback.

[0165] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user's emotions. The monitoring unit estimates the emotions using, for example, facial expression recognition or voice analysis of the user. The monitoring unit allows the user to freely set monitoring priorities, for example, when the user is relaxed. Furthermore, the monitoring unit can preferentially display important monitoring items when the user is feeling stressed. Furthermore, the monitoring unit can preferentially display monitoring items that are highly entertaining when the user is excited. This enables the monitoring unit to determine monitoring priorities based on the user's emotions. For example, the monitoring unit can estimate the user's emotions using facial expression recognition or voice analysis of the user and allow the user to freely set monitoring priorities. When the user is feeling stressed, the monitoring unit can preferentially display important monitoring items. When the user is excited, the monitoring unit can preferentially display monitoring items that are highly entertaining. This enables the monitoring unit to determine monitoring priorities based on the user's emotions.

[0166] The monitoring unit can integrate information from different data sources during monitoring. The monitoring unit, for example, integrates information from multiple databases to improve the accuracy of monitoring. The monitoring unit, for example, integrates information from social media and news sites to improve the real-time nature of monitoring. The monitoring unit can also integrate the user's past usage history and feedback to realize personalized monitoring. This enables the monitoring unit to perform monitoring that integrates information from different data sources. For example, the monitoring unit can integrate information from multiple databases to improve the accuracy of monitoring. The monitoring unit can integrate information from social media and news sites to improve the real-time nature of monitoring. The monitoring unit can integrate the user's past usage history and feedback to realize personalized monitoring. This enables the monitoring unit to perform monitoring that integrates information from different data sources.

[0167] The monitoring unit can adjust the monitoring speed during monitoring to perform the monitoring efficiently. For example, the monitoring unit can increase the monitoring speed in response to a user request and quickly provide the generated AI. For example, the monitoring unit can adjust the monitoring speed to efficiently use resources. The monitoring unit can also adjust the monitoring speed to perform monitoring efficiently while maintaining the quality of the generated AI. This enables the monitoring unit to adjust the monitoring speed. For example, the monitoring unit can increase the monitoring speed in response to a user request and quickly provide the generated AI. The monitoring unit can adjust the monitoring speed to efficiently use resources. The monitoring unit can adjust the monitoring speed to perform monitoring efficiently while maintaining the quality of the generated AI. This enables the monitoring unit to adjust the monitoring speed.

[0168] The monitoring unit can optimize the energy consumption of the monitoring during monitoring. The monitoring unit reduces the energy consumption by, for example, improving the efficiency of the monitoring algorithm. The monitoring unit can minimize the energy consumption by, for example, optimizing the monitoring process. The monitoring unit can also optimize the energy consumption of the monitoring by using highly energy-efficient hardware. This enables the monitoring unit to optimize the energy consumption of the monitoring. For example, the monitoring unit can reduce the energy consumption by, for example, improving the efficiency of the monitoring algorithm. The monitoring unit can minimize the energy consumption by optimizing the monitoring process. The monitoring unit can use highly energy-efficient hardware to optimize the energy consumption of the monitoring. This enables the monitoring unit to optimize the energy consumption of the monitoring. === Hard Collateral 1-1 === Each of the multiple elements including the generation unit, customization unit, sales unit, trial unit, and management unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The customization unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The sales unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The trial unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The management unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned generation unit, customization unit, sales unit, trial unit, and management unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The customization unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The sales unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The trial unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The management unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned generation unit, customization unit, sales unit, trial unit, and management unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The customization unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The sales unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The trial unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The management unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned generation unit, customization unit, sales unit, trial unit, and management unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The customization unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The sales unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The trial unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The management unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0169] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0170] The generation AI market system can further include a behavior analysis unit that analyzes user behavior history. The behavior analysis unit analyzes, for example, which generation AIs users frequently use and which functions they use most frequently. This can be used to customize the generation AI or add new functions. The behavior analysis unit can also make suggestions to promote the use of the generation AI based on user behavior patterns. For example, if the generation AI is frequently used during a specific time period, promotions can be tailored to that time period. Furthermore, the behavior analysis unit can share user behavior data with other elements to help improve the performance of the generation AI and the user experience.

[0171] The generative AI market system may further include a feedback collection unit that collects user feedback in real time. For example, the feedback collection unit may display a pop-up window while the user is using the generative AI and conduct a simple survey. The feedback collection unit may also automatically send an email requesting feedback after the user has used the generative AI. This allows for the rapid collection of user opinions and feedback, which can be used to improve the generative AI. Furthermore, the feedback collection unit may share the collected feedback with other elements and reflect it in customizing the generative AI or developing new functions.

[0172] The generative AI market system can further include an interface adjustment unit that estimates the user's emotions and adjusts the generative AI's interface based on the estimated emotions. For example, if the user is relaxed, the interface adjustment unit changes the interface to a simpler, more intuitive design. Also, if the user is feeling stressed, the interface can be adjusted to be easier and more user-friendly. Furthermore, if the user is excited, entertainment elements can be added to the interface to maintain the user's excitement. This enables the interface adjustment unit to optimize the interface based on the user's emotions.

[0173] The generative AI market system can further include an offering content adjustment unit that estimates the user's emotions and adjusts the offering content of the generative AI based on the estimated emotions. For example, if the user is relaxed, the offering content adjustment unit can preferentially provide generative AI related to relaxation and entertainment. Also, if the user is stressed, the offering content adjustment unit can provide generative AI related to stress reduction and relaxation. Furthermore, if the user is excited, the offering content adjustment unit can provide generative AI related to action and adventure. This enables the offering content adjustment unit to provide generative AI based on the user's emotions.

[0174] The generation AI market system can further include a price adjustment unit that estimates the user's emotions and adjusts the price of the generation AI based on the estimated emotions. For example, the price adjustment unit can provide the generation AI at a regular price if the user is relaxed. Also, the price adjustment unit can provide the generation AI at a discounted price if the user is stressed. Furthermore, the price adjustment unit can provide the generation AI at a special price if the user is excited. This enables the price adjustment unit to set prices based on the user's emotions.

[0175] The generation AI market system can further include a recommendation unit that recommends generation AIs based on the user's behavioral history. The recommendation unit, for example, analyzes the history of generation AIs that the user has used in the past and recommends similar generation AIs. The recommendation unit can also propose new generation AIs based on the user's behavioral patterns. Furthermore, the recommendation unit can adjust the recommended content of the generation AI based on user feedback. This enables the recommendation unit to recommend the optimal generation AI based on the user's behavioral history.

[0176] The generation AI market system can further include a performance optimization unit that monitors user usage in real time and optimizes the performance of the generation AI. The performance optimization unit monitors, for example, the frequency of use and error rate of the generation AI and adjusts performance as necessary. The performance optimization unit can also analyze user usage and optimize resource allocation for the generation AI. Furthermore, the performance optimization unit can make adjustments to improve the performance of the generation AI based on user feedback. This enables the performance optimization unit to optimize the performance of the generation AI in real time.

[0177] The generation AI market system can further include a customization recommendation unit that customizes the generation AI based on the user's behavioral history. The customization recommendation unit can, for example, suggest new customization options based on customization options previously selected by the user. The customization recommendation unit can also analyze the user's behavioral patterns and suggest the optimal customization method. Furthermore, the customization recommendation unit can adjust the customization content based on user feedback. This enables the customization recommendation unit to suggest the optimal customization based on the user's behavioral history.

[0178] The generative AI market system can further include a guide providing unit that estimates the user's emotions and provides a usage guide for the generative AI based on the estimated emotions. For example, the guide providing unit can provide a detailed usage guide when the user is relaxed. Also, it can provide a concise and easy-to-understand usage guide when the user is stressed. Furthermore, it can provide an interactive usage guide when the user is excited. This enables the guide providing unit to provide the optimal usage guide based on the user's emotions.

[0179] The generation AI market system can further include a training data optimization unit that optimizes the training data of the generation AI based on the user's behavioral history. The training data optimization unit, for example, analyzes data that the user has used in the past and selects optimal training data. The training data optimization unit can also add new training data based on the user's behavioral patterns. Furthermore, the training data optimization unit can adjust the content of the training data based on user feedback. This enables the training data optimization unit to select optimal training data based on the user's behavioral history.

[0180] The processing flow of the second embodiment will be briefly explained below.

[0181] Step 1: The generator creates a generator AI. The generator creates a generator AI based on a specific theme or request, and can analyze the generator AI's algorithm to select the optimal one. The generator can also adjust the generator AI's generation speed and energy consumption to generate more efficiently. Step 2: The customization unit customizes the generated AI created by the generation unit based on themes and requests. The customization unit adds functions to the generated AI or changes its design. It can also estimate the user's emotions and adjust the customization content based on the estimated emotions. Step 3: The sales department sells the generative AI customized by the customization department on the Internet. The sales department sells the generative AI through online marketplaces or dedicated sales sites. The sales department can also estimate user emotions and adjust sales strategies based on the estimated emotions. Step 4: The trial department allows purchasers to try out the prototype of the generative AI sold by the sales department. The trial department sets the trial period and trial conditions, allowing purchasers to check the performance of the generative AI. The trial department can also estimate the user's emotions and adjust the trial content based on the estimated emotions. Step 5: The management unit manages the usage fees for the generative AI tested by the trial unit. The management unit sets monthly fees and fees based on usage volume and manages the usage fees. It can also estimate the user's emotions and adjust the usage fee management method based on the estimated emotions.

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0202] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0218] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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."

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] [Explanation of symbols]

[0254] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A generation unit that generates a generated AI; a customization unit that customizes the generated AI created by the generation unit based on a theme or request; a sales department that sells the generated AI customized by the customization department on the Internet; A trial section where a purchaser can try out a prototype of the generated AI sold by the sales section; a management unit that manages the usage fee of the generated AI tried by the trial unit; system.

2. Equipped with an analysis unit that analyzes the algorithm of the generated AI 2. The system of claim 1.

3. Equipped with an evaluation unit that evaluates the generated AI 2. The system of claim 1.

4. Equipped with a monitoring unit that monitors the usage status of the generated AI 2. The system of claim 1.

5. The generation unit Creating generative AI for specific themes or requests The system of claim 1 .

6. The customization unit Customize generative AI based on themes and requests 2. The system of claim 1.

7. The sales department Selling generative AI online 2. The system of claim 1.

8. The trial section Buyers test generative AI prototypes 2. The system of claim 1.

9. The management unit Manage usage fees for generated AI 2. The system of claim 1.

10. The generation unit The user's emotions are estimated, and an AI theme is selected based on the estimated user's emotions. The system of claim 1 .

11. The generation unit At the time of generation, the appropriate generation algorithm is selected by referring to the performance data of past generation AIs. The system of claim 1 .

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