System

Abstract

An object of the system according to the embodiment is to provide an optimal service in a group or a group-oriented system by merging individual generated AI.SOLUTION: A system includes a generation AI, an information share part, and a cooperation part. The generative AI learns the preferences and behavior patterns of individual users. The information share unit shares information included in the generated AI. The cooperation part provides an optimum service based on the information shared by the information sharing part.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, each generative AI is optimized independently, making it difficult to provide optimal services in groups or systems for groups.

[0005] The system according to the embodiment aims to combine individual generative AIs to provide optimal services for groups or systems aimed at groups. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation AI, an information sharing unit, and a collaboration unit. The generation AI learns the preferences and behavioral patterns of individual users. The information sharing unit shares information held by the generation AI. The collaboration unit provides optimal services based on the information shared by the information sharing unit. [Effects of the Invention]

[0007] The system according to the embodiment can combine individual generative AIs to provide optimal services for groups or systems aimed at groups. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Fusion Generative AI Platform according to an embodiment of the present invention is a platform that fuses individual generative AIs. This platform provides a foundation for multiple generative AIs to work together, enabling groups and systems for groups to provide more valuable services. As a result, the Fusion Generative AI Platform enables groups and systems for groups to provide more valuable services, promoting the improvement of human knowledge and skills and realizing a society that contributes to the creation of new value.

[0029] The Fusion generative AI platform according to the embodiment includes a generative AI, an information sharing unit, and a collaboration unit. The generative AI learns the preferences and behavioral patterns of individual users. For example, the generative AI learns a user's purchase history and predicts their next purchase. The generative AI can also learn a user's browsing history and recommend related content. The generative AI can also learn a user's usage frequency and provide optimal services. The information sharing unit shares information held by the generative AI. For example, the information sharing unit shares user preferences and behavioral patterns learned by the generative AI with other generative AIs. The information sharing unit can also store data collected by the generative AI in a central database so that other generative AIs can access it. The information sharing unit can also share the skills and knowledge held by the generative AI, allowing the collaboration unit to provide optimal services. The collaboration unit provides optimal services based on the shared information. For example, when multiple generative AIs collaborate on a project, the collaboration unit divides up roles based on the areas in which each generative AI excels, improving overall efficiency. In addition, when multiple generative AIs work together within a company to streamline operations, the collaboration department shares information held by each generative AI and makes optimal decisions, thereby improving business efficiency. In addition, in educational settings, the collaboration department improves learning effectiveness by having multiple generative AIs work together to provide optimal learning plans for each student. As a result, the Fusion generative AI platform according to the embodiment allows individual generative AIs to work together to provide optimal services.

[0030] Generative AI can monitor a user's health status and provide advice and services according to the health status. For example, generative AI can monitor a user's health data (heart rate, sleep patterns, etc.) and provide advice according to the health status. For example, if the user is not getting enough exercise, it can suggest an appropriate exercise plan. Generative AI can also analyze a user's health status in real time and provide health management services. For example, it can check the nutritional balance of meals and suggest areas for improvement. Generative AI can also send reminders and notifications to the user based on the health status. For example, it can send notifications encouraging regular health checks and medication. This allows it to provide appropriate advice and services according to the user's health status.

[0031] Generative AI can learn a user's long-term goals and suggest steps toward achieving them. For example, generative AI can learn a user's long-term goals (career, health, hobbies, etc.) and suggest specific steps toward achieving them. For example, it can provide a skill acquisition plan for career advancement. Generative AI can also monitor the user's progress toward achieving their goals and provide appropriate feedback and advice. For example, it can send messages to maintain motivation to achieve their goals. Generative AI can also support the user's daily life based on their long-term goals. For example, it can suggest diet and exercise based on health goals. This can support the user in achieving their long-term goals.

[0032] Generative AI can work with multiple devices in the home, learning the preferences and behavioral patterns of each family member to provide the optimal home environment. For example, generative AI can work with smart devices in the home (lighting, air conditioners, televisions, etc.) to learn the preferences and behavioral patterns of each family member and provide optimal settings. For example, it can automatically adjust the lighting based on the time each family member returns home. Generative AI can also analyze the behavioral patterns of each family member and optimally control home devices. For example, it can adjust the air conditioner temperature based on the time the family gathers in the living room. Generative AI can also learn the preferences of each family member and suggest entertainment content. For example, it can recommend movies and music that the whole family can enjoy. In this way, generative AI can work with home devices to provide the optimal home environment.

[0033] The generative AI can work with the generative AI of a colleague at work to make suggestions to improve the productivity of the entire team. For example, the generative AI can work with the generative AI of a colleague at work to optimize the schedule for the entire team. For example, it can adjust meeting schedules and suggest times that are convenient for everyone to attend. The generative AI can also support task management for the entire team to improve productivity. For example, it can share task progress in real time and send alerts if delays occur. The generative AI can also work with the generative AI of a colleague at work to smooth communication across the entire team. For example, it can automatically share important messages and information so that everyone can keep up to date with the latest information. This makes it possible to work with the generative AI of a colleague at work to make suggestions to improve the productivity of the entire team.

[0034] Generative AI can develop algorithms that automatically share information with other generative AIs and work together to make optimal decisions. For example, generative AIs can develop protocols that automatically share information with other generative AIs and work together to make optimal decisions. For example, multiple generative AIs can work together to share project progress in real time. Generative AIs can also develop algorithms that work together to provide optimal services through information sharing with other generative AIs. For example, multiple generative AIs can work together to provide services that meet user needs. Generative AIs can also build information sharing systems that work with other generative AIs to make optimal decisions. For example, data held by each generative AI can be integrated to make optimal decisions as a whole. This allows them to share information with other generative AIs and work together to make optimal decisions.

[0035] A generative AI can analyze a user's behavioral history and predict when collaboration with other generative AIs will be necessary. For example, a generative AI may analyze a user's behavioral history and develop an algorithm to predict when collaboration with other generative AIs will be necessary. For example, it may predict when a user will need the support of other generative AIs when performing a specific task. A generative AI may also build a system that optimizes collaboration with other generative AIs based on the user's behavioral history. For example, when a user uses multiple devices, generative AIs collaborate to provide optimal services. A generative AI may also analyze behavioral history and predict in real time when collaboration with other generative AIs will be necessary. For example, it may prepare the necessary resources before the user starts a specific activity. This allows it to predict when collaboration with other generative AIs will be necessary based on the user's behavioral history.

[0036] Generative AI can work with other generative AIs in different industries to achieve cross-industry optimization. For example, generative AI can work with other generative AIs in different industries to build a system that achieves cross-industry optimization. For example, generative AIs from the medical and logistics industries can work together to achieve efficient delivery of pharmaceuticals. Generative AIs from different industries can also share information and develop algorithms that provide optimal services to each other. For example, generative AIs from the financial and retail industries can work together to predict customer purchasing behavior. Generative AI can also integrate data from different industries to achieve cross-industry optimization and build a system that makes optimal decisions as a whole. For example, generative AIs from the energy and transportation industries can work together to optimize energy consumption. This allows generative AIs from different industries to work together to achieve cross-industry optimization.

[0037] Generative AI can work in collaboration with public service generative AI to improve services throughout the region. For example, generative AI can work in collaboration with public service generative AI to build a system that aims to improve services throughout the region. For example, it can work in collaboration with a traffic management system to alleviate traffic congestion. Generative AI can also share information with public service generative AI to develop algorithms that provide optimal services throughout the region. For example, it can work in collaboration with medical services to speed up emergency response. Generative AI can also build a system that integrates public service data and makes optimal decisions, aiming to improve services throughout the region. For example, it can work in collaboration with education services to improve the local educational environment. In this way, it can work in collaboration with public service generative AI to improve services throughout the region.

[0038] The generation AI analyzes the skill sets of each generation AI and can automatically allocate tasks optimally. For example, the generation AI analyzes the skill sets of each generation AI and develops an algorithm that automatically allocates tasks optimally. For example, it assigns tasks according to each generation AI's area of ​​expertise. The generation AI also builds a system that optimizes task allocation based on the skill sets of each generation AI. For example, it dynamically adjusts tasks according to the progress of the project. The generation AI also provides feedback to ensure optimal task allocation based on the results of the skill set analysis. For example, it sets task priorities based on the skill sets. This makes it possible to analyze the skill sets of each generation AI and automatically allocate tasks optimally.

[0039] The Generative AI can monitor the performance of each Generative AI in real time and make adjustments as necessary. For example, the Generative AI can build a system that monitors the performance of each Generative AI in real time and makes adjustments as necessary. For example, it can send an alert if performance drops. The Generative AI can also develop an algorithm that makes optimal adjustments based on the performance data of each Generative AI. For example, it can identify the cause of performance decline and propose appropriate countermeasures. The Generative AI can also provide feedback to make adjustments in real time based on the performance monitoring results. For example, it can make adjustments to maintain optimal performance. This makes it possible to monitor the performance of each Generative AI in real time and make adjustments as necessary.

[0040] Generative AI can link generative AIs from different companies to improve the efficiency of an entire industry. For example, generative AI can link generative AIs from different companies to build a system that improves the efficiency of an entire industry. For example, it can optimize the entire supply chain. Generative AI can also develop algorithms that allow generative AIs from different companies to share information and provide optimal services to each other. For example, generative AIs from the manufacturing and logistics industries can work together to optimize inventory management. Generative AI can also build a system that integrates data from different companies and makes optimal decisions as a whole to improve the efficiency of an entire industry. For example, generative AIs from the energy and transportation industries can work together to optimize energy consumption. This allows generative AIs from different companies to work together and improve the efficiency of an entire industry.

[0041] Generative AI can improve the quality of education by linking the generative AIs of educational institutions. For example, generative AI can link the generative AIs of educational institutions to build a system that improves the quality of education. For example, each generative AI can share its educational resources and provide optimal learning plans. Generative AI can also develop algorithms that allow the generative AIs of educational institutions to share information and provide optimal educational services to each other. For example, generative AIs from different educational institutions can work together to provide the optimal learning plan for each student. Generative AI can also build a system that integrates data from educational institutions and makes optimal decisions as a whole to improve the quality of education. For example, it can provide feedback to improve the quality of education based on the data each generative AI has. This allows the generative AIs of educational institutions to work together to improve the quality of education.

[0042] The generation AI can analyze the performance of each generation AI and allocate resources optimally. For example, the generation AI can analyze the performance of each generation AI in real time and build a system that allocates resources optimally. For example, it can concentrate resources on generation AIs with high performance. The generation AI can also develop an algorithm that optimizes resource allocation based on the performance data of each generation AI. For example, it can identify the cause of performance decline and propose appropriate countermeasures. The generation AI can also provide feedback to allocate resources optimally based on the performance analysis results. For example, it can make adjustments to maintain optimal performance. This makes it possible to analyze the performance of each generation AI and allocate resources optimally.

[0043] A generative AI can strengthen cooperation among generative AIs and maximize the efficiency of the entire group. For example, a generative AI can strengthen cooperation among generative AIs and build a system that maximizes the efficiency of the entire group. For example, each generative AI can share information it has in real time to make optimal decisions. A generative AI can also develop algorithms to strengthen cooperation among generative AIs and maximize the efficiency of the entire group. For example, each generative AI can assign tasks based on its area of ​​expertise, improving overall efficiency. A generative AI can also build a feedback system to strengthen cooperation among generative AIs and maximize the efficiency of the entire group. For example, it can monitor the effectiveness of cooperation in real time and make adjustments as necessary. This can strengthen cooperation among generative AIs and maximize the efficiency of the entire group.

[0044] Generative AI can link generative AIs in different regions and promote inter-regional cooperation. For example, generative AI can link generative AIs in different regions and build a system that promotes inter-regional cooperation. For example, generative AIs in different regions share information and provide optimal services. Generative AI can also develop algorithms that allow generative AIs in different regions to link together and promote inter-regional cooperation. For example, by providing services tailored to the needs of each region. Generative AI can also integrate data from generative AIs in different regions and build a system that makes optimal decisions as a whole to promote inter-regional cooperation. For example, by optimally allocating resources to each region. This allows generative AIs in different regions to link together and promote inter-regional cooperation.

[0045] Generative AI can link generative AIs from different industries to create synergy between industries. For example, generative AI can link generative AIs from different industries to build a system that creates synergy between industries. For example, generative AIs from the medical and logistics industries can work together to achieve efficient delivery of pharmaceuticals. Generative AI can also develop algorithms that allow generative AIs from different industries to work together to create synergy between industries. For example, generative AIs from the financial and retail industries can work together to predict customer purchasing behavior. Generative AI can also create synergy between industries by integrating data from generative AIs from different industries to build a system that makes optimal decisions as a whole. For example, generative AIs from the energy and transportation industries can work together to optimize energy consumption. This allows generative AIs from different industries to work together and create synergy between industries.

[0046] The generative AI can analyze the user's skill level and propose the optimal learning plan. For example, the generative AI can build a system that analyzes the user's skill level in real time and proposes the optimal learning plan. For example, it can provide learning content that matches the user's current skill level. The generative AI can also develop an algorithm that proposes the optimal learning plan based on the user's skill level data. For example, it can propose specific steps necessary to improve the user's skills. The generative AI can also build a system that customizes the learning plan for the user based on the results of the skill level analysis. For example, it can dynamically adjust the learning plan according to the user's progress. This makes it possible to analyze the user's skill level and propose the optimal learning plan.

[0047] A generative AI can collaborate with other generative AIs to jointly share knowledge and improve skills. A generative AI, for example, collaborates with other generative AIs to build a system that jointly shares knowledge and improves skills. For example, multiple generative AIs collaborate to provide optimal learning content to users. A generative AI also develops algorithms that share knowledge and improve skills through collaboration with other generative AIs. For example, each generative AI shares its specialized knowledge and optimizes learning plans for users. A generative AI also builds a system that strengthens collaboration with other generative AIs to share knowledge and improve skills. For example, data held by each generative AI is integrated to provide the optimal learning plan as a whole. This allows collaboration with other generative AIs to jointly share knowledge and improve skills.

[0048] Generative AI can collaborate with experts from different fields to improve cross-disciplinary knowledge. For example, generative AI could collaborate with experts from different fields to build a system that improves cross-disciplinary knowledge. For example, experts from the medical and engineering fields could collaborate to develop new technologies. Generative AI could also develop algorithms that improve cross-disciplinary knowledge through collaboration with experts from different fields. For example, it could integrate specialized knowledge from each field and provide new learning content. Generative AI could also build a system that strengthens collaboration with experts from different fields to improve cross-disciplinary knowledge. For example, it could integrate data from each field and provide the optimal learning plan as a whole. This would enable collaboration with experts from different fields to improve cross-disciplinary knowledge.

[0049] Generative AI can work with educational institutions to improve the quality of educational programs. For example, generative AI could work with educational institutions to build a system that improves the quality of educational programs. For example, it could share the educational resources held by each educational institution and provide optimal learning plans. Generative AI could also develop algorithms that improve the quality of educational programs through collaboration with educational institutions. For example, generative AIs from different educational institutions could work together to provide optimal learning plans for each student. Generative AI could also build a system that integrates data from educational institutions and makes optimal decisions as a whole to improve the quality of educational programs. For example, it could provide feedback to improve the quality of education based on the data held by each generative AI. This would allow collaboration with educational institutions to improve the quality of educational programs.

[0050] Generative AI can analyze data from different industries and discover new market opportunities. For example, generative AI can analyze data from different industries and build a system to discover new market opportunities. For example, it can integrate data from the medical and engineering fields to develop new technologies. Generative AI can also develop algorithms to discover new market opportunities based on data from different industries. For example, it can analyze data from each industry and propose new business models. Generative AI can also integrate data from different industries to discover new market opportunities and build a system to make optimal decisions as a whole. For example, it can integrate data from the energy and transportation industries to discover new market opportunities. This makes it possible to analyze data from different industries and discover new market opportunities.

[0051] Generative AI can collaborate with other generative AIs to jointly advance projects that create new value. For example, generative AI can collaborate with other generative AIs to build a system that jointly advances projects that create new value. For example, multiple generative AIs collaborate to develop new products and services. Generative AI can also develop algorithms that advance projects that create new value through collaboration with other generative AIs. For example, each generative AI can share its specialized knowledge and propose new business models. Generative AI can also build a system that strengthens collaboration with other generative AIs to create new value. For example, data held by each generative AI can be integrated to advance the optimal project as a whole. This allows generative AIs to collaborate with other generative AIs to jointly advance projects that create new value.

[0052] A generative AI can work with other generative AIs in different regions to provide new services tailored to the needs of each region. For example, a generative AI can work with other generative AIs in different regions to build a system that provides new services tailored to the needs of each region. For example, it can provide services tailored to the characteristics of each region. Furthermore, a generative AI can work with other generative AIs in different regions to develop algorithms that provide new services tailored to the needs of each region. For example, it can analyze data from each region and propose new business models. Furthermore, in order to provide new services tailored to the needs of each region, a generative AI can integrate data from different regions and build a system that makes optimal decisions as a whole. For example, it can optimally allocate resources to each region. This allows a generative AI in different regions to work together to provide new services tailored to the needs of each region.

[0053] Generative AI can propose optimal measures to address environmental issues and support the realization of a sustainable society. For example, generative AI can propose optimal measures to address environmental issues and build systems to support the realization of a sustainable society. For example, it can optimize energy consumption. Generative AI can also develop algorithms to propose optimal measures to address environmental issues and propose specific actions for environmental protection. For example, it can promote the use of renewable energy. Generative AI can also analyze environmental data and build systems to propose optimal measures to support the realization of a sustainable society. For example, it can perform resource management to minimize environmental impact. This can propose optimal measures to address environmental issues and support the realization of a sustainable society.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] Generative AI can monitor a user's health status and provide advice and services according to the health status. For example, generative AI can monitor a user's health data (heart rate, sleep patterns, etc.) and provide advice according to the health status. For example, if the user is not getting enough exercise, it can suggest an appropriate exercise plan. Generative AI can also analyze a user's health status in real time and provide health management services. For example, it can check the nutritional balance of meals and suggest areas for improvement. Generative AI can also send reminders and notifications to the user based on the health status. For example, it can send notifications encouraging regular health checks and medication. This allows it to provide appropriate advice and services according to the user's health status.

[0056] Generative AI can learn a user's long-term goals and suggest steps toward achieving them. For example, generative AI can learn a user's long-term goals (career, health, hobbies, etc.) and suggest specific steps toward achieving them. For example, it can provide a skill acquisition plan for career advancement. Generative AI can also monitor the user's progress toward achieving their goals and provide appropriate feedback and advice. For example, it can send messages to maintain motivation to achieve their goals. Generative AI can also support the user's daily life based on their long-term goals. For example, it can suggest diet and exercise based on health goals. This can support the user in achieving their long-term goals.

[0057] Generative AI can work with multiple devices in the home, learning the preferences and behavioral patterns of each family member to provide the optimal home environment. For example, generative AI can work with smart devices in the home (lighting, air conditioners, televisions, etc.) to learn the preferences and behavioral patterns of each family member and provide optimal settings. For example, it can automatically adjust the lighting based on the time each family member returns home. Generative AI can also analyze the behavioral patterns of each family member and optimally control home devices. For example, it can adjust the air conditioner temperature based on the time the family gathers in the living room. Generative AI can also learn the preferences of each family member and suggest entertainment content. For example, it can recommend movies and music that the whole family can enjoy. In this way, generative AI can work with home devices to provide the optimal home environment.

[0058] The generative AI can work with the generative AI of a colleague at work to make suggestions to improve the productivity of the entire team. For example, the generative AI can work with the generative AI of a colleague at work to optimize the schedule for the entire team. For example, it can adjust meeting schedules and suggest times that are convenient for everyone to attend. The generative AI can also support task management for the entire team to improve productivity. For example, it can share task progress in real time and send alerts if delays occur. The generative AI can also work with the generative AI of a colleague at work to smooth communication across the entire team. For example, it can automatically share important messages and information so that everyone can keep up to date with the latest information. This makes it possible to work with the generative AI of a colleague at work to make suggestions to improve the productivity of the entire team.

[0059] Generative AI can develop algorithms that automatically share information with other generative AIs and work together to make optimal decisions. For example, generative AIs can develop protocols that automatically share information with other generative AIs and work together to make optimal decisions. For example, multiple generative AIs can work together to share project progress in real time. Generative AIs can also develop algorithms that work together to provide optimal services through information sharing with other generative AIs. For example, multiple generative AIs can work together to provide services that meet user needs. Generative AIs can also build information sharing systems that work with other generative AIs to make optimal decisions. For example, data held by each generative AI can be integrated to make optimal decisions as a whole. This allows them to share information with other generative AIs and work together to make optimal decisions.

[0060] A generative AI can analyze a user's behavioral history and predict when collaboration with other generative AIs will be necessary. For example, a generative AI may analyze a user's behavioral history and develop an algorithm to predict when collaboration with other generative AIs will be necessary. For example, it may predict when a user will need the support of other generative AIs when performing a specific task. A generative AI may also build a system that optimizes collaboration with other generative AIs based on the user's behavioral history. For example, when a user uses multiple devices, generative AIs collaborate to provide optimal services. A generative AI may also analyze behavioral history and predict in real time when collaboration with other generative AIs will be necessary. For example, it may prepare the necessary resources before the user starts a specific activity. This allows it to predict when collaboration with other generative AIs will be necessary based on the user's behavioral history.

[0061] Generative AI can work with other generative AIs in different industries to achieve cross-industry optimization. For example, generative AI can work with other generative AIs in different industries to build a system that achieves cross-industry optimization. For example, generative AIs from the medical and logistics industries can work together to achieve efficient delivery of pharmaceuticals. Generative AIs from different industries can also share information and develop algorithms that provide optimal services to each other. For example, generative AIs from the financial and retail industries can work together to predict customer purchasing behavior. Generative AI can also integrate data from different industries to achieve cross-industry optimization and build a system that makes optimal decisions as a whole. For example, generative AIs from the energy and transportation industries can work together to optimize energy consumption. This allows generative AIs from different industries to work together to achieve cross-industry optimization.

[0062] Generative AI can work in collaboration with public service generative AI to improve services throughout the region. For example, generative AI can work in collaboration with public service generative AI to build a system that aims to improve services throughout the region. For example, it can work in collaboration with a traffic management system to alleviate traffic congestion. Generative AI can also share information with public service generative AI to develop algorithms that provide optimal services throughout the region. For example, it can work in collaboration with medical services to speed up emergency response. Generative AI can also build a system that integrates public service data and makes optimal decisions, aiming to improve services throughout the region. For example, it can work in collaboration with education services to improve the local educational environment. In this way, it can work in collaboration with public service generative AI to improve services throughout the region.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The generative AI learns the preferences and behavioral patterns of each individual user. For example, the generative AI can learn a user's purchasing history and predict their next purchase. It can also learn a user's browsing history and recommend related content. It can also learn a user's frequency of use and provide optimal services. Step 2: The information sharing unit shares the information held by the generating AI. For example, the information sharing unit shares the user preferences and behavioral patterns learned by the generating AI with other generating AIs. The information sharing unit can also store the data collected by the generating AI in a central database so that other generating AIs can access it. Furthermore, the generating AI can share its skills and knowledge, allowing the collaboration unit to provide optimal services. Step 3: The Collaboration Department provides optimal services based on the shared information. For example, when multiple generative AIs work together on a project, they share roles in the areas in which they excel, improving overall efficiency. Also, when multiple generative AIs work together within a company to streamline operations, they share the information they have and make optimal decisions, improving work efficiency. Furthermore, in educational settings, multiple generative AIs work together to provide optimal learning plans for each student, improving learning effectiveness.

[0065] (Example 2) The Fusion Generative AI Platform according to an embodiment of the present invention is a platform that fuses individual generative AIs. This platform provides a foundation for multiple generative AIs to work together, enabling groups and systems for groups to provide more valuable services. As a result, the Fusion Generative AI Platform enables groups and systems for groups to provide more valuable services, promoting the improvement of human knowledge and skills and realizing a society that contributes to the creation of new value.

[0066] The Fusion generative AI platform according to the embodiment includes a generative AI, an information sharing unit, and a collaboration unit. The generative AI learns the preferences and behavioral patterns of individual users. For example, the generative AI learns a user's purchase history and predicts their next purchase. The generative AI can also learn a user's browsing history and recommend related content. The generative AI can also learn a user's usage frequency and provide optimal services. The information sharing unit shares information held by the generative AI. For example, the information sharing unit shares user preferences and behavioral patterns learned by the generative AI with other generative AIs. The information sharing unit can also store data collected by the generative AI in a central database so that other generative AIs can access it. The information sharing unit can also share the skills and knowledge held by the generative AI, allowing the collaboration unit to provide optimal services. The collaboration unit provides optimal services based on the shared information. For example, when multiple generative AIs collaborate on a project, the collaboration unit divides up roles based on the areas in which each generative AI excels, improving overall efficiency. In addition, when multiple generative AIs work together within a company to streamline operations, the collaboration department shares information held by each generative AI and makes optimal decisions, thereby improving business efficiency. In addition, in educational settings, the collaboration department improves learning effectiveness by having multiple generative AIs work together to provide optimal learning plans for each student. As a result, the Fusion generative AI platform according to the embodiment allows individual generative AIs to work together to provide optimal services.

[0067] The generation AI can estimate the user's emotions in real time and dynamically change the service content based on the emotions. The generation AI, for example, analyzes the user's facial expressions and voice to estimate emotions in real time. For example, if the user is feeling stressed, it will suggest relaxing music or activities. The generation AI also dynamically changes the service content provided based on the user's emotional data. For example, if the user is tired, it will prioritize suggesting easy tasks. The generation AI also uses its emotion estimation function to provide feedback according to the user's emotions. For example, if the user is happy, it will display a positive message. This makes it possible to provide services that correspond to the user's emotions.

[0068] Generative AI can monitor a user's health status and provide advice and services according to the health status. For example, generative AI can monitor a user's health data (heart rate, sleep patterns, etc.) and provide advice according to the health status. For example, if the user is not getting enough exercise, it can suggest an appropriate exercise plan. Generative AI can also analyze a user's health status in real time and provide health management services. For example, it can check the nutritional balance of meals and suggest areas for improvement. Generative AI can also send reminders and notifications to the user based on the health status. For example, it can send notifications encouraging regular health checks and medication. This allows it to provide appropriate advice and services according to the user's health status.

[0069] Generative AI can learn a user's long-term goals and suggest steps toward achieving them. For example, generative AI can learn a user's long-term goals (career, health, hobbies, etc.) and suggest specific steps toward achieving them. For example, it can provide a skill acquisition plan for career advancement. Generative AI can also monitor the user's progress toward achieving their goals and provide appropriate feedback and advice. For example, it can send messages to maintain motivation to achieve their goals. Generative AI can also support the user's daily life based on their long-term goals. For example, it can suggest diet and exercise based on health goals. This can support the user in achieving their long-term goals.

[0070] Generative AI can work with multiple devices in the home, learning the preferences and behavioral patterns of each family member to provide the optimal home environment. For example, generative AI can work with smart devices in the home (lighting, air conditioners, televisions, etc.) to learn the preferences and behavioral patterns of each family member and provide optimal settings. For example, it can automatically adjust the lighting based on the time each family member returns home. Generative AI can also analyze the behavioral patterns of each family member and optimally control home devices. For example, it can adjust the air conditioner temperature based on the time the family gathers in the living room. Generative AI can also learn the preferences of each family member and suggest entertainment content. For example, it can recommend movies and music that the whole family can enjoy. In this way, generative AI can work with home devices to provide the optimal home environment.

[0071] The generative AI can work with the generative AI of a colleague at work to make suggestions to improve the productivity of the entire team. For example, the generative AI can work with the generative AI of a colleague at work to optimize the schedule for the entire team. For example, it can adjust meeting schedules and suggest times that are convenient for everyone to attend. The generative AI can also support task management for the entire team to improve productivity. For example, it can share task progress in real time and send alerts if delays occur. The generative AI can also work with the generative AI of a colleague at work to smooth communication across the entire team. For example, it can automatically share important messages and information so that everyone can keep up to date with the latest information. This makes it possible to work with the generative AI of a colleague at work to make suggestions to improve the productivity of the entire team.

[0072] The generative AI can use its emotion estimation function to recommend entertainment content based on the user's emotions. For example, the generative AI analyzes the user's emotions in real time and recommends entertainment content based on those emotions. For example, if the user feels like relaxing, it will suggest relaxing music or movies. The generative AI also uses its emotion estimation function to recommend games or activities based on the user's emotions. For example, if the user is feeling stressed, it will suggest games that will help relieve stress. The generative AI also automatically generates a playlist of entertainment content based on the user's emotion data. For example, if the user is happy, it will create a music playlist to maintain a positive mood. This allows it to recommend entertainment content based on the user's emotions.

[0073] Generative AI can develop algorithms that automatically share information with other generative AIs and work together to make optimal decisions. For example, generative AIs can develop protocols that automatically share information with other generative AIs and work together to make optimal decisions. For example, multiple generative AIs can work together to share project progress in real time. Generative AIs can also develop algorithms that work together to provide optimal services through information sharing with other generative AIs. For example, multiple generative AIs can work together to provide services that meet user needs. Generative AIs can also build information sharing systems that work with other generative AIs to make optimal decisions. For example, data held by each generative AI can be integrated to make optimal decisions as a whole. This allows them to share information with other generative AIs and work together to make optimal decisions.

[0074] A generative AI can analyze a user's behavioral history and predict when collaboration with other generative AIs will be necessary. For example, a generative AI may analyze a user's behavioral history and develop an algorithm to predict when collaboration with other generative AIs will be necessary. For example, it may predict when a user will need the support of other generative AIs when performing a specific task. A generative AI may also build a system that optimizes collaboration with other generative AIs based on the user's behavioral history. For example, when a user uses multiple devices, generative AIs collaborate to provide optimal services. A generative AI may also analyze behavioral history and predict in real time when collaboration with other generative AIs will be necessary. For example, it may prepare the necessary resources before the user starts a specific activity. This allows it to predict when collaboration with other generative AIs will be necessary based on the user's behavioral history.

[0075] A generative AI can use the emotion estimation function to understand the emotional state of other generative AIs and build cooperative relationships. For example, a generative AI can use the emotion estimation function to develop an algorithm that understands the emotional state of other generative AIs and build cooperative relationships. For example, it can provide support when another generative AI is feeling stressed. A generative AI can also use the emotion estimation function to build a system that facilitates communication with other generative AIs. For example, it can provide appropriate feedback according to the emotional state. A generative AI can also analyze the emotional state of other generative AIs in real time and suggest actions to build cooperative relationships. For example, it can allocate tasks according to the emotional state. This allows a generative AI to understand the emotional state of other generative AIs and build cooperative relationships.

[0076] Generative AI can work with other generative AIs in different industries to achieve cross-industry optimization. For example, generative AI can work with other generative AIs in different industries to build a system that achieves cross-industry optimization. For example, generative AIs from the medical and logistics industries can work together to achieve efficient delivery of pharmaceuticals. Generative AIs from different industries can also share information and develop algorithms that provide optimal services to each other. For example, generative AIs from the financial and retail industries can work together to predict customer purchasing behavior. Generative AI can also integrate data from different industries to achieve cross-industry optimization and build a system that makes optimal decisions as a whole. For example, generative AIs from the energy and transportation industries can work together to optimize energy consumption. This allows generative AIs from different industries to work together to achieve cross-industry optimization.

[0077] Generative AI can work in collaboration with public service generative AI to improve services throughout the region. For example, generative AI can work in collaboration with public service generative AI to build a system that aims to improve services throughout the region. For example, it can work in collaboration with a traffic management system to alleviate traffic congestion. Generative AI can also share information with public service generative AI to develop algorithms that provide optimal services throughout the region. For example, it can work in collaboration with medical services to speed up emergency response. Generative AI can also build a system that integrates public service data and makes optimal decisions, aiming to improve services throughout the region. For example, it can work in collaboration with education services to improve the local educational environment. In this way, it can work in collaboration with public service generative AI to improve services throughout the region.

[0078] The generative AI can use the emotion estimation function to adjust cooperation with other generative AIs based on the user's emotions. For example, the generative AI uses the emotion estimation function to build a system that adjusts cooperation with other generative AIs based on the user's emotions. For example, if the user is feeling stressed, the generative AI optimizes cooperation with other generative AIs. The generative AI also develops an algorithm that dynamically adjusts cooperation with other generative AIs based on the user's emotional data. For example, if the user is relaxed, the generative AI strengthens cooperation. The generative AI also uses the emotion estimation function to build a system that adjusts cooperation according to the user's emotions. For example, if the user has positive emotions, the system promotes cooperation with other generative AIs. This makes it possible to adjust cooperation with other generative AIs based on the user's emotions.

[0079] The generative AI can integrate the emotion estimation results of each generative AI to optimize the overall emotional balance. For example, the generative AI collects the emotion estimation results of each generative AI in real time and develops an algorithm to optimize the overall emotional balance. For example, it makes appropriate adjustments if the emotional balance is disrupted. The generative AI also integrates the emotion estimation results of each generative AI and provides feedback to maintain the overall emotional balance. For example, it suggests appropriate actions if the emotional balance becomes unbalanced. The generative AI also builds a system to optimize the overall emotional balance based on the emotion estimation results. For example, it makes real-time adjustments to maintain an optimal emotional balance. In this way, the emotion estimation results of each generative AI can be integrated to optimize the overall emotional balance.

[0080] The generation AI analyzes the skill sets of each generation AI and can automatically allocate tasks optimally. For example, the generation AI analyzes the skill sets of each generation AI and develops an algorithm that automatically allocates tasks optimally. For example, it assigns tasks according to each generation AI's area of ​​expertise. The generation AI also builds a system that optimizes task allocation based on the skill sets of each generation AI. For example, it dynamically adjusts tasks according to the progress of the project. The generation AI also provides feedback to ensure optimal task allocation based on the results of the skill set analysis. For example, it sets task priorities based on the skill sets. This makes it possible to analyze the skill sets of each generation AI and automatically allocate tasks optimally.

[0081] The Generative AI can monitor the performance of each Generative AI in real time and make adjustments as necessary. For example, the Generative AI can build a system that monitors the performance of each Generative AI in real time and makes adjustments as necessary. For example, it can send an alert if performance drops. The Generative AI can also develop an algorithm that makes optimal adjustments based on the performance data of each Generative AI. For example, it can identify the cause of performance decline and propose appropriate countermeasures. The Generative AI can also provide feedback to make adjustments in real time based on the performance monitoring results. For example, it can make adjustments to maintain optimal performance. This makes it possible to monitor the performance of each Generative AI in real time and make adjustments as necessary.

[0082] Generative AI can link generative AIs from different companies to improve the efficiency of an entire industry. For example, generative AI can link generative AIs from different companies to build a system that improves the efficiency of an entire industry. For example, it can optimize the entire supply chain. Generative AI can also develop algorithms that allow generative AIs from different companies to share information and provide optimal services to each other. For example, generative AIs from the manufacturing and logistics industries can work together to optimize inventory management. Generative AI can also build a system that integrates data from different companies and makes optimal decisions as a whole to improve the efficiency of an entire industry. For example, generative AIs from the energy and transportation industries can work together to optimize energy consumption. This allows generative AIs from different companies to work together and improve the efficiency of an entire industry.

[0083] Generative AI can improve the quality of education by linking the generative AIs of educational institutions. For example, generative AI can link the generative AIs of educational institutions to build a system that improves the quality of education. For example, each generative AI can share its educational resources and provide optimal learning plans. Generative AI can also develop algorithms that allow the generative AIs of educational institutions to share information and provide optimal educational services to each other. For example, generative AIs from different educational institutions can work together to provide the optimal learning plan for each student. Generative AI can also build a system that integrates data from educational institutions and makes optimal decisions as a whole to improve the quality of education. For example, it can provide feedback to improve the quality of education based on the data each generative AI has. This allows the generative AIs of educational institutions to work together to improve the quality of education.

[0084] The generative AI can use the emotion estimation function to optimize the cooperation of the generative AIs based on the user's emotions. For example, the generative AI uses the emotion estimation function to build a system that optimizes the cooperation of the generative AIs based on the user's emotions. For example, if the user is feeling stressed, the cooperation is optimized. The generative AI also develops an algorithm that dynamically adjusts the cooperation of the generative AIs based on the user's emotion data. For example, if the user is relaxed, the cooperation is strengthened. The generative AI also uses the emotion estimation function to build a system that adjusts cooperation according to the user's emotions. For example, if the user has positive emotions, the cooperation of the generative AIs is promoted. This makes it possible to optimize the cooperation of the generative AIs based on the user's emotions.

[0085] Generative AI can use its emotion estimation function to grasp the emotional state of an entire group and provide optimal services. For example, a group-oriented system using generative AI can build a system that grasps the emotional state of an entire group in real time. For example, it can analyze the emotional state of participants in a meeting and provide appropriate feedback. Generative AI can also use its emotion estimation function to develop an algorithm that provides optimal services based on the emotional state of the entire group. For example, it can suggest a leadership style depending on the emotional state. Generative AI can also build a feedback system to grasp the emotional state of an entire group and provide optimal services. For example, it can suggest team building activities depending on the emotional state. This makes it possible to grasp the emotional state of an entire group and provide optimal services.

[0086] The generation AI can analyze the performance of each generation AI and allocate resources optimally. For example, the generation AI can analyze the performance of each generation AI in real time and build a system that allocates resources optimally. For example, it can concentrate resources on generation AIs with high performance. The generation AI can also develop an algorithm that optimizes resource allocation based on the performance data of each generation AI. For example, it can identify the cause of performance decline and propose appropriate countermeasures. The generation AI can also provide feedback to allocate resources optimally based on the performance analysis results. For example, it can make adjustments to maintain optimal performance. This makes it possible to analyze the performance of each generation AI and allocate resources optimally.

[0087] A generative AI can strengthen cooperation among generative AIs and maximize the efficiency of the entire group. For example, a generative AI can strengthen cooperation among generative AIs and build a system that maximizes the efficiency of the entire group. For example, each generative AI can share information it has in real time to make optimal decisions. A generative AI can also develop algorithms to strengthen cooperation among generative AIs and maximize the efficiency of the entire group. For example, each generative AI can assign tasks based on its area of ​​expertise, improving overall efficiency. A generative AI can also build a feedback system to strengthen cooperation among generative AIs and maximize the efficiency of the entire group. For example, it can monitor the effectiveness of cooperation in real time and make adjustments as necessary. This can strengthen cooperation among generative AIs and maximize the efficiency of the entire group.

[0088] Generative AI can link generative AIs in different regions and promote inter-regional cooperation. For example, generative AI can link generative AIs in different regions and build a system that promotes inter-regional cooperation. For example, generative AIs in different regions share information and provide optimal services. Generative AI can also develop algorithms that allow generative AIs in different regions to link together and promote inter-regional cooperation. For example, by providing services tailored to the needs of each region. Generative AI can also integrate data from generative AIs in different regions and build a system that makes optimal decisions as a whole to promote inter-regional cooperation. For example, by optimally allocating resources to each region. This allows generative AIs in different regions to link together and promote inter-regional cooperation.

[0089] Generative AI can link generative AIs from different industries to create synergy between industries. For example, generative AI can link generative AIs from different industries to build a system that creates synergy between industries. For example, generative AIs from the medical and logistics industries can work together to achieve efficient delivery of pharmaceuticals. Generative AI can also develop algorithms that allow generative AIs from different industries to work together to create synergy between industries. For example, generative AIs from the financial and retail industries can work together to predict customer purchasing behavior. Generative AI can also create synergy between industries by integrating data from generative AIs from different industries to build a system that makes optimal decisions as a whole. For example, generative AIs from the energy and transportation industries can work together to optimize energy consumption. This allows generative AIs from different industries to work together and create synergy between industries.

[0090] Generative AI can use its emotion estimation function to customize services based on the user's emotions. For example, generative AI uses its emotion estimation function to build a system that customizes services based on the user's emotions. For example, if the user is feeling stressed, it can provide a service that helps them relax. Generative AI can also develop an algorithm that dynamically adjusts service content based on the user's emotion data. For example, if the user is feeling positive, it can suggest entertainment content. Generative AI can also use its emotion estimation function to build a system that customizes services according to the user's emotions. For example, if the user is tired, it can suggest a refreshing activity. This makes it possible to customize services based on the user's emotions.

[0091] The generative AI can use its emotion estimation function to provide content that will increase a user's motivation to learn. For example, the generative AI uses its emotion estimation function to build a system that provides content that will increase a user's motivation to learn. For example, it suggests learning content based on topics that interest the user. The generative AI also develops an algorithm that provides feedback to increase a user's motivation to learn based on the user's emotion data. For example, if a user has positive emotions, it suggests a more difficult task. The generative AI also uses its emotion estimation function to build a system that customizes content to increase a user's motivation to learn. For example, if a user is tired, it suggests a learning activity that will refresh them. This makes it possible to provide content that will increase a user's motivation to learn.

[0092] The generative AI can analyze the user's skill level and propose the optimal learning plan. For example, the generative AI can build a system that analyzes the user's skill level in real time and proposes the optimal learning plan. For example, it can provide learning content that matches the user's current skill level. The generative AI can also develop an algorithm that proposes the optimal learning plan based on the user's skill level data. For example, it can propose specific steps necessary to improve the user's skills. The generative AI can also build a system that customizes the learning plan for the user based on the results of the skill level analysis. For example, it can dynamically adjust the learning plan according to the user's progress. This makes it possible to analyze the user's skill level and propose the optimal learning plan.

[0093] A generative AI can collaborate with other generative AIs to jointly share knowledge and improve skills. A generative AI, for example, collaborates with other generative AIs to build a system that jointly shares knowledge and improves skills. For example, multiple generative AIs collaborate to provide optimal learning content to users. A generative AI also develops algorithms that share knowledge and improve skills through collaboration with other generative AIs. For example, each generative AI shares its specialized knowledge and optimizes learning plans for users. A generative AI also builds a system that strengthens collaboration with other generative AIs to share knowledge and improve skills. For example, data held by each generative AI is integrated to provide the optimal learning plan as a whole. This allows collaboration with other generative AIs to jointly share knowledge and improve skills.

[0094] Generative AI can collaborate with experts from different fields to improve cross-disciplinary knowledge. For example, generative AI could collaborate with experts from different fields to build a system that improves cross-disciplinary knowledge. For example, experts from the medical and engineering fields could collaborate to develop new technologies. Generative AI could also develop algorithms that improve cross-disciplinary knowledge through collaboration with experts from different fields. For example, it could integrate specialized knowledge from each field and provide new learning content. Generative AI could also build a system that strengthens collaboration with experts from different fields to improve cross-disciplinary knowledge. For example, it could integrate data from each field and provide the optimal learning plan as a whole. This would enable collaboration with experts from different fields to improve cross-disciplinary knowledge.

[0095] Generative AI can work with educational institutions to improve the quality of educational programs. For example, generative AI could work with educational institutions to build a system that improves the quality of educational programs. For example, it could share the educational resources held by each educational institution and provide optimal learning plans. Generative AI could also develop algorithms that improve the quality of educational programs through collaboration with educational institutions. For example, generative AIs from different educational institutions could work together to provide optimal learning plans for each student. Generative AI could also build a system that integrates data from educational institutions and makes optimal decisions as a whole to improve the quality of educational programs. For example, it could provide feedback to improve the quality of education based on the data held by each generative AI. This would allow collaboration with educational institutions to improve the quality of educational programs.

[0096] The generative AI can use the emotion estimation function to adjust the study plan based on the user's emotions. For example, the generative AI uses the emotion estimation function to build a system that adjusts the study plan based on the user's emotions. For example, if the user is feeling stressed, the generative AI can suggest relaxing study activities. The generative AI can also develop an algorithm that dynamically adjusts the study plan based on the user's emotional data. For example, if the user is feeling positive, the generative AI can suggest more difficult tasks. The generative AI can also use the emotion estimation function to build a system that adjusts the study plan according to the user's emotions. For example, if the user is tired, the system can suggest refreshing study activities. This makes it possible to adjust the study plan based on the user's emotions.

[0097] Generative AI can use its emotion estimation function to propose new business models based on user emotions. For example, generative AI uses its emotion estimation function to build a system that proposes new business models based on user emotions. For example, if a user has positive emotions, it will propose new services or products. Generative AI can also develop algorithms that propose new business models based on user emotion data. For example, it can propose marketing strategies based on the user's emotional state. Generative AI can also use its emotion estimation function to build a system that proposes business models based on the user's emotions. For example, if a user is feeling stressed, it can propose services that will help them relax. This makes it possible to propose new business models based on the user's emotions.

[0098] Generative AI can analyze data from different industries and discover new market opportunities. For example, generative AI can analyze data from different industries and build a system to discover new market opportunities. For example, it can integrate data from the medical and engineering fields to develop new technologies. Generative AI can also develop algorithms to discover new market opportunities based on data from different industries. For example, it can analyze data from each industry and propose new business models. Generative AI can also integrate data from different industries to discover new market opportunities and build a system to make optimal decisions as a whole. For example, it can integrate data from the energy and transportation industries to discover new market opportunities. This makes it possible to analyze data from different industries and discover new market opportunities.

[0099] Generative AI can collaborate with other generative AIs to jointly advance projects that create new value. For example, generative AI can collaborate with other generative AIs to build a system that jointly advances projects that create new value. For example, multiple generative AIs collaborate to develop new products and services. Generative AI can also develop algorithms that advance projects that create new value through collaboration with other generative AIs. For example, each generative AI can share its specialized knowledge and propose new business models. Generative AI can also build a system that strengthens collaboration with other generative AIs to create new value. For example, data held by each generative AI can be integrated to advance the optimal project as a whole. This allows generative AIs to collaborate with other generative AIs to jointly advance projects that create new value.

[0100] A generative AI can work with other generative AIs in different regions to provide new services tailored to the needs of each region. For example, a generative AI can work with other generative AIs in different regions to build a system that provides new services tailored to the needs of each region. For example, it can provide services tailored to the characteristics of each region. Furthermore, a generative AI can work with other generative AIs in different regions to develop algorithms that provide new services tailored to the needs of each region. For example, it can analyze data from each region and propose new business models. Furthermore, in order to provide new services tailored to the needs of each region, a generative AI can integrate data from different regions and build a system that makes optimal decisions as a whole. For example, it can optimally allocate resources to each region. This allows a generative AI in different regions to work together to provide new services tailored to the needs of each region.

[0101] Generative AI can propose optimal measures to address environmental issues and support the realization of a sustainable society. For example, generative AI can propose optimal measures to address environmental issues and build systems to support the realization of a sustainable society. For example, it can optimize energy consumption. Generative AI can also develop algorithms to propose optimal measures to address environmental issues and propose specific actions for environmental protection. For example, it can promote the use of renewable energy. Generative AI can also analyze environmental data and build systems to propose optimal measures to support the realization of a sustainable society. For example, it can perform resource management to minimize environmental impact. This can propose optimal measures to address environmental issues and support the realization of a sustainable society.

[0102] Generative AI can use the emotion estimation function to generate new ideas for value creation based on the user's emotions. For example, generative AI uses the emotion estimation function to build a system that generates new ideas for value creation based on the user's emotions. For example, if the user has positive emotions, it will suggest new services or products. Generative AI can also develop an algorithm that generates new ideas for value creation based on the user's emotional data. For example, it can suggest marketing strategies based on the user's emotional state. Generative AI can also use the emotion estimation function to build a system that generates ideas for value creation based on the user's emotions. For example, if the user is feeling stressed, it will suggest services that will help them relax. This makes it possible to generate new ideas for value creation based on the user's emotions.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] Generative AI can monitor a user's health status and provide advice and services according to the health status. For example, generative AI can monitor a user's health data (heart rate, sleep patterns, etc.) and provide advice according to the health status. For example, if the user is not getting enough exercise, it can suggest an appropriate exercise plan. Generative AI can also analyze a user's health status in real time and provide health management services. For example, it can check the nutritional balance of meals and suggest areas for improvement. Generative AI can also send reminders and notifications to the user based on the health status. For example, it can send notifications encouraging regular health checks and medication. This allows it to provide appropriate advice and services according to the user's health status.

[0105] Generative AI can learn a user's long-term goals and suggest steps toward achieving them. For example, generative AI can learn a user's long-term goals (career, health, hobbies, etc.) and suggest specific steps toward achieving them. For example, it can provide a skill acquisition plan for career advancement. Generative AI can also monitor the user's progress toward achieving their goals and provide appropriate feedback and advice. For example, it can send messages to maintain motivation to achieve their goals. Generative AI can also support the user's daily life based on their long-term goals. For example, it can suggest diet and exercise based on health goals. This can support the user in achieving their long-term goals.

[0106] Generative AI can work with multiple devices in the home, learning the preferences and behavioral patterns of each family member to provide the optimal home environment. For example, generative AI can work with smart devices in the home (lighting, air conditioners, televisions, etc.) to learn the preferences and behavioral patterns of each family member and provide optimal settings. For example, it can automatically adjust the lighting based on the time each family member returns home. Generative AI can also analyze the behavioral patterns of each family member and optimally control home devices. For example, it can adjust the air conditioner temperature based on the time the family gathers in the living room. Generative AI can also learn the preferences of each family member and suggest entertainment content. For example, it can recommend movies and music that the whole family can enjoy. In this way, generative AI can work with home devices to provide the optimal home environment.

[0107] The generative AI can work with the generative AI of a colleague at work to make suggestions to improve the productivity of the entire team. For example, the generative AI can work with the generative AI of a colleague at work to optimize the schedule for the entire team. For example, it can adjust meeting schedules and suggest times that are convenient for everyone to attend. The generative AI can also support task management for the entire team to improve productivity. For example, it can share task progress in real time and send alerts if delays occur. The generative AI can also work with the generative AI of a colleague at work to smooth communication across the entire team. For example, it can automatically share important messages and information so that everyone can keep up to date with the latest information. This makes it possible to work with the generative AI of a colleague at work to make suggestions to improve the productivity of the entire team.

[0108] The generative AI can use its emotion estimation function to recommend entertainment content based on the user's emotions. For example, the generative AI analyzes the user's emotions in real time and recommends entertainment content based on those emotions. For example, if the user feels like relaxing, it will suggest relaxing music or movies. The generative AI also uses its emotion estimation function to recommend games or activities based on the user's emotions. For example, if the user is feeling stressed, it will suggest games that will help relieve stress. The generative AI also automatically generates a playlist of entertainment content based on the user's emotion data. For example, if the user is happy, it will create a music playlist to maintain a positive mood. This allows it to recommend entertainment content based on the user's emotions.

[0109] Generative AI can develop algorithms that automatically share information with other generative AIs and work together to make optimal decisions. For example, generative AIs can develop protocols that automatically share information with other generative AIs and work together to make optimal decisions. For example, multiple generative AIs can work together to share project progress in real time. Generative AIs can also develop algorithms that work together to provide optimal services through information sharing with other generative AIs. For example, multiple generative AIs can work together to provide services that meet user needs. Generative AIs can also build information sharing systems that work with other generative AIs to make optimal decisions. For example, data held by each generative AI can be integrated to make optimal decisions as a whole. This allows them to share information with other generative AIs and work together to make optimal decisions.

[0110] A generative AI can analyze a user's behavioral history and predict when collaboration with other generative AIs will be necessary. For example, a generative AI may analyze a user's behavioral history and develop an algorithm to predict when collaboration with other generative AIs will be necessary. For example, it may predict when a user will need the support of other generative AIs when performing a specific task. A generative AI may also build a system that optimizes collaboration with other generative AIs based on the user's behavioral history. For example, when a user uses multiple devices, generative AIs collaborate to provide optimal services. A generative AI may also analyze behavioral history and predict in real time when collaboration with other generative AIs will be necessary. For example, it may prepare the necessary resources before the user starts a specific activity. This allows it to predict when collaboration with other generative AIs will be necessary based on the user's behavioral history.

[0111] A generative AI can use the emotion estimation function to understand the emotional state of other generative AIs and build cooperative relationships. For example, a generative AI can use the emotion estimation function to develop an algorithm that understands the emotional state of other generative AIs and build cooperative relationships. For example, it can provide support when another generative AI is feeling stressed. A generative AI can also use the emotion estimation function to build a system that facilitates communication with other generative AIs. For example, it can provide appropriate feedback according to the emotional state. A generative AI can also analyze the emotional state of other generative AIs in real time and suggest actions to build cooperative relationships. For example, it can allocate tasks according to the emotional state. This allows a generative AI to understand the emotional state of other generative AIs and build cooperative relationships.

[0112] Generative AI can work with other generative AIs in different industries to achieve cross-industry optimization. For example, generative AI can work with other generative AIs in different industries to build a system that achieves cross-industry optimization. For example, generative AIs from the medical and logistics industries can work together to achieve efficient delivery of pharmaceuticals. Generative AIs from different industries can also share information and develop algorithms that provide optimal services to each other. For example, generative AIs from the financial and retail industries can work together to predict customer purchasing behavior. Generative AI can also integrate data from different industries to achieve cross-industry optimization and build a system that makes optimal decisions as a whole. For example, generative AIs from the energy and transportation industries can work together to optimize energy consumption. This allows generative AIs from different industries to work together to achieve cross-industry optimization.

[0113] Generative AI can work in collaboration with public service generative AI to improve services throughout the region. For example, generative AI can work in collaboration with public service generative AI to build a system that aims to improve services throughout the region. For example, it can work in collaboration with a traffic management system to alleviate traffic congestion. Generative AI can also share information with public service generative AI to develop algorithms that provide optimal services throughout the region. For example, it can work in collaboration with medical services to speed up emergency response. Generative AI can also build a system that integrates public service data and makes optimal decisions, aiming to improve services throughout the region. For example, it can work in collaboration with education services to improve the local educational environment. In this way, it can work in collaboration with public service generative AI to improve services throughout the region.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The generative AI learns the preferences and behavioral patterns of each individual user. For example, the generative AI can learn a user's purchasing history and predict their next purchase. It can also learn a user's browsing history and recommend related content. It can also learn a user's frequency of use and provide optimal services. Step 2: The information sharing unit shares the information held by the generating AI. For example, the information sharing unit shares the user preferences and behavioral patterns learned by the generating AI with other generating AIs. The information sharing unit can also store the data collected by the generating AI in a central database so that other generating AIs can access it. Furthermore, the generating AI can share its skills and knowledge, allowing the collaboration unit to provide optimal services. Step 3: The Collaboration Department provides optimal services based on the shared information. For example, when multiple generative AIs work together on a project, they share roles in the areas in which they excel, improving overall efficiency. Also, when multiple generative AIs work together within a company to streamline operations, they share the information they have and make optimal decisions, improving work efficiency. Furthermore, in educational settings, multiple generative AIs work together to provide optimal learning plans for each student, improving learning effectiveness.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0183] 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. Generative AI that learns individual user preferences and behavioral patterns, an information sharing unit that shares information held by the generation AI; a cooperation unit that provides optimal services based on the information shared by the information sharing unit. A system characterized by:

2. The generated AI is The user's emotions are estimated in real time, and the service content is dynamically changed based on the emotions.

2. The system of claim 1.

3. The generated AI is Monitor the health condition of the user and provide advice and services according to the health condition.

2. The system of claim 1.

4. The generated AI is Learn the user's long-term goals and suggest steps toward achieving those goals 2. The system of claim 1.

5. The generated AI is By linking multiple devices in the home and learning the preferences and behavioral patterns of all family members, the system provides an optimal home environment.

2. The system of claim 1.

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