System

Abstract

An object of the system according to the embodiment is to reproduce characteristics and actions of a real human in a virtual world and create a future invention product.SOLUTION: A system according to an embodiment includes a digital twin technology, a virtual-world constructing unit, and a generation / AI unit. The digital twin technology reproduces characteristics and actions of a real human in a virtual world. The virtual world constructing unit constructs a virtual world reproduced by a digital twin technology. The generation AI section creates a future invention in the virtual reality constructed by the virtual reality construction section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to adequately reproduce the characteristics and behavior of real people in the virtual world and create future inventions.

[0005] The system according to the embodiment aims to reproduce the characteristics and behavior of real people in a virtual world and create inventions of the future. [Means for solving the problem]

[0006] The system according to the embodiment includes digital twin technology, a virtual world construction unit, and a generation AI unit. The digital twin technology reproduces the characteristics and behavior of real people in a virtual world. The virtual world construction unit constructs a virtual world reproduced by the digital twin technology. The generation AI unit creates future inventions within the virtual world constructed by the virtual world construction unit. [Effects of the Invention]

[0007] The system according to the embodiment can reproduce the characteristics and behavior of real people in the virtual world and create future inventions. [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 future invention import system according to an embodiment of the present invention is a system that combines digital twin technology and generative AI to operate a large number of human-like AIs in a virtual world recreated on a computer. As a result, the future invention import system can efficiently create future inventions and import them into the real world by utilizing digital twin technology and generative AI.

[0029] A future invention import system according to an embodiment includes digital twin technology, a virtual world construction unit, and a generation AI unit. Digital twin technology reproduces the characteristics and behaviors of real people in a virtual world. For example, it collects individual human physical characteristics, behavioral patterns, thought processes, and other data as digital data and uses that data to build a human-like AI within the virtual world. The virtual world construction unit builds a virtual world reproduced using digital twin technology. For example, it reproduces cities, natural environments, and social infrastructure within the virtual world. The generation AI unit creates future inventions within the virtual world constructed by the virtual world construction unit. For example, the generation AI generates ideas and blueprints for future inventions based on activity data of the digital twin within the virtual world and prompts containing instructions for future inventions. This allows the future invention import system to combine digital twin technology and generation AI to create future inventions and import them into the real world.

[0030] Digital twin technology collects the physical characteristics, behavioral patterns, and thought processes of individual humans as digital data, and based on that data, can build AI that mimics humans within a virtual world. Digital twin technology, for example, analyzes the behavioral patterns of digital twins to create detailed models of decision-making processes under specific circumstances. For example, it can simulate evacuation behavior in an emergency or purchasing behavior in everyday life. Digital twin technology can also simulate driving behavior in traffic jams or evacuation behavior during a disaster. Digital twin technology can also simulate work performance in the workplace and the division of household chores within the home. This makes it possible to create virtual worlds that are closer to reality by reproducing the characteristics of individual humans in detail.

[0031] Digital twin technology can collect physiological data in real time, making reactions in virtual worlds more realistic. Digital twin technology can, for example, collect physiological data (heart rate, brain waves, etc.) of digital twins in real time, making reactions in virtual worlds more realistic. For example, it can simulate heart rate fluctuations under stressful situations. Digital twin technology can also simulate fluctuations in heart rate and breathing rate during exercise. Digital twin technology can also simulate brain wave fluctuations during sleep and reaction speeds when awake. In this way, by collecting physiological data in real time, reactions in virtual worlds can be made more realistic.

[0032] Digital twin technology can reproduce the characteristics of animals and plants in a virtual world and simulate ecosystems. Digital twin technology can, for example, reproduce the characteristics of animals and plants in a virtual world and simulate ecosystems. For example, it can analyze the interactions between animals and plants in a forest ecosystem. Digital twin technology can also analyze the interactions between fish and coral in a marine ecosystem. Digital twin technology can also analyze the interactions between birds and insects in an urban ecosystem. In this way, ecosystems can be simulated by reproducing the characteristics of animals and plants in a virtual world.

[0033] Digital twin technology can be applied to the field of education to verify the effectiveness of learning in a virtual world. Digital twin technology can be applied to the field of education to verify the effectiveness of learning in a virtual world. For example, participating in a class in a virtual classroom or conducting experimental simulations. Digital twin technology can also recreate historical events in a virtual world, allowing students to learn as if they were actually there. Digital twin technology can also be used to conduct scientific experiments in a virtual laboratory or geography studies in a virtual field. This makes it possible to verify the effectiveness of learning in a virtual world by applying it to the field of education.

[0034] Virtual worlds can set physical laws in more detail than the real world, allowing them to respond to even the most minute changes in the environment. Virtual worlds can, for example, set physical laws in more detail than the real world, allowing them to respond to even the most minute changes in the environment. For example, they can precisely simulate wind, water flow, and temperature changes. Virtual worlds can also precisely simulate gravity, friction, and electromagnetic forces. Virtual worlds can also precisely simulate chemical reactions and the growth processes of living organisms. In this way, by setting physical laws in more detail, it is possible to build virtual worlds that can respond to even the most minute changes in the environment.

[0035] Virtual worlds can introduce systems that allow for dynamic changes to social rules, allowing the digital twin's reaction to be observed in different scenarios. Virtual worlds can introduce systems that allow for dynamic changes to social rules, allowing the digital twin's reaction to be observed in different scenarios. For example, changes to laws and regulations can be simulated. Virtual worlds can also simulate changes to economic policies and tax systems. Virtual worlds can also simulate changes to the education system and healthcare system. This allows the digital twin's reaction to be observed in different scenarios by dynamically changing social rules.

[0036] Virtual worlds can be built with scenarios that have different cultural spheres or historical backgrounds, and the behavior of digital twins can be compared. Virtual worlds can be built with scenarios that have different cultural spheres or historical backgrounds, and the behavior of digital twins can be compared. For example, scenarios of ancient Rome or medieval Europe can be recreated. Virtual worlds can also recreate scenarios of Edo-period Japan or modern-day America. Virtual worlds can also recreate scenarios of futuristic cities or alien colonies. This makes it possible to compare the behavior of digital twins in scenarios with different cultural spheres or historical backgrounds.

[0037] Virtual worlds can be set up to set different climate conditions and verify the adaptability of digital twins. For example, virtual worlds can be set up to set different climate conditions and verify the adaptability of digital twins. For example, they can simulate behavior in extremely cold environments or desert environments. Virtual worlds can also simulate behavior in tropical rainforests or high mountain areas. Virtual worlds can also simulate the urban heat island effect or climate change in rural areas. This makes it possible to verify the adaptability of digital twins by setting different climate conditions.

[0038] Generative AI can reflect feedback from digital twins in real time on invention ideas and make improvements. Generative AI can, for example, reflect feedback from digital twins in real time on invention ideas and make improvements. For example, it can suggest ways to make the user interface easier to use or to add new functions. Generative AI can also make suggestions regarding product design and material selection. Generative AI can also make suggestions regarding energy efficiency and environmental impact. This allows inventions to be improved quickly by reflecting feedback from digital twins in real time.

[0039] Generative AI can input activity data of digital twins in different virtual world scenarios and create a variety of inventions. For example, generative AI can input activity data of digital twins in different virtual world scenarios and create a variety of inventions. For example, it can generate inventions based on activity data in urban environments or natural environments. Generative AI can also generate inventions based on activity data in futuristic cities or alien colonies. Generative AI can also generate inventions based on activity data in scenarios with different cultural spheres and historical backgrounds. This makes it possible to create a variety of inventions based on activity data in different virtual world scenarios.

[0040] Generative AI can create new entertainment content from activity data of digital twins in virtual worlds. For example, generative AI can develop games based on adventures and missions in virtual worlds. Generative AI can also produce dramas and movies based on life and interactions in virtual worlds. Generative AI can also provide interactive experiences based on events and festivals in virtual worlds. This allows new entertainment content to be created based on activity data of digital twins.

[0041] Generative AI can be applied to the medical field to suggest new treatments based on the health data of a digital twin in a virtual world. Generative AI can be applied to the medical field to suggest new treatments based on the health data of a digital twin in a virtual world. For example, it can develop treatments for personalized medicine and preventive medicine. Generative AI can also suggest rehabilitation and fitness programs. Generative AI can also develop treatments for mental health care and stress management. This allows new treatments to be suggested based on the health data of a digital twin.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The future invention import system can further include a health management unit that monitors the user's health status. The health management unit collects the user's physiological data (heart rate, blood pressure, body temperature, etc.) in real time and evaluates their health status. For example, the health management unit can analyze the user's stress level and suggest appropriate relaxation methods. The health management unit can also detect lack of exercise or overwork and recommend appropriate exercise programs or rest. Furthermore, the health management unit can evaluate the nutritional balance of meals and provide healthy meal plans. This allows for comprehensive management of the user's health status and supports health maintenance.

[0044] The future invention import system may further include an interest analysis unit that analyzes the user's hobbies and interests. The interest analysis unit analyzes the user's behavioral data and search history to identify the user's hobbies and interests. For example, the interest analysis unit may estimate the user's hobbies based on the websites the user frequently visits and the products the user purchases. The interest analysis unit may also identify the user's interests based on the events the user participates in and the social media accounts the user follows. Furthermore, the interest analysis unit may suggest related products and services based on the user's hobbies and interests. This allows for personalized suggestions based on the user's hobbies and interests.

[0045] The future invention import system may further include a sleep management unit that monitors the user's sleep state. The sleep management unit collects the user's sleep data (sleep time, sleep quality, number of turns, etc.) in real time and evaluates the sleep state. For example, the sleep management unit may analyze the user's sleep quality and provide advice for improvement. The sleep management unit may also analyze the user's sleep patterns and suggest an optimal sleep schedule. Furthermore, the sleep management unit may adjust the user's sleep environment (temperature, humidity, lighting, etc.) to support comfortable sleep. This allows for comprehensive management of the user's sleep state and provides high-quality sleep.

[0046] The future invention import system can further include a purchase analysis unit that analyzes the user's purchasing history. The purchase analysis unit analyzes the user's purchasing history and identifies purchasing trends. For example, the purchase analysis unit can estimate the user's purchasing trends based on the products and services the user frequently purchases. The purchase analysis unit can also suggest related products and services based on the user's purchasing history. Furthermore, the purchase analysis unit can predict demand for specific products based on the user's purchasing history. This allows for personalized suggestions to be made according to the user's purchasing trends.

[0047] The future invention import system can further include an exercise analysis unit that analyzes the user's exercise data. The exercise analysis unit collects the user's exercise data (number of steps, calories burned, exercise time, etc.) in real time and evaluates the user's exercise status. For example, the exercise analysis unit can analyze the user's exercise patterns and suggest an appropriate exercise program. The exercise analysis unit can also evaluate the effectiveness of exercise based on the user's exercise data and suggest areas for improvement. Furthermore, the exercise analysis unit can provide advice for maintaining health based on the user's exercise data. This allows for comprehensive management of the user's exercise status and supports a healthy lifestyle.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: Digital twin technology replicates the characteristics and behavior of real people in a virtual world. For example, it collects individual human physical characteristics, behavioral patterns, thought processes, and other data as digital data, and uses this data to build a human-like AI in the virtual world. Step 2: The Virtual World Construction Department will build a virtual world that is recreated using digital twin technology. For example, cities, natural environments, and social infrastructure will be recreated within the virtual world. Step 3: The generative AI unit creates future inventions within the virtual world constructed by the virtual world construction unit. For example, the generative AI generates ideas and blueprints for future inventions based on the digital twin's activity data within the virtual world and prompts containing instructions for future inventions.

[0050] (Example 2) The future invention import system according to an embodiment of the present invention is a system that combines digital twin technology and generative AI to operate a large number of human-like AIs in a virtual world recreated on a computer. As a result, the future invention import system can efficiently create future inventions and import them into the real world by utilizing digital twin technology and generative AI.

[0051] A future invention import system according to an embodiment includes digital twin technology, a virtual world construction unit, and a generation AI unit. Digital twin technology reproduces the characteristics and behaviors of real people in a virtual world. For example, it collects individual human physical characteristics, behavioral patterns, thought processes, and other data as digital data and uses that data to build a human-like AI within the virtual world. The virtual world construction unit builds a virtual world reproduced using digital twin technology. For example, it reproduces cities, natural environments, and social infrastructure within the virtual world. The generation AI unit creates future inventions within the virtual world constructed by the virtual world construction unit. For example, the generation AI generates ideas and blueprints for future inventions based on activity data of the digital twin within the virtual world and prompts containing instructions for future inventions. This allows the future invention import system to combine digital twin technology and generation AI to create future inventions and import them into the real world.

[0052] Digital twin technology collects the physical characteristics, behavioral patterns, and thought processes of individual humans as digital data, and based on that data, can build AI that mimics humans within a virtual world. Digital twin technology, for example, analyzes the behavioral patterns of digital twins to create detailed models of decision-making processes under specific circumstances. For example, it can simulate evacuation behavior in an emergency or purchasing behavior in everyday life. Digital twin technology can also simulate driving behavior in traffic jams or evacuation behavior during a disaster. Digital twin technology can also simulate work performance in the workplace and the division of household chores within the home. This makes it possible to create virtual worlds that are closer to reality by reproducing the characteristics of individual humans in detail.

[0053] Digital twin technology can collect physiological data in real time, making reactions in virtual worlds more realistic. Digital twin technology can, for example, collect physiological data (heart rate, brain waves, etc.) of digital twins in real time, making reactions in virtual worlds more realistic. For example, it can simulate heart rate fluctuations under stressful situations. Digital twin technology can also simulate fluctuations in heart rate and breathing rate during exercise. Digital twin technology can also simulate brain wave fluctuations during sleep and reaction speeds when awake. In this way, by collecting physiological data in real time, reactions in virtual worlds can be made more realistic.

[0054] Digital twin technology can use emotion estimation functions to estimate the emotional state of a digital twin in real time and simulate behavior based on those emotions. Digital twin technology can, for example, use emotion estimation functions to estimate the emotional state of a digital twin in real time and simulate behavior based on those emotions. For example, it can analyze the impact that emotions such as joy and sadness have on behavior. Digital twin technology can also analyze the impact that emotions such as anger and fear have on decision-making. Digital twin technology can also analyze the impact that states of stress and relaxation have on performance. As a result, the emotion estimation function can be used to simulate behavior based on the emotional state of a digital twin.

[0055] Digital twin technology can reproduce the characteristics of animals and plants in a virtual world and simulate ecosystems. Digital twin technology can, for example, reproduce the characteristics of animals and plants in a virtual world and simulate ecosystems. For example, it can analyze the interactions between animals and plants in a forest ecosystem. Digital twin technology can also analyze the interactions between fish and coral in a marine ecosystem. Digital twin technology can also analyze the interactions between birds and insects in an urban ecosystem. In this way, ecosystems can be simulated by reproducing the characteristics of animals and plants in a virtual world.

[0056] Digital twin technology can be applied to the field of education to verify the effectiveness of learning in a virtual world. Digital twin technology can be applied to the field of education to verify the effectiveness of learning in a virtual world. For example, participating in a class in a virtual classroom or conducting experimental simulations. Digital twin technology can also recreate historical events in a virtual world, allowing students to learn as if they were actually there. Digital twin technology can also be used to conduct scientific experiments in a virtual laboratory or geography studies in a virtual field. This makes it possible to verify the effectiveness of learning in a virtual world by applying it to the field of education.

[0057] Digital twin technology can use emotion estimation functions to simulate social interactions within virtual worlds based on the emotional state of a digital twin. Digital twin technology can, for example, use emotion estimation functions to simulate social interactions within virtual worlds based on the emotional state of a digital twin. For example, it can analyze communication at work or interactions with friends. Digital twin technology can also analyze group activities at school or parent-child relationships within the home. Digital twin technology can also analyze interactions in online communities and human relationships at events. In this way, the emotion estimation function can be used to simulate social interactions within virtual worlds.

[0058] Virtual worlds can set physical laws in more detail than the real world, allowing them to respond to even the most minute changes in the environment. Virtual worlds can, for example, set physical laws in more detail than the real world, allowing them to respond to even the most minute changes in the environment. For example, they can precisely simulate wind, water flow, and temperature changes. Virtual worlds can also precisely simulate gravity, friction, and electromagnetic forces. Virtual worlds can also precisely simulate chemical reactions and the growth processes of living organisms. In this way, by setting physical laws in more detail, it is possible to build virtual worlds that can respond to even the most minute changes in the environment.

[0059] Virtual worlds can introduce systems that allow for dynamic changes to social rules, allowing the digital twin's reaction to be observed in different scenarios. Virtual worlds can introduce systems that allow for dynamic changes to social rules, allowing the digital twin's reaction to be observed in different scenarios. For example, changes to laws and regulations can be simulated. Virtual worlds can also simulate changes to economic policies and tax systems. Virtual worlds can also simulate changes to the education system and healthcare system. This allows the digital twin's reaction to be observed in different scenarios by dynamically changing social rules.

[0060] The emotion estimation function can analyze in real time how environmental changes in the virtual world affect the emotions of a digital twin. For example, the emotion estimation function analyzes in real time how environmental changes in the virtual world affect the emotions of a digital twin. For example, it analyzes the impact of changes in weather and the changing of seasons on emotions. The emotion estimation function can also analyze the impact of urban development and natural disasters on emotions. The emotion estimation function can also analyze the impact of social events and policy changes on emotions. This makes it possible to analyze in real time how environmental changes affect the emotions of a digital twin.

[0061] Virtual worlds can be built with scenarios that have different cultural spheres or historical backgrounds, and the behavior of digital twins can be compared. Virtual worlds can be built with scenarios that have different cultural spheres or historical backgrounds, and the behavior of digital twins can be compared. For example, scenarios of ancient Rome or medieval Europe can be recreated. Virtual worlds can also recreate scenarios of Edo-period Japan or modern-day America. Virtual worlds can also recreate scenarios of futuristic cities or alien colonies. This makes it possible to compare the behavior of digital twins in scenarios with different cultural spheres or historical backgrounds.

[0062] Virtual worlds can be set up to set different climate conditions and verify the adaptability of digital twins. For example, virtual worlds can be set up to set different climate conditions and verify the adaptability of digital twins. For example, they can simulate behavior in extremely cold environments or desert environments. Virtual worlds can also simulate behavior in tropical rainforests or high mountain areas. Virtual worlds can also simulate the urban heat island effect or climate change in rural areas. This makes it possible to verify the adaptability of digital twins by setting different climate conditions.

[0063] The emotion estimation function can perform stress tests based on the emotional changes of a digital twin in a virtual world. The emotion estimation function can perform stress tests based on the emotional changes of a digital twin in a virtual world. For example, it can analyze stress responses in crowded urban environments or isolated environments. The emotion estimation function can also analyze the impact of overwork at work or troubles at home on stress. The emotion estimation function can also analyze the impact of natural disasters or social unrest on emotions. This makes it possible to analyze the stress responses of a digital twin by performing stress tests based on emotional changes.

[0064] Generative AI can reflect feedback from digital twins in real time on invention ideas and make improvements. Generative AI can, for example, reflect feedback from digital twins in real time on invention ideas and make improvements. For example, it can suggest ways to make the user interface easier to use or to add new functions. Generative AI can also make suggestions regarding product design and material selection. Generative AI can also make suggestions regarding energy efficiency and environmental impact. This allows inventions to be improved quickly by reflecting feedback from digital twins in real time.

[0065] Generative AI can input activity data of digital twins in different virtual world scenarios and create a variety of inventions. For example, generative AI can input activity data of digital twins in different virtual world scenarios and create a variety of inventions. For example, it can generate inventions based on activity data in urban environments or natural environments. Generative AI can also generate inventions based on activity data in futuristic cities or alien colonies. Generative AI can also generate inventions based on activity data in scenarios with different cultural spheres and historical backgrounds. This makes it possible to create a variety of inventions based on activity data in different virtual world scenarios.

[0066] The emotion estimation function can generate inventions that are easy to empathize with emotionally based on the emotional state of a digital twin. The emotion estimation function can, for example, generate inventions that are easy to empathize with emotionally based on the emotional state of a digital twin. For example, it can suggest designs and functions that will elicit joy and surprise from the user. The emotion estimation function can also suggest products that promote stress reduction and relaxation. The emotion estimation function can also suggest products with stories that evoke emotion and empathy. In this way, by generating inventions that are easy to empathize with emotionally, it is possible to improve user satisfaction.

[0067] Generative AI can create new entertainment content from activity data of digital twins in virtual worlds. For example, generative AI can develop games based on adventures and missions in virtual worlds. Generative AI can also produce dramas and movies based on life and interactions in virtual worlds. Generative AI can also provide interactive experiences based on events and festivals in virtual worlds. This allows new entertainment content to be created based on activity data of digital twins.

[0068] Generative AI can be applied to the medical field to suggest new treatments based on the health data of a digital twin in a virtual world. Generative AI can be applied to the medical field to suggest new treatments based on the health data of a digital twin in a virtual world. For example, it can develop treatments for personalized medicine and preventive medicine. Generative AI can also suggest rehabilitation and fitness programs. Generative AI can also develop treatments for mental health care and stress management. This allows new treatments to be suggested based on the health data of a digital twin.

[0069] The emotion estimation function can create new services that provide emotional satisfaction to users based on the emotional state of a digital twin. The emotion estimation function can create new services that provide emotional satisfaction to users based on the emotional state of a digital twin. For example, it can provide services for relaxation and stress relief. The emotion estimation function can also provide entertainment services that evoke emotion and empathy. The emotion estimation function can also provide personalized services that elicit positive emotions. This makes it possible to create new services that provide emotional satisfaction to users.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The future invention import system can further include a health management unit that monitors the user's health status. The health management unit collects the user's physiological data (heart rate, blood pressure, body temperature, etc.) in real time and evaluates their health status. For example, the health management unit can analyze the user's stress level and suggest appropriate relaxation methods. The health management unit can also detect lack of exercise or overwork and recommend appropriate exercise programs or rest. Furthermore, the health management unit can evaluate the nutritional balance of meals and provide healthy meal plans. This allows for comprehensive management of the user's health status and supports health maintenance.

[0072] The future invention import system may further include a personalized advertisement serving unit based on the user's emotional state. The personalized advertisement serving unit estimates the user's emotional state and selects the most appropriate advertisement based on that emotion. For example, when the user is relaxed, advertisements for relaxation-related products and services may be served. Also, when the user is stressed, advertisements for products and services that help relieve stress may be served. Furthermore, when the user is excited, advertisements for entertainment-related products and services may be served. This allows personalized advertisements to be served according to the user's emotional state, thereby increasing the effectiveness of advertising.

[0073] The future invention import system may further include an interest analysis unit that analyzes the user's hobbies and interests. The interest analysis unit analyzes the user's behavioral data and search history to identify the user's hobbies and interests. For example, the interest analysis unit may estimate the user's hobbies based on the websites the user frequently visits and the products the user purchases. The interest analysis unit may also identify the user's interests based on the events the user participates in and the social media accounts the user follows. Furthermore, the interest analysis unit may suggest related products and services based on the user's hobbies and interests. This allows for personalized suggestions based on the user's hobbies and interests.

[0074] The future invention import system can further include a music recommendation unit based on the user's emotional state. The music recommendation unit estimates the user's emotional state and recommends optimal music based on that emotion. For example, when the user is relaxed, music with a relaxing effect can be recommended. Also, when the user is stressed, music that helps relieve stress can be recommended. Furthermore, when the user is excited, energetic music can be recommended. In this way, music that matches the user's emotional state can be recommended, improving the user's mood.

[0075] The future invention import system may further include a sleep management unit that monitors the user's sleep state. The sleep management unit collects the user's sleep data (sleep time, sleep quality, number of turns, etc.) in real time and evaluates the sleep state. For example, the sleep management unit may analyze the user's sleep quality and provide advice for improvement. The sleep management unit may also analyze the user's sleep patterns and suggest an optimal sleep schedule. Furthermore, the sleep management unit may adjust the user's sleep environment (temperature, humidity, lighting, etc.) to support comfortable sleep. This allows for comprehensive management of the user's sleep state and provides high-quality sleep.

[0076] The future invention import system can further include a feedback collection unit based on the user's emotional state. The feedback collection unit estimates the user's emotional state and collects feedback based on that emotion. For example, when the user is satisfied, positive feedback can be collected. When the user is dissatisfied, feedback regarding areas for improvement can be collected. Furthermore, when the user is excited, new ideas and suggestions can be collected. In this way, feedback according to the user's emotional state can be collected and used to improve services and products.

[0077] The future invention import system can further include a purchase analysis unit that analyzes the user's purchasing history. The purchase analysis unit analyzes the user's purchasing history and identifies purchasing trends. For example, the purchase analysis unit can estimate the user's purchasing trends based on the products and services the user frequently purchases. The purchase analysis unit can also suggest related products and services based on the user's purchasing history. Furthermore, the purchase analysis unit can predict demand for specific products based on the user's purchasing history. This allows for personalized suggestions to be made according to the user's purchasing trends.

[0078] The future invention import system can further include a stress management unit based on the user's emotional state. The stress management unit estimates the user's emotional state and performs stress management based on that emotion. For example, when the user is feeling stressed, it can suggest relaxation methods or activities for relieving stress. The stress management unit can also analyze the user's stress level and provide appropriate counseling and support. Furthermore, the stress management unit can provide advice for stress prevention based on the user's emotional state. This allows for comprehensive support for the user's stress management.

[0079] The future invention import system can further include an exercise analysis unit that analyzes the user's exercise data. The exercise analysis unit collects the user's exercise data (number of steps, calories burned, exercise time, etc.) in real time and evaluates the user's exercise status. For example, the exercise analysis unit can analyze the user's exercise patterns and suggest an appropriate exercise program. The exercise analysis unit can also evaluate the effectiveness of exercise based on the user's exercise data and suggest areas for improvement. Furthermore, the exercise analysis unit can provide advice for maintaining health based on the user's exercise data. This allows for comprehensive management of the user's exercise status and supports a healthy lifestyle.

[0080] The future invention import system can further include a personalized news provision unit based on the user's emotional state. The personalized news provision unit estimates the user's emotional state and selects the most appropriate news based on that emotion. For example, when the user is relaxed, it can provide relaxing news or positive news. When the user is stressed, it can provide news that helps relieve stress or entertainment news. Furthermore, when the user is excited, it can provide energetic news or sports news. This allows the user to receive personalized news according to their emotional state, increasing their receptivity to news.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: Digital twin technology replicates the characteristics and behavior of real people in a virtual world. For example, it collects individual human physical characteristics, behavioral patterns, thought processes, and other data as digital data, and uses this data to build a human-like AI in the virtual world. Step 2: The Virtual World Construction Department will build a virtual world that is recreated using digital twin technology. For example, cities, natural environments, and social infrastructure will be recreated within the virtual world. Step 3: The generative AI unit creates future inventions within the virtual world constructed by the virtual world construction unit. For example, the generative AI generates ideas and blueprints for future inventions based on the digital twin's activity data within the virtual world and prompts containing instructions for future inventions.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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 AI 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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 AI 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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 AI 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. Digital twin technology is used to reproduce the characteristics and behavior of real people in the virtual world, and a virtual world construction unit that constructs a virtual world reproduced by the digital twin technology; A generation AI unit that creates future inventions in the virtual world constructed by the virtual world construction unit. A system characterized by:

2. The digital twin technology is Collecting digital data on the physical characteristics, behavioral patterns, and thought processes of each individual person, and using that data to build a human-like AI within the virtual world.

2. The system of claim 1.

3. The digital twin technology is Collecting physiological data in real time to make reactions within the virtual world more realistic 2. The system of claim 1.

4. The digital twin technology is Estimate the emotional state of the digital twin in real time and simulate the behavior based on that emotion.

2. The system of claim 1.

5. The digital twin technology is The characteristics of animals and plants are reproduced in the virtual world to simulate an ecosystem.

2. The system of claim 1.

6. The digital twin technology is Applying this to the field of education, we will verify the learning effects within the virtual world.

2. The system of claim 1.

7. The digital twin technology is Simulating social interactions within the virtual world based on the emotional state of the digital twin 2. The system of claim 1.

8. The virtual world is The laws of physics are set in greater detail than in the real world, allowing the system to respond to even the slightest changes in the environment.

2. The system of claim 1.

Images