System

Abstract

An object of the system according to the embodiment is to analyze a signal or a radio wave from outer space and estimate information regarding an unknown region or a living body.SOLUTION: A system includes a signal collection unit, a generation AI analysis unit, and an estimation unit. The signal collection unit collects signals and radio waves from outer space. The generation AI analysis unit analyzes the signals and radio waves collected by the signal collection unit in the generation AI. The estimation unit estimates information on an unknown region of the universe or a living body on the basis of a result analyzed by the generation AI analysis 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 faced the challenge of making it difficult to efficiently analyze signals and radio waves from space and infer information about unknown regions and living organisms.

[0005] The system according to the embodiment aims to analyze signals and radio waves from space and estimate information about unknown regions and living organisms. [Means for solving the problem]

[0006] The system according to the embodiment includes a signal collection unit, a generation AI analysis unit, and an estimation unit. The signal collection unit collects signals and radio waves from space. The generation AI analysis unit analyzes the signals and radio waves collected by the signal collection unit using a generation AI. The estimation unit estimates information about unknown regions of space and living organisms based on the results of the analysis by the generation AI analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze signals and radio waves from space and infer information about unknown regions and living organisms. [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 space analysis system according to an embodiment of the present invention collects signals and radio waves from space, analyzes them using a generating AI, and estimates information about unknown regions and life forms. This allows the space analysis system to efficiently estimate information about unknown regions and life forms in the universe.

[0029] A space analysis system according to an embodiment includes a signal collection unit, a generation AI analysis unit, and an estimation unit. The signal collection unit collects signals and radio waves from space. For example, the signals and radio waves are collected using ground-based observatories, space telescopes, and artificial satellites. The signal collection unit can also collect signals and radio waves in real time and store them in a database. The generation AI analysis unit uses a generation AI to analyze the signals and radio waves collected by the signal collection unit. For example, the generation AI can analyze signal and radio wave patterns using deep learning or neural networks to extract unknown information. The generation AI can also analyze the meaning of specific frequencies or patterns when they are repeated. The estimation unit estimates information about unknown regions and life forms in space based on the results of the analysis by the generation AI analysis unit. For example, if a specific signal is received from a specific direction, the estimation unit estimates the possibility that an unknown celestial body or region exists in that direction. The estimation unit can also estimate the characteristics and structure of the region from the signal strength and pattern. This allows the space analysis system according to an embodiment to efficiently estimate information about unknown regions and life forms in space.

[0030] Signal collection units are also installed on the surface of the Moon and Mars, allowing data to be collected from a wider range. The signal collection unit, for example, installs signal and radio wave collection devices on the surface of the Moon to collect signals from outside the Earth. For example, devices installed on the surface of the Moon collect signals that cannot be observed from Earth. Signal and radio wave collection devices are also installed on the surface of Mars to collect signals from Mars. For example, devices installed on the surface of Mars collect signals from Mars in real time. Signal and radio wave collection devices are also installed on the surface of the Moon and Mars, in addition to the Earth, to collect data from a wider range. For example, data from multiple locations is integrated and analyzed. This allows data to be collected from a wider range.

[0031] The signal collection unit is equipped with a multispectral sensor that can simultaneously observe different wavelengths and frequency bands, allowing for more detailed data to be collected. The signal collection unit, for example, is equipped with a multispectral sensor in a signal or radio wave collection device to simultaneously observe different wavelengths and frequency bands. For example, it simultaneously observes visible light, infrared light, radio waves, etc., and collects detailed data. Furthermore, a system that simultaneously observes signals of different wavelengths and frequency bands is constructed using a multispectral sensor. For example, multiple sensors are combined to collect data over a wide range. Furthermore, a multispectral sensor that simultaneously observes different wavelengths and frequency bands is equipped to collect more detailed data. For example, signals of different wavelengths are analyzed to obtain more detailed information. This allows for more detailed data to be collected.

[0032] The signal collection unit can also be installed on the seafloor and in polar regions to search for signals that suggest the presence of extraterrestrial life. The signal collection unit, for example, installs signal and radio wave collection devices on the seafloor to search for signals that suggest the presence of extraterrestrial life. For example, signals from the deep sea are collected and analyzed. Signal and radio wave collection devices are also installed in polar regions to search for signals that suggest the presence of extraterrestrial life. For example, signals from the Antarctic and Arctic are collected and analyzed. Signal and radio wave collection devices are also installed in extreme environments on Earth to search for signals that suggest the presence of extraterrestrial life. For example, signals from the seafloor and polar regions are collected and analyzed. In this way, signals that suggest the presence of extraterrestrial life can be searched for.

[0033] The signal collection unit may be added with a function to simultaneously collect audio and video data, allowing analysis to be performed from visual and audio information as well. For example, the signal collection unit may be added with a function to collect audio data in a signal or radio wave collection device, allowing analysis to be performed from visual and audio information as well. For example, the audio data may be analyzed to identify the source of the signal. Alternatively, a function to collect video data may be added, allowing analysis to be performed from visual information as well. For example, the video data may be analyzed to identify the source of the signal. Alternatively, a function to simultaneously collect audio and video data may be added, allowing analysis to be performed from visual and audio information as well. For example, the audio data and video data may be integrated to identify the source of the signal. This allows analysis to be performed from visual and audio information as well.

[0034] The generative AI analysis unit can be equipped with a function for comparing signals and radio waves with past data when analyzing them, and identifying abnormal patterns and new discoveries. For example, the generative AI analysis unit adds a function for comparing with past data to the generative AI, and identifies abnormal patterns and new discoveries when analyzing signals and radio waves. For example, it compares past data with current data and identifies abnormal patterns. Furthermore, a system is built using the function for comparing with past data to identify abnormal patterns and new discoveries when analyzing signals and radio waves. For example, it identifies abnormal patterns based on past data. Furthermore, a function for comparing with past data when analyzing signals and radio waves can be added to identify abnormal patterns and new discoveries. For example, it compares past data with current data and identifies new discoveries. This makes it possible to identify abnormal patterns and new discoveries.

[0035] The generative AI analysis unit can be equipped with a function to visualize the analysis results of signals and radio waves as a 3D model. For example, the generative AI analysis unit adds a function to the generative AI to visualize the analysis results of signals and radio waves as a 3D model, making them easier to understand intuitively. For example, signal patterns and changes in intensity are displayed in a 3D model. Furthermore, a system that makes it easier to understand intuitively is built using the function to visualize the analysis results of signals and radio waves as a 3D model. For example, the rhythm and tone of signals are displayed in a 3D model. Furthermore, visualizing the analysis results as a 3D model makes it easier to understand intuitively. For example, signal patterns and changes in intensity are displayed in a 3D model, making it easier to understand intuitively. This makes it easier to understand intuitively the analysis results.

[0036] The generative AI analysis unit can analyze signals and radio waves generated by natural phenomena on Earth to explore unknown regions inside the Earth. For example, the generative AI analysis unit uses generative AI to analyze signals and radio waves generated by natural phenomena on Earth to explore unknown regions inside the Earth. For example, it analyzes signals generated from earthquakes and volcanic activity to estimate the structure of the Earth's interior. In addition, a system can be built to analyze signals and radio waves generated by natural phenomena on Earth to explore unknown regions inside the Earth. For example, it analyzes signals generated from earthquakes and volcanic activity to estimate the structure of the Earth's interior. In addition, the generative AI can be used to analyze signals and radio waves generated by natural phenomena on Earth to explore unknown regions inside the Earth. For example, it analyzes signals generated from earthquakes and volcanic activity to estimate the structure of the Earth's interior. This makes it possible to explore unknown regions inside the Earth.

[0037] The generative AI analysis unit can analyze signals and radio waves from different languages ​​and cultures to explore the possibility of communication with extraterrestrial life forms. For example, the generative AI analysis unit adds a function to the generative AI to analyze signals and radio waves from different languages ​​and cultures to explore the possibility of communication with extraterrestrial life forms. For example, it analyzes signals in different languages ​​and finds common patterns. Furthermore, a system can be built to explore the possibility of communication with extraterrestrial life forms using the function to analyze signals and radio waves from different languages ​​and cultures. For example, it can analyze signals in different languages ​​and find common patterns. Furthermore, a function to analyze signals and radio waves from different languages ​​and cultures can be added to the generative AI to explore the possibility of communication with extraterrestrial life forms. For example, it can analyze signals in different languages ​​and find common patterns. This makes it possible to explore the possibility of communication with extraterrestrial life forms.

[0038] The estimation unit can be equipped with a function to integrate astronomical data when estimating unknown areas, thereby providing more accurate estimations. For example, the estimation unit adds a function to integrate other astronomical data to the generation AI, thereby providing more accurate estimations when estimating unknown areas. For example, it integrates star spectral data to improve the accuracy of the estimation results. Furthermore, a system is constructed that provides more accurate estimations when estimating unknown areas, using the function to integrate other astronomical data. For example, it integrates star spectral data to improve the accuracy of the estimation results. Furthermore, a function to integrate other astronomical data is added to the generation AI, thereby providing more accurate estimations when estimating unknown areas. For example, it integrates star spectral data to improve the accuracy of the estimation results. This allows for more accurate estimations.

[0039] The estimation unit has a function for visualizing the estimation results of the unknown area in virtual reality and augmented reality, allowing the user to intuitively understand. For example, the estimation unit adds a function to the generation AI to visualize the estimation results of the unknown area in VR or AR, allowing the user to intuitively understand. For example, the estimation results are displayed as a 3D model and experienced in VR or AR. Furthermore, a system is constructed using the function to visualize the estimation results of the unknown area in VR or AR, allowing the user to intuitively understand. For example, the estimation results are displayed as a 3D model and experienced in VR or AR. Furthermore, a function to visualize the estimation results of the unknown area in VR or AR is added to the generation AI to allow the user to intuitively understand. For example, the estimation results are displayed as a 3D model and experienced in VR or AR. This allows the user to intuitively understand.

[0040] The estimation unit can estimate unknown areas in the Earth's deep sea and underground, aiming to make new discoveries inside the Earth. The estimation unit can, for example, use generation AI to estimate unknown areas in the Earth's deep sea and underground, aiming to make new discoveries inside the Earth. For example, it can analyze signals from the deep sea and underground and estimate unknown areas. A system can also be built to estimate unknown areas in the Earth's deep sea and underground, aiming to make new discoveries inside the Earth. For example, it can analyze signals from the deep sea and underground and estimate unknown areas. A system can also be built to estimate unknown areas in the Earth's deep sea and underground, aiming to make new discoveries inside the Earth. For example, it can analyze signals from the deep sea and underground and estimate unknown areas. A system can also be built to estimate unknown areas in the Earth's deep sea and underground, aiming to make new discoveries inside the Earth. For example, it can analyze signals from the deep sea and underground and estimate unknown areas. This can aim to make new discoveries inside the Earth.

[0041] The estimation unit can analyze data from different planets and star systems and estimate unknown regions outside the solar system. The estimation unit, for example, adds a function to analyze data from different planets and star systems to the generation AI and estimates unknown regions outside the solar system. For example, it analyzes signals from different planets and star systems and estimates unknown regions. Furthermore, a system for estimating unknown regions outside the solar system is constructed using the function to analyze data from different planets and star systems. For example, it analyzes signals from different planets and star systems and estimates unknown regions. Furthermore, a function to analyze data from different planets and star systems is added to the generation AI and estimates unknown regions outside the solar system. For example, it analyzes signals from different planets and star systems and estimates unknown regions. This makes it possible to estimate unknown regions outside the solar system.

[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 signal collection unit can not only collect signals and radio waves from space, but also signals and radio waves generated by natural phenomena on Earth. For example, it can collect signals generated by earthquakes and volcanic activity to analyze the structure of the Earth's interior. It can also collect radio waves generated by natural phenomena such as lightning and auroras to analyze the state of the Earth's atmosphere and magnetic field. It can also collect signals generated by ocean waves and currents to analyze ocean dynamics. This allows the space analysis system to efficiently collect and analyze information about natural phenomena on Earth.

[0044] Signal collectors will be installed not only on the surfaces of the Moon and Mars, but also on the moons of Jupiter and Saturn, allowing data to be collected from a wider range of locations. For example, equipment installed on Jupiter's moon Europa will collect signals from oceans that may exist beneath the ice. Equipment installed on Saturn's moon Titan will also be able to collect signals from methane lakes and rivers. Furthermore, equipment installed on Jupiter and Saturn's moons will be able to collect information on the atmosphere and magnetic field of these moons in real time. This will allow data to be collected from a wider range of locations, allowing for efficient inference of information about unknown regions of space and life forms.

[0045] The signal collection unit can be equipped not only with a multispectral sensor that can simultaneously observe different wavelengths and frequency bands, but also with sensors that can simultaneously observe different physical phenomena. For example, it can be equipped with a gravitational wave sensor to observe gravitational waves from space. It can also be equipped with a neutrino detector to observe neutrinos from space. It can also be equipped with a magnetic field sensor to observe fluctuations in the magnetic field in space. This allows different physical phenomena to be observed simultaneously and more detailed data to be collected.

[0046] Signal collection units can be installed not only on the ocean floor and in polar regions, but also outside the Earth's atmosphere to search for signals that suggest the presence of extraterrestrial life. For example, devices installed outside the Earth's atmosphere can directly collect signals from space. Devices installed outside the Earth's atmosphere can also collect signals without being affected by the Earth's atmosphere or magnetic field. Furthermore, devices installed outside the Earth's atmosphere can collect signals that suggest the presence of extraterrestrial life in real time. This makes it possible to search for signals that suggest the presence of extraterrestrial life.

[0047] The signal collection unit can not only add the function of simultaneously collecting audio and video data, but also simultaneously collect tactile and olfactory data. For example, it can be equipped with a tactile sensor to collect the physical characteristics of the signal source. It can also be equipped with an olfactory sensor to collect the chemical characteristics of the signal source. Furthermore, it is possible to integrate audio, video, tactile, and olfactory data to analyze detailed information about the signal source. This allows analysis to be performed not only from visual and auditory information, but also from tactile and olfactory information.

[0048] The generative AI analysis unit can compare signals and radio waves with past data to identify abnormal patterns and new discoveries, as well as predict future data. For example, it can predict future signal patterns based on past and current data. It can also compare past and current data to predict future abnormal patterns. Furthermore, predicting future data can increase the possibility of new discoveries. This allows it to not only identify abnormal patterns and new discoveries, but also predict future data.

[0049] The generative AI analysis unit can not only visualize the signal and radio wave analysis results as a 3D model, but also provide the analysis results as audio and haptic feedback. For example, it can express signal patterns and changes in intensity to the user through audio. It can also provide the signal analysis results as haptic feedback, allowing the user to physically feel them. Furthermore, it is possible to combine audio and haptic feedback to make the analysis results more intuitively understandable. This allows the analysis results to be provided not only as a 3D model, but also as audio and haptic feedback.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The signal collector collects signals and radio waves from space. For example, signals and radio waves can be collected using ground observatories, space telescopes, and artificial satellites. The signal collector can also collect signals and radio waves in real time and store them in a database. Step 2: The Generative AI Analysis Unit uses the Generative AI to analyze the signals and radio waves collected by the Signal Collection Unit. For example, the Generative AI uses deep learning and neural networks to analyze the patterns of signals and radio waves and extract unknown information. The Generative AI can also analyze what a specific frequency or pattern means when it is repeated. Step 3: The estimation unit infers information about unknown regions of the universe and life forms based on the results of the analysis by the generation AI analysis unit. For example, if a specific signal is received from a specific direction, it estimates the possibility that an unknown celestial body or region exists in that direction. It can also infer the characteristics and structure of that region from the signal strength and pattern.

[0052] (Example 2) The space analysis system according to an embodiment of the present invention collects signals and radio waves from space, analyzes them using a generating AI, and estimates information about unknown regions and life forms. This allows the space analysis system to efficiently estimate information about unknown regions and life forms in the universe.

[0053] A space analysis system according to an embodiment includes a signal collection unit, a generation AI analysis unit, and an estimation unit. The signal collection unit collects signals and radio waves from space. For example, the signals and radio waves are collected using ground-based observatories, space telescopes, and artificial satellites. The signal collection unit can also collect signals and radio waves in real time and store them in a database. The generation AI analysis unit uses a generation AI to analyze the signals and radio waves collected by the signal collection unit. For example, the generation AI can analyze signal and radio wave patterns using deep learning or neural networks to extract unknown information. The generation AI can also analyze the meaning of specific frequencies or patterns when they are repeated. The estimation unit estimates information about unknown regions and life forms in space based on the results of the analysis by the generation AI analysis unit. For example, if a specific signal is received from a specific direction, the estimation unit estimates the possibility that an unknown celestial body or region exists in that direction. The estimation unit can also estimate the characteristics and structure of the region from the signal strength and pattern. This allows the space analysis system according to an embodiment to efficiently estimate information about unknown regions and life forms in space.

[0054] The signal collection unit can be equipped with an emotion estimation function that emotionally evaluates the possibility that the source of a signal is an intelligent life form. The signal collection unit, for example, is equipped with the emotion estimation function in a device that collects signals or radio waves from space, and analyzes the emotional characteristics of the collected signals. For example, the possibility that the source is an intelligent life form is evaluated based on the signal pattern or changes in intensity. In addition, the emotion estimation function is used to build a system that emotionally evaluates whether the source of a signal is an intelligent life form. For example, the rhythm or tone of the signal is analyzed to extract emotional characteristics. In addition, by analyzing the emotional characteristics of the signal, the possibility that the source is an intelligent life form is evaluated. For example, emotional characteristics are extracted and evaluated based on the signal pattern or changes in intensity. This makes it possible to emotionally evaluate the possibility that the source of a signal is an intelligent life form.

[0055] Signal collection units are also installed on the surface of the Moon and Mars, allowing data to be collected from a wider range. The signal collection unit, for example, installs signal and radio wave collection devices on the surface of the Moon to collect signals from outside the Earth. For example, devices installed on the surface of the Moon collect signals that cannot be observed from Earth. Signal and radio wave collection devices are also installed on the surface of Mars to collect signals from Mars. For example, devices installed on the surface of Mars collect signals from Mars in real time. Signal and radio wave collection devices are also installed on the surface of the Moon and Mars, in addition to the Earth, to collect data from a wider range. For example, data from multiple locations is integrated and analyzed. This allows data to be collected from a wider range.

[0056] The signal collection unit is equipped with a multispectral sensor that can simultaneously observe different wavelengths and frequency bands, allowing for more detailed data to be collected. The signal collection unit, for example, is equipped with a multispectral sensor in a signal or radio wave collection device to simultaneously observe different wavelengths and frequency bands. For example, it simultaneously observes visible light, infrared light, radio waves, etc., and collects detailed data. Furthermore, a system that simultaneously observes signals of different wavelengths and frequency bands is constructed using a multispectral sensor. For example, multiple sensors are combined to collect data over a wide range. Furthermore, a multispectral sensor that simultaneously observes different wavelengths and frequency bands is equipped to collect more detailed data. For example, signals of different wavelengths are analyzed to obtain more detailed information. This allows for more detailed data to be collected.

[0057] The signal collection unit can also be installed on the seafloor and in polar regions to search for signals that suggest the presence of extraterrestrial life. The signal collection unit, for example, installs signal and radio wave collection devices on the seafloor to search for signals that suggest the presence of extraterrestrial life. For example, signals from the deep sea are collected and analyzed. Signal and radio wave collection devices are also installed in polar regions to search for signals that suggest the presence of extraterrestrial life. For example, signals from the Antarctic and Arctic are collected and analyzed. Signal and radio wave collection devices are also installed in extreme environments on Earth to search for signals that suggest the presence of extraterrestrial life. For example, signals from the seafloor and polar regions are collected and analyzed. In this way, signals that suggest the presence of extraterrestrial life can be searched for.

[0058] The signal collection unit may be added with a function to simultaneously collect audio and video data, allowing analysis to be performed from visual and audio information as well. For example, the signal collection unit may be added with a function to collect audio data in a signal or radio wave collection device, allowing analysis to be performed from visual and audio information as well. For example, the audio data may be analyzed to identify the source of the signal. Alternatively, a function to collect video data may be added, allowing analysis to be performed from visual information as well. For example, the video data may be analyzed to identify the source of the signal. Alternatively, a function to simultaneously collect audio and video data may be added, allowing analysis to be performed from visual and audio information as well. For example, the audio data and video data may be integrated to identify the source of the signal. This allows analysis to be performed from visual and audio information as well.

[0059] The signal collection unit can use the emotion estimation function to monitor the operator's emotions in real time when collecting signals or radio waves, thereby optimizing collection efficiency. The signal collection unit, for example, uses the emotion estimation function to build a system that monitors the operator's emotions in real time when collecting signals or radio waves. For example, the emotional state of the operator is analyzed and collection efficiency is optimized. Also, a system is developed that monitors the operator's emotions in real time and optimizes collection efficiency. For example, the settings of the collection device are adjusted according to the emotional state. Also, the emotion estimation function is used to monitor the operator's emotions in real time and optimize collection efficiency. For example, the operation of the collection device is adjusted according to the emotional state. In this way, the operator's emotions can be monitored in real time and collection efficiency can be optimized.

[0060] The generative AI analysis unit can be equipped with an emotion estimation function that estimates the emotional state of the intelligent life form that is the source of the signal based on the analysis results of the signal and radio waves. For example, the generative AI analysis unit incorporates the emotion estimation function into the generative AI and estimates the emotional state of the intelligent life form that is the source of the signal based on the analysis results of the signal and radio waves. For example, the emotional state is estimated by analyzing changes in the signal pattern and intensity. Furthermore, the emotion estimation function is used to build a system that estimates the emotional state of the intelligent life form that is the source of the signal based on the analysis results of the signal and radio waves. For example, the emotional state is estimated by analyzing the rhythm and tone of the signal. Furthermore, the emotional state of the intelligent life form that is the source of the signal is estimated based on the analysis results of the signal and radio waves. For example, the emotional state is estimated based on changes in the signal pattern and intensity. In this way, the emotional state of the intelligent life form that is the source of the signal can be estimated.

[0061] The generative AI analysis unit can be equipped with a function for comparing signals and radio waves with past data when analyzing them, and identifying abnormal patterns and new discoveries. For example, the generative AI analysis unit adds a function for comparing with past data to the generative AI, and identifies abnormal patterns and new discoveries when analyzing signals and radio waves. For example, it compares past data with current data and identifies abnormal patterns. Furthermore, a system is built using the function for comparing with past data to identify abnormal patterns and new discoveries when analyzing signals and radio waves. For example, it identifies abnormal patterns based on past data. Furthermore, a function for comparing with past data when analyzing signals and radio waves can be added to identify abnormal patterns and new discoveries. For example, it compares past data with current data and identifies new discoveries. This makes it possible to identify abnormal patterns and new discoveries.

[0062] The generative AI analysis unit can be equipped with a function to visualize the analysis results of signals and radio waves as a 3D model. For example, the generative AI analysis unit adds a function to the generative AI to visualize the analysis results of signals and radio waves as a 3D model, making them easier to understand intuitively. For example, signal patterns and changes in intensity are displayed in a 3D model. Furthermore, a system that makes it easier to understand intuitively is built using the function to visualize the analysis results of signals and radio waves as a 3D model. For example, the rhythm and tone of signals are displayed in a 3D model. Furthermore, visualizing the analysis results as a 3D model makes it easier to understand intuitively. For example, signal patterns and changes in intensity are displayed in a 3D model, making it easier to understand intuitively. This makes it easier to understand intuitively the analysis results.

[0063] The generative AI analysis unit can analyze signals and radio waves generated by natural phenomena on Earth to explore unknown regions inside the Earth. For example, the generative AI analysis unit uses generative AI to analyze signals and radio waves generated by natural phenomena on Earth to explore unknown regions inside the Earth. For example, it analyzes signals generated from earthquakes and volcanic activity to estimate the structure of the Earth's interior. In addition, a system can be built to analyze signals and radio waves generated by natural phenomena on Earth to explore unknown regions inside the Earth. For example, it analyzes signals generated from earthquakes and volcanic activity to estimate the structure of the Earth's interior. In addition, the generative AI can be used to analyze signals and radio waves generated by natural phenomena on Earth to explore unknown regions inside the Earth. For example, it analyzes signals generated from earthquakes and volcanic activity to estimate the structure of the Earth's interior. This makes it possible to explore unknown regions inside the Earth.

[0064] The generative AI analysis unit can analyze signals and radio waves from different languages ​​and cultures to explore the possibility of communication with extraterrestrial life forms. For example, the generative AI analysis unit adds a function to the generative AI to analyze signals and radio waves from different languages ​​and cultures to explore the possibility of communication with extraterrestrial life forms. For example, it analyzes signals in different languages ​​and finds common patterns. Furthermore, a system can be built to explore the possibility of communication with extraterrestrial life forms using the function to analyze signals and radio waves from different languages ​​and cultures. For example, it can analyze signals in different languages ​​and find common patterns. Furthermore, a function to analyze signals and radio waves from different languages ​​and cultures can be added to the generative AI to explore the possibility of communication with extraterrestrial life forms. For example, it can analyze signals in different languages ​​and find common patterns. This makes it possible to explore the possibility of communication with extraterrestrial life forms.

[0065] The generative AI analysis unit can use the emotion estimation function to collect the user's emotional reactions to the signals and radio waves analyzed by the generative AI, thereby improving the reliability of the analysis results. The generative AI analysis unit can, for example, use the emotion estimation function to collect the user's emotional reactions to the signals and radio waves analyzed by the generative AI, thereby improving the reliability of the analysis results. For example, the analysis results can be evaluated based on the user's emotional score. In addition, a system can be built that collects the user's emotional reactions to the signals and radio waves analyzed by the generative AI in real time, thereby improving the reliability of the analysis results. For example, the analysis results can be adjusted based on the user's emotional reactions. In addition, the emotion estimation function can be used to collect the user's emotional reactions to the signals and radio waves analyzed by the generative AI, thereby improving the reliability of the analysis results. For example, the analysis results can be evaluated based on the user's emotional score. This can improve the reliability of the analysis results.

[0066] The estimation unit incorporates an emotion estimation function and can emotionally evaluate the possibility of the existence of intelligent life based on the estimation results of the unknown area. The estimation unit, for example, incorporates the emotion estimation function into the generation AI and emotionally evaluates the possibility of the existence of intelligent life based on the estimation results of the unknown area. For example, it analyzes changes in the pattern and intensity of the estimation results and makes an emotional evaluation. Furthermore, using the emotion estimation function, a system is constructed that emotionally evaluates the possibility of the existence of intelligent life from the estimation results of the unknown area. For example, it analyzes the rhythm and tone of the estimation results and makes an emotional evaluation. Furthermore, it emotionally evaluates the possibility of the existence of intelligent life based on the estimation results of the unknown area. For example, it makes an emotional evaluation based on changes in the pattern and intensity of the estimation results. This makes it possible to emotionally evaluate the possibility of the existence of intelligent life.

[0067] The estimation unit can be equipped with a function to integrate astronomical data when estimating unknown areas, thereby providing more accurate estimations. For example, the estimation unit adds a function to integrate other astronomical data to the generation AI, thereby providing more accurate estimations when estimating unknown areas. For example, it integrates star spectral data to improve the accuracy of the estimation results. Furthermore, a system is constructed that provides more accurate estimations when estimating unknown areas, using the function to integrate other astronomical data. For example, it integrates star spectral data to improve the accuracy of the estimation results. Furthermore, a function to integrate other astronomical data is added to the generation AI, thereby providing more accurate estimations when estimating unknown areas. For example, it integrates star spectral data to improve the accuracy of the estimation results. This allows for more accurate estimations.

[0068] The estimation unit has a function for visualizing the estimation results of the unknown area in virtual reality and augmented reality, allowing the user to intuitively understand. For example, the estimation unit adds a function to the generation AI to visualize the estimation results of the unknown area in VR or AR, allowing the user to intuitively understand. For example, the estimation results are displayed as a 3D model and experienced in VR or AR. Furthermore, a system is constructed using the function to visualize the estimation results of the unknown area in VR or AR, allowing the user to intuitively understand. For example, the estimation results are displayed as a 3D model and experienced in VR or AR. Furthermore, a function to visualize the estimation results of the unknown area in VR or AR is added to the generation AI to allow the user to intuitively understand. For example, the estimation results are displayed as a 3D model and experienced in VR or AR. This allows the user to intuitively understand.

[0069] The estimation unit can estimate unknown areas in the Earth's deep sea and underground, aiming to make new discoveries inside the Earth. The estimation unit can, for example, use generation AI to estimate unknown areas in the Earth's deep sea and underground, aiming to make new discoveries inside the Earth. For example, it can analyze signals from the deep sea and underground and estimate unknown areas. A system can also be built to estimate unknown areas in the Earth's deep sea and underground, aiming to make new discoveries inside the Earth. For example, it can analyze signals from the deep sea and underground and estimate unknown areas. A system can also be built to estimate unknown areas in the Earth's deep sea and underground, aiming to make new discoveries inside the Earth. For example, it can analyze signals from the deep sea and underground and estimate unknown areas. A system can also be built to estimate unknown areas in the Earth's deep sea and underground, aiming to make new discoveries inside the Earth. For example, it can analyze signals from the deep sea and underground and estimate unknown areas. This can aim to make new discoveries inside the Earth.

[0070] The estimation unit can analyze data from different planets and star systems and estimate unknown regions outside the solar system. The estimation unit, for example, adds a function to analyze data from different planets and star systems to the generation AI and estimates unknown regions outside the solar system. For example, it analyzes signals from different planets and star systems and estimates unknown regions. Furthermore, a system for estimating unknown regions outside the solar system is constructed using the function to analyze data from different planets and star systems. For example, it analyzes signals from different planets and star systems and estimates unknown regions. Furthermore, a function to analyze data from different planets and star systems is added to the generation AI and estimates unknown regions outside the solar system. For example, it analyzes signals from different planets and star systems and estimates unknown regions. This makes it possible to estimate unknown regions outside the solar system.

[0071] The estimation unit can use the emotion estimation function to collect the user's emotional reactions to the unknown area estimated by the generation AI, thereby improving the reliability of the estimation result. The estimation unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the unknown area estimated by the generation AI, thereby improving the reliability of the estimation result. For example, the estimation result is evaluated based on the user's emotional score. In addition, a system is constructed that collects the user's emotional reactions to the unknown area estimated by the generation AI in real time, thereby improving the reliability of the estimation result. For example, the estimation result is adjusted based on the user's emotional reaction. In addition, the emotion estimation function is used to collect the user's emotional reactions to the unknown area estimated by the generation AI, thereby improving the reliability of the estimation result. For example, the estimation result is evaluated based on the user's emotional score. This can improve the reliability of the estimation result.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The signal collection unit can not only collect signals and radio waves from space, but also signals and radio waves generated by natural phenomena on Earth. For example, it can collect signals generated by earthquakes and volcanic activity to analyze the structure of the Earth's interior. It can also collect radio waves generated by natural phenomena such as lightning and auroras to analyze the state of the Earth's atmosphere and magnetic field. It can also collect signals generated by ocean waves and currents to analyze ocean dynamics. This allows the space analysis system to efficiently collect and analyze information about natural phenomena on Earth.

[0074] The signal collection unit uses the emotion estimation function to not only evaluate the possibility that the source of the signal is an intelligent life form, but also to estimate the emotional state of the intelligent life form. For example, by analyzing the rhythm and tone of the signal, it can estimate whether the intelligent life form is excited or calm. It can also evaluate whether the intelligent life form is happy or sad based on changes in the signal pattern and intensity. Furthermore, by analyzing the emotional characteristics of the signal, it can estimate whether the intelligent life form is friendly or hostile. This makes it possible to not only emotionally evaluate the possibility that the source of the signal is an intelligent life form, but also to estimate its emotional state.

[0075] Signal collectors will be installed not only on the surfaces of the Moon and Mars, but also on the moons of Jupiter and Saturn, allowing data to be collected from a wider range of locations. For example, equipment installed on Jupiter's moon Europa will collect signals from oceans that may exist beneath the ice. Equipment installed on Saturn's moon Titan will also be able to collect signals from methane lakes and rivers. Furthermore, equipment installed on Jupiter and Saturn's moons will be able to collect information on the atmosphere and magnetic field of these moons in real time. This will allow data to be collected from a wider range of locations, allowing for efficient inference of information about unknown regions of space and life forms.

[0076] The signal collection unit can be equipped not only with a multispectral sensor that can simultaneously observe different wavelengths and frequency bands, but also with sensors that can simultaneously observe different physical phenomena. For example, it can be equipped with a gravitational wave sensor to observe gravitational waves from space. It can also be equipped with a neutrino detector to observe neutrinos from space. It can also be equipped with a magnetic field sensor to observe fluctuations in the magnetic field in space. This allows different physical phenomena to be observed simultaneously and more detailed data to be collected.

[0077] Signal collection units can be installed not only on the ocean floor and in polar regions, but also outside the Earth's atmosphere to search for signals that suggest the presence of extraterrestrial life. For example, devices installed outside the Earth's atmosphere can directly collect signals from space. Devices installed outside the Earth's atmosphere can also collect signals without being affected by the Earth's atmosphere or magnetic field. Furthermore, devices installed outside the Earth's atmosphere can collect signals that suggest the presence of extraterrestrial life in real time. This makes it possible to search for signals that suggest the presence of extraterrestrial life.

[0078] The signal collection unit can not only add the function of simultaneously collecting audio and video data, but also simultaneously collect tactile and olfactory data. For example, it can be equipped with a tactile sensor to collect the physical characteristics of the signal source. It can also be equipped with an olfactory sensor to collect the chemical characteristics of the signal source. Furthermore, it is possible to integrate audio, video, tactile, and olfactory data to analyze detailed information about the signal source. This allows analysis to be performed not only from visual and auditory information, but also from tactile and olfactory information.

[0079] The signal collection unit uses the emotion estimation function to monitor the operator's emotions in real time when collecting signals and radio waves, optimizing collection efficiency as well as monitoring the operator's health. For example, it can monitor the operator's heart rate and blood pressure to evaluate their health. It can also monitor the operator's stress level to optimize collection efficiency. Furthermore, it can automatically adjust the settings of the collection device according to the operator's health. This allows for real-time monitoring of the operator's emotions and health to optimize collection efficiency.

[0080] Based on the analysis of signals and radio waves, the Generative AI Analysis Unit can estimate not only the emotional state of the source intelligent life form, but also the culture and technological level of the source intelligent life form. For example, by analyzing the signal pattern and changes in intensity, it can estimate the cultural characteristics of the source intelligent life form. It can also analyze the content and format of the signal to evaluate the technological level of the source intelligent life form. Furthermore, by analyzing the emotional characteristics of the signal, it is possible to estimate the social structure and values ​​of the source intelligent life form. This makes it possible to estimate not only the emotional state of the source intelligent life form, but also its culture and technological level.

[0081] The generative AI analysis unit can compare signals and radio waves with past data to identify abnormal patterns and new discoveries, as well as predict future data. For example, it can predict future signal patterns based on past and current data. It can also compare past and current data to predict future abnormal patterns. Furthermore, predicting future data can increase the possibility of new discoveries. This allows it to not only identify abnormal patterns and new discoveries, but also predict future data.

[0082] The generative AI analysis unit can not only visualize the signal and radio wave analysis results as a 3D model, but also provide the analysis results as audio and haptic feedback. For example, it can express signal patterns and changes in intensity to the user through audio. It can also provide the signal analysis results as haptic feedback, allowing the user to physically feel them. Furthermore, it is possible to combine audio and haptic feedback to make the analysis results more intuitively understandable. This allows the analysis results to be provided not only as a 3D model, but also as audio and haptic feedback.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The signal collector collects signals and radio waves from space. For example, signals and radio waves can be collected using ground observatories, space telescopes, and artificial satellites. The signal collector can also collect signals and radio waves in real time and store them in a database. Step 2: The Generative AI Analysis Unit uses the Generative AI to analyze the signals and radio waves collected by the Signal Collection Unit. For example, the Generative AI uses deep learning and neural networks to analyze the patterns of signals and radio waves and extract unknown information. The Generative AI can also analyze what a specific frequency or pattern means when it is repeated. Step 3: The estimation unit infers information about unknown regions of the universe and life forms based on the results of the analysis by the generation AI analysis unit. For example, if a specific signal is received from a specific direction, it estimates the possibility that an unknown celestial body or region exists in that direction. It can also infer the characteristics and structure of that region from the signal strength and pattern.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the 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.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a signal collection unit that collects signals and radio waves from space; a generation AI analysis unit that analyzes the signals and radio waves collected by the signal collection unit using a generation AI; and an estimation unit that estimates information about unknown regions of the universe and living organisms based on the results of the analysis by the generation AI analysis unit. A system characterized by:

2. The signal collecting unit Emotion estimation function that emotionally evaluates the possibility that the source of the signal is an intelligent life form 2. The system of claim 1.

3. The signal collecting unit It will also be installed on the surface of the Moon and Mars to collect data from a wider area.

2. The system of claim 1.

4. The signal collecting unit Equipped with a multispectral sensor that can simultaneously observe different wavelengths and frequency bands, it will collect more detailed data.

2. The system of claim 1.

5. The signal collecting unit They will also be deployed on the ocean floor and in polar regions to search for signals that could suggest the presence of extraterrestrial life.

2. The system of claim 1.

6. The signal collecting unit Add a function to simultaneously collect audio and video data, and perform the above analysis from visual and audio information.

2. The system of claim 1.

7. The signal collecting unit The emotions of operators are monitored in real time when collecting the signals and radio waves, and collection efficiency is optimized.

2. The system of claim 1.

8. The generation AI analysis unit An emotion estimation function is provided to estimate the emotional state of the intelligent life form that is the source of the signal based on the analysis results of the signal and the radio wave.

2. The system of claim 1.

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