System

Abstract

An object of a system according to an embodiment is to analyze environmental data and efficiently perform environmental improvement.SOLUTION: A system according to an embodiment includes a search robot, a generation AI, and an environmental arrangement unit. The generation AI is mounted on the search robot. The generation AI analyzes the environmental information collected by the search robot. The environmental improvement unit performs environmental improvement based on the AI analyzed by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has not been effective in investigating and addressing environmental issues, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze environmental data and efficiently improve the environment. [Means for solving the problem]

[0006] The system according to the embodiment includes an exploration robot, a generation AI, and an environment maintenance unit. The exploration robot is equipped with the generation AI. The generation AI analyzes environmental data collected by the exploration robot. The environment maintenance unit performs environmental maintenance based on the data analyzed by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can analyze environmental data and efficiently improve the environment. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 environmental survey system according to an embodiment of the present invention is a system in which an exploration robot equipped with a generation AI collects and analyzes environmental data and performs environmental maintenance. As a result, the environmental survey system can maintain a habitable environment.

[0029] An environmental investigation system according to an embodiment includes an exploration robot, a generation AI, an environmental data collection unit, an environmental data analysis unit, and an environment maintenance unit. The exploration robot collects environmental data. For example, the exploration robot analyzes the atmospheric components of a planet and measures the concentrations of oxygen and carbon dioxide. The exploration robot can also conduct geological surveys and assess the risk of earthquakes and volcanic activity. The exploration robot can also collect water quality data and determine whether the water is safe for drinking. The generation AI analyzes the environmental data collected by the exploration robot. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM). The generation AI can also analyze the data using a multimodal generation AI. The generation AI can also extract and analyze important parts of the data. For example, the text generation AI has learned large amounts of data and has advanced analytical capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to identify particularly important information from the data and perform analysis based on that information. The environment maintenance unit performs environment maintenance based on the data analyzed by the generation AI. For example, if the oxygen concentration is low, an oxygen generator can be installed. Furthermore, if there is a shortage of drinking water, water sources can be developed and water purification facilities can be installed. Furthermore, fertilizer can be spread and soil improvement can be performed to create soil suitable for growing crops. In this way, the environmental survey system according to the embodiment can prepare an environment suitable for migration. For example, the output unit displays the results of the environmental preparation via a web application or a mobile application. If feedback is desired in paper form, the results can be printed using a printer. Sending the results by email provides quick feedback.

[0030] The environmental data collection unit uses data collected by the exploration robot to enable the generation AI to predict environmental changes in real time and dynamically optimize the exploration robot's behavior. For example, the environmental data collection unit uses atmospheric data collected by the exploration robot to enable the generation AI to predict weather changes in real time and optimize the exploration robot's movement route. For example, it predicts the occurrence of storms and selects a safe route. This makes it possible to predict environmental changes in real time and dynamically optimize the exploration robot's behavior.

[0031] The environmental data collection unit uses the data collected by the exploration robot to generate a 3D model of the planet's terrain using a generation AI, which can then perform detailed terrain analysis. For example, the environmental data collection unit uses the data collected by the exploration robot to generate a 3D model using a generation AI, which can then perform detailed terrain analysis. For example, it can accurately grasp the shapes of mountainous areas and valleys. This allows the planet's terrain to be generated as a 3D model and detailed terrain analysis to be performed.

[0032] The environmental data collection unit can equip the exploration robot with multiple sensors and simultaneously collect different environmental data. For example, the environmental data collection unit can equip the exploration robot with a radiation sensor and a magnetic field sensor and simultaneously collect data. For example, it can analyze the correlation between radiation levels and magnetic field strength. This allows different environmental data to be collected simultaneously.

[0033] The environmental data collection unit can efficiently collect environmental data over a wide area by simultaneously dispatching multiple exploration robots and having them collect data in cooperation with each other. For example, the environmental data collection unit can simultaneously dispatch multiple exploration robots, with each robot responsible for collecting data in a different area. For example, geological data over a wide area can be collected in a short period of time. This allows for efficient collection of environmental data over a wide area.

[0034] The environmental data analysis unit simulates the planet's ecosystem based on the data collected by the generation AI, and can predict long-term environmental changes. For example, the environmental data analysis unit simulates the ecosystem based on plant data collected by the generation AI, and predicts changes in plant growth and distribution. For example, it analyzes changes in vegetation due to climate change. This makes it possible to simulate the planet's ecosystem and predict long-term environmental changes.

[0035] The environmental data analysis unit can build a planetary climate model based on the data collected by the generation AI and perform a detailed analysis of the impact of climate change. For example, the environmental data analysis unit can build a climate model based on the temperature data collected by the generation AI and perform a detailed analysis of temperature fluctuations. For example, it can predict seasonal temperature fluctuations. This allows the construction of a planetary climate model and a detailed analysis of the impact of climate change.

[0036] The environmental data analysis unit allows the generation AI to integrate and analyze different data sources during environmental surveys. For example, the generation AI integrates satellite data and ground observation data to improve the accuracy of environmental surveys. For example, it combines satellite images and ground weather data for analysis. This improves the accuracy of environmental surveys by integrating and analyzing different data sources.

[0037] The environmental data analysis unit allows the generation AI to construct a virtual reality (VR) environment based on the results of the environmental survey, allowing the user to experience the environment of the destination. The environmental data analysis unit, for example, allows the generation AI to construct a virtual reality (VR) environment based on the results of the environmental survey, allowing the user to experience the environment of the destination. For example, the planet's landscape and climate are reproduced in VR. This allows the creation of a virtual reality (VR) environment, allowing the user to experience the environment of the destination.

[0038] The environment maintenance unit allows the generation AI to monitor the progress of environment maintenance in real time and dynamically adjust the work plan as needed.The environment maintenance unit allows the generation AI to monitor the progress of environment maintenance in real time and dynamically adjust the work plan as needed.For example, if an unexpected failure occurs, the work plan will be immediately changed.This allows the progress of environment maintenance to be monitored in real time and the work plan to be dynamically adjusted.

[0039] The environmental maintenance unit allows the generation AI to manage resources for environmental maintenance and allocate resources efficiently. For example, the generation AI manages resources for environmental maintenance and allocates resources efficiently. For example, it supplies necessary resources at the optimal time. This allows resource management for environmental maintenance and allocates resources efficiently.

[0040] In the environmental maintenance unit, the generating AI can integrate different technologies to perform the work of environmental maintenance. For example, the generating AI can integrate drones and autonomous vehicles to efficiently perform environmental maintenance work. For example, drones monitor from the air, and autonomous vehicles perform work on the ground. This allows different technologies to be integrated to efficiently perform environmental maintenance work.

[0041] The environment preparation unit allows the generation AI to construct a virtual reality (VR) environment based on the results of the environment preparation, allowing the user to experience the prepared environment. The environment preparation unit, for example, allows the generation AI to construct a virtual reality (VR) environment based on the results of the environment preparation, allowing the user to experience the prepared environment. For example, the prepared landscape and infrastructure are reproduced in VR. In this way, a virtual reality (VR) environment is constructed, allowing the user to experience the prepared environment.

[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 environmental exploration system further includes an energy management unit. The energy management unit can optimize the energy consumption of the exploration robot. For example, the energy management unit monitors the remaining battery level of the exploration robot in real time and selects an energy-efficient route. The energy management unit can also turn off some sensors as needed to allow the exploration robot to save energy. Furthermore, the energy management unit can also provide the exploration robot with a function to automatically charge using a solar panel. This optimizes the energy consumption of the exploration robot and enables it to conduct exploration activities for a long period of time.

[0044] The environmental investigation system further includes a communication unit. The communication unit can optimize communication between the exploration robot and the operator. For example, the communication unit transmits data collected by the exploration robot to the operator in real time. The communication unit can also quickly transmit instructions from the operator to the exploration robot. Furthermore, the communication unit can also have a function to automatically select the optimal communication route when the exploration robot goes out of communication range. This optimizes communication between the exploration robot and the operator, enabling efficient data collection and instruction transmission.

[0045] The environmental investigation system further includes a safety management unit. The safety management unit can provide a function for ensuring the safety of the exploration robot. For example, the safety management unit can issue a warning when the exploration robot approaches a dangerous area. The safety management unit can also automatically switch the exploration robot into repair mode if it breaks down. Furthermore, the safety management unit can also have a function for monitoring the surrounding environment in real time to prevent the exploration robot from colliding with other robots or obstacles. This ensures the safety of the exploration robot and supports efficient exploration activities.

[0046] The environmental investigation system further includes a data storage unit. The data storage unit can efficiently store data collected by the exploration robot. For example, the data storage unit automatically uploads the collected data to cloud storage. The data storage unit can also compress and store the collected data. Furthermore, the data storage unit can also have a function to back up the collected data and prevent data loss. This allows the data collected by the exploration robot to be efficiently stored and ensures data safety.

[0047] The environmental investigation system further includes a data sharing unit. The data sharing unit can share the data collected by the exploration robot with other researchers and institutions. For example, the data sharing unit can upload the collected data to an online platform so that other researchers can access it. The data sharing unit can also share the collected data exclusively with a specific research group. Furthermore, the data sharing unit can also have a function to share the collected data in real time and promote collaborative research. This allows the data collected by the exploration robot to be shared efficiently and supports the progress of research.

[0048] The environmental investigation system further includes a data visualization unit. The data visualization unit can visually display the data collected by the exploration robot. For example, the data visualization unit displays the collected data as a graph or chart. The data visualization unit can also plot the collected data on a map. Furthermore, the data visualization unit can also display the collected data as a 3D model and have a function to support detailed analysis. This allows the data collected by the exploration robot to be visually displayed, facilitating understanding of the data.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The rover collects environmental data. For example, the rover can analyze the planet's atmospheric composition and measure the concentration of oxygen and carbon dioxide. It can also conduct geological surveys and assess the risk of earthquakes and volcanic activity. It can also collect water quality data and determine whether the water is safe for drinking. Step 2: The generative AI analyzes the environmental data collected by the exploration robot. For example, the generative AI analyzes the data using a text generation AI (e.g., LLM). It can also analyze the data using a multimodal generation AI. The generative AI can extract and analyze important parts of the data. For example, a text generation AI has learned large amounts of data and has advanced analytical capabilities. A multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generative AI uses keyword extraction technology to pick out particularly important information from the data and conducts analysis based on that. Step 3: The Environmental Improvement Department performs environmental improvements based on the data analyzed by the generation AI. For example, if the oxygen concentration is low, an oxygen generator will be installed. If there is a shortage of drinking water, water sources can be developed and water purification facilities can be installed. Furthermore, fertilizer can be spread and soil improvement can be carried out to create soil suitable for growing crops.

[0051] (Example 2) The environmental survey system according to an embodiment of the present invention is a system in which an exploration robot equipped with a generation AI collects and analyzes environmental data and performs environmental maintenance. As a result, the environmental survey system can maintain a habitable environment.

[0052] An environmental investigation system according to an embodiment includes an exploration robot, a generation AI, an environmental data collection unit, an environmental data analysis unit, and an environment maintenance unit. The exploration robot collects environmental data. For example, the exploration robot analyzes the atmospheric components of a planet and measures the concentrations of oxygen and carbon dioxide. The exploration robot can also conduct geological surveys and assess the risk of earthquakes and volcanic activity. The exploration robot can also collect water quality data and determine whether the water is safe for drinking. The generation AI analyzes the environmental data collected by the exploration robot. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM). The generation AI can also analyze the data using a multimodal generation AI. The generation AI can also extract and analyze important parts of the data. For example, the text generation AI has learned large amounts of data and has advanced analytical capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to identify particularly important information from the data and perform analysis based on that information. The environment maintenance unit performs environment maintenance based on the data analyzed by the generation AI. For example, if the oxygen concentration is low, an oxygen generator can be installed. Furthermore, if there is a shortage of drinking water, water sources can be developed and water purification facilities can be installed. Furthermore, fertilizer can be spread and soil improvement can be performed to create soil suitable for growing crops. In this way, the environmental survey system according to the embodiment can prepare an environment suitable for migration. For example, the output unit displays the results of the environmental preparation via a web application or a mobile application. If feedback is desired in paper form, the results can be printed using a printer. Sending the results by email provides quick feedback.

[0053] The environmental data collection unit uses data collected by the exploration robot to enable the generation AI to predict environmental changes in real time and dynamically optimize the exploration robot's behavior. For example, the environmental data collection unit uses atmospheric data collected by the exploration robot to enable the generation AI to predict weather changes in real time and optimize the exploration robot's movement route. For example, it predicts the occurrence of storms and selects a safe route. This makes it possible to predict environmental changes in real time and dynamically optimize the exploration robot's behavior.

[0054] The environmental data collection unit uses the data collected by the exploration robot to generate a 3D model of the planet's terrain using a generation AI, which can then perform detailed terrain analysis. For example, the environmental data collection unit uses the data collected by the exploration robot to generate a 3D model using a generation AI, which can then perform detailed terrain analysis. For example, it can accurately grasp the shapes of mountainous areas and valleys. This allows the planet's terrain to be generated as a 3D model and detailed terrain analysis to be performed.

[0055] The environmental data collection unit uses the emotion estimation function to monitor the stress level of the operator operating the exploration robot in real time, thereby improving operation efficiency. For example, the environmental data collection unit analyzes the operator's facial expressions while operating the exploration robot and monitors the stress level in real time. For example, it suggests taking a break if stress increases. This allows the operator's stress level to be monitored in real time, improving operation efficiency.

[0056] The environmental data collection unit can equip the exploration robot with multiple sensors and simultaneously collect different environmental data. For example, the environmental data collection unit can equip the exploration robot with a radiation sensor and a magnetic field sensor and simultaneously collect data. For example, it can analyze the correlation between radiation levels and magnetic field strength. This allows different environmental data to be collected simultaneously.

[0057] The environmental data collection unit can efficiently collect environmental data over a wide area by simultaneously dispatching multiple exploration robots and having them collect data in cooperation with each other. For example, the environmental data collection unit can simultaneously dispatch multiple exploration robots, with each robot responsible for collecting data in a different area. For example, geological data over a wide area can be collected in a short period of time. This allows for efficient collection of environmental data over a wide area.

[0058] The environmental data collection unit is equipped with an emotion estimation function and can evaluate the emotional appeal of potential relocation sites based on data collected by the exploration robot. For example, the environmental data collection unit uses a generation AI to estimate emotions based on landscape data collected by the exploration robot and evaluate the beauty of the potential relocation site. For example, areas with many beautiful landscapes are given a high rating. This allows the emotional appeal of potential relocation sites to be evaluated.

[0059] The environmental data analysis unit simulates the planet's ecosystem based on the data collected by the generation AI, and can predict long-term environmental changes. For example, the environmental data analysis unit simulates the ecosystem based on plant data collected by the generation AI, and predicts changes in plant growth and distribution. For example, it analyzes changes in vegetation due to climate change. This makes it possible to simulate the planet's ecosystem and predict long-term environmental changes.

[0060] The environmental data analysis unit can build a planetary climate model based on the data collected by the generation AI and perform a detailed analysis of the impact of climate change. For example, the environmental data analysis unit can build a climate model based on the temperature data collected by the generation AI and perform a detailed analysis of temperature fluctuations. For example, it can predict seasonal temperature fluctuations. This allows the construction of a planetary climate model and a detailed analysis of the impact of climate change.

[0061] The environmental data analysis unit uses the emotion estimation function to analyze people's emotional responses to the results of the environmental survey and can reflect this in the selection of a relocation destination. For example, the environmental data analysis unit collects people's emotional responses to the results of the environmental survey, and the generation AI analyzes the data. For example, areas with many positive responses to the survey results are designated as potential relocation locations. This allows people's emotional responses to the results of the environmental survey to be analyzed and reflected in the selection of a relocation destination.

[0062] The environmental data analysis unit allows the generation AI to integrate and analyze different data sources during environmental surveys. For example, the generation AI integrates satellite data and ground observation data to improve the accuracy of environmental surveys. For example, it combines satellite images and ground weather data for analysis. This improves the accuracy of environmental surveys by integrating and analyzing different data sources.

[0063] The environmental data analysis unit allows the generation AI to construct a virtual reality (VR) environment based on the results of the environmental survey, allowing the user to experience the environment of the destination. The environmental data analysis unit, for example, allows the generation AI to construct a virtual reality (VR) environment based on the results of the environmental survey, allowing the user to experience the environment of the destination. For example, the planet's landscape and climate are reproduced in VR. This allows the creation of a virtual reality (VR) environment, allowing the user to experience the environment of the destination.

[0064] The environmental data analysis unit can use the emotion estimation function to emotionally evaluate the attractiveness of a relocation destination based on the results of an environmental survey and reflect this in the relocation plan. The environmental data analysis unit, for example, uses the emotion estimation function to evaluate the attractiveness of a relocation destination based on the results of an environmental survey and reflects this in the relocation plan. For example, it preferentially selects areas where there are many positive emotions regarding the survey results. This allows the attractiveness of a relocation destination to be emotionally evaluated and reflected in the relocation plan.

[0065] The environment maintenance unit allows the generation AI to monitor the progress of environment maintenance in real time and dynamically adjust the work plan as needed.The environment maintenance unit allows the generation AI to monitor the progress of environment maintenance in real time and dynamically adjust the work plan as needed.For example, if an unexpected failure occurs, the work plan will be immediately changed.This allows the progress of environment maintenance to be monitored in real time and the work plan to be dynamically adjusted.

[0066] The environmental maintenance unit allows the generation AI to manage resources for environmental maintenance and allocate resources efficiently. For example, the generation AI manages resources for environmental maintenance and allocates resources efficiently. For example, it supplies necessary resources at the optimal time. This allows resource management for environmental maintenance and allocates resources efficiently.

[0067] In the environmental maintenance unit, the generating AI can integrate different technologies to perform the work of environmental maintenance. For example, the generating AI can integrate drones and autonomous vehicles to efficiently perform environmental maintenance work. For example, drones monitor from the air, and autonomous vehicles perform work on the ground. This allows different technologies to be integrated to efficiently perform environmental maintenance work.

[0068] The environment preparation unit allows the generation AI to construct a virtual reality (VR) environment based on the results of the environment preparation, allowing the user to experience the prepared environment. The environment preparation unit, for example, allows the generation AI to construct a virtual reality (VR) environment based on the results of the environment preparation, allowing the user to experience the prepared environment. For example, the prepared landscape and infrastructure are reproduced in VR. In this way, a virtual reality (VR) environment is constructed, allowing the user to experience the prepared environment.

[0069] The environmental improvement unit can use the emotion estimation function to emotionally evaluate the attractiveness of the relocation destination based on the results of the environmental improvement and reflect this in the relocation plan. The environmental improvement unit, for example, uses the emotion estimation function to evaluate the attractiveness of the relocation destination based on the results of the environmental improvement and reflects this in the relocation plan. For example, it preferentially selects areas where there are many positive emotions regarding the improvement results. In this way, the attractiveness of the relocation destination can be emotionally evaluated and reflected in the relocation plan.

[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 environmental exploration system further includes an energy management unit. The energy management unit can optimize the energy consumption of the exploration robot. For example, the energy management unit monitors the remaining battery level of the exploration robot in real time and selects an energy-efficient route. The energy management unit can also turn off some sensors as needed to allow the exploration robot to save energy. Furthermore, the energy management unit can also provide the exploration robot with a function to automatically charge using a solar panel. This optimizes the energy consumption of the exploration robot and enables it to conduct exploration activities for a long period of time.

[0072] The environmental investigation system further includes a communication unit. The communication unit can optimize communication between the exploration robot and the operator. For example, the communication unit transmits data collected by the exploration robot to the operator in real time. The communication unit can also quickly transmit instructions from the operator to the exploration robot. Furthermore, the communication unit can also have a function to automatically select the optimal communication route when the exploration robot goes out of communication range. This optimizes communication between the exploration robot and the operator, enabling efficient data collection and instruction transmission.

[0073] The environmental investigation system further includes a safety management unit. The safety management unit can provide a function for ensuring the safety of the exploration robot. For example, the safety management unit can issue a warning when the exploration robot approaches a dangerous area. The safety management unit can also automatically switch the exploration robot into repair mode if it breaks down. Furthermore, the safety management unit can also have a function for monitoring the surrounding environment in real time to prevent the exploration robot from colliding with other robots or obstacles. This ensures the safety of the exploration robot and supports efficient exploration activities.

[0074] The environmental investigation system can further use an emotion estimation function to optimize the operation of the exploration robot. For example, the emotion estimation function can be used to estimate the emotional state of the operator based on data collected by the exploration robot, and the operation of the exploration robot can be temporarily suspended if stress increases. The emotion estimation function can also be used to adjust the operation speed of the exploration robot according to the emotional state of the operator. Furthermore, the emotion estimation function can also be used to optimize the operation route of the exploration robot based on the emotional state of the operator. This allows the exploration robot to perform optimal operation according to the emotional state of the operator.

[0075] The environmental exploration system can further use the emotion estimation function to optimize the operation interface of the exploration robot. For example, the emotion estimation function can be used to monitor the operator's emotional state in real time and simplify the operation interface if stress increases. The emotion estimation function can also be used to change the color and layout of the operation interface according to the operator's emotional state. The emotion estimation function can also be used to adjust the feedback of the operation interface based on the operator's emotional state. This provides an optimal operation interface according to the operator's emotional state.

[0076] The environmental investigation system can further use the emotion estimation function to optimize the cooperative behavior of the exploration robots. For example, the emotion estimation function can be used to monitor the emotional state of the operators of multiple exploration robots and adjust their cooperative behavior if stress increases. The emotion estimation function can also be used to optimize communication between the exploration robots according to the emotional state of the operators. The emotion estimation function can also be used to adjust the timing of the cooperative behavior of the exploration robots based on the emotional state of the operators. This allows for optimal cooperative behavior of the exploration robots according to the emotional state of the operators.

[0077] The environmental exploration system can further use the emotion estimation function to optimize the data collection strategy of the exploration robot. For example, the emotion estimation function can be used to monitor the emotional state of the operator and change the priority of data collection when stress increases. The emotion estimation function can also be used to adjust the frequency of data collection according to the emotional state of the operator. The emotion estimation function can also be used to optimize the data collection method based on the emotional state of the operator. This provides an optimal data collection strategy according to the emotional state of the operator.

[0078] The environmental investigation system further includes a data storage unit. The data storage unit can efficiently store data collected by the exploration robot. For example, the data storage unit automatically uploads the collected data to cloud storage. The data storage unit can also compress and store the collected data. Furthermore, the data storage unit can also have a function to back up the collected data and prevent data loss. This allows the data collected by the exploration robot to be efficiently stored and ensures data safety.

[0079] The environmental investigation system further includes a data sharing unit. The data sharing unit can share the data collected by the exploration robot with other researchers and institutions. For example, the data sharing unit can upload the collected data to an online platform so that other researchers can access it. The data sharing unit can also share the collected data exclusively with a specific research group. Furthermore, the data sharing unit can also have a function to share the collected data in real time and promote collaborative research. This allows the data collected by the exploration robot to be shared efficiently and supports the progress of research.

[0080] The environmental investigation system further includes a data visualization unit. The data visualization unit can visually display the data collected by the exploration robot. For example, the data visualization unit displays the collected data as a graph or chart. The data visualization unit can also plot the collected data on a map. Furthermore, the data visualization unit can also display the collected data as a 3D model and have a function to support detailed analysis. This allows the data collected by the exploration robot to be visually displayed, facilitating understanding of the data.

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

[0082] Step 1: The rover collects environmental data. For example, the rover can analyze the planet's atmospheric composition and measure the concentration of oxygen and carbon dioxide. It can also conduct geological surveys and assess the risk of earthquakes and volcanic activity. It can also collect water quality data and determine whether the water is safe for drinking. Step 2: The generative AI analyzes the environmental data collected by the exploration robot. For example, the generative AI analyzes the data using a text generation AI (e.g., LLM). It can also analyze the data using a multimodal generation AI. The generative AI can extract and analyze important parts of the data. For example, a text generation AI has learned large amounts of data and has advanced analytical capabilities. A multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generative AI uses keyword extraction technology to pick out particularly important information from the data and conducts analysis based on that. Step 3: The Environmental Improvement Department performs environmental improvements based on the data analyzed by the generation AI. For example, if the oxygen concentration is low, an oxygen generator will be installed. If there is a shortage of drinking water, water sources can be developed and water purification facilities can be installed. Furthermore, fertilizer can be spread and soil improvement can be carried out to create soil suitable for growing crops.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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, in order to avoid confusion and to 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. An exploration robot equipped with generative AI, A generation AI that analyzes the environmental data collected by the exploration robot; An environment improvement unit that improves the environment based on the data analyzed by the generation AI. A system characterized by:

2. The Environmental Data Collection Department Based on the data collected by the exploration robot, the generative AI predicts environmental changes in real time and dynamically optimizes the behavior of the exploration robot.

2. The system of claim 1.

3. The Environmental Data Collection Department The exploration robot is equipped with multiple sensors to simultaneously collect different environmental data.

2. The system of claim 1.

4. The Environmental Data Analysis Department Based on the data collected by the generative AI, the planet's ecosystem is simulated and long-term environmental changes are predicted.

2. The system of claim 1.

5. The environmental improvement department The generative AI monitors the progress of environmental development in real time and dynamically adjusts the work plan as needed.

2. The system of claim 1.

6. The Environmental Data Collection Department Based on the data collected by the exploration robot, the generation AI monitors the stress level of the operator operating the exploration robot in real time, thereby improving the efficiency of operation.

2. The system of claim 1.

7. The Environmental Data Analysis Department Analyzing people's emotional responses to the results of environmental surveys and reflecting this in their relocation decisions 2. The system of claim 1.

8. The environmental improvement department Analyzing people's emotional responses to the results of environmental improvements and reflecting this in improvement plans 2. The system of claim 1.

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