System

Abstract

An object of the system according to the embodiment is to support a physical exercise for a person with a physical disability to express his or her likeness.SOLUTION: A system includes a brain wave acquisition part, a behavior specification part, and a robot control part. The brain wave acquisition unit acquires brain waves of the person. The action identification unit analyzes the brain wave data acquired by the brain wave acquisition unit and identifies an action that the person wants to do. The robot control unit controls the robot device based on the action specified by the action specifying 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 technologies do not adequately provide means to support the physical movements that enable people with physical disabilities to express themselves, and there is room for improvement.

[0005] The system according to the embodiment aims to support physical exercises that allow people with physical disabilities to express their individuality. [Means for solving the problem]

[0006] The system according to the embodiment includes an electroencephalogram (EEG) acquisition unit, a behavior identification unit, and a robot control unit. The EEG acquisition unit acquires the EEG of the individual. The behavior identification unit analyzes the EEG data acquired by the EEG acquisition unit and identifies the behavior the individual wants to perform. The robot control unit controls the robot device based on the behavior identified by the behavior identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can support physical exercises that allow people with physical disabilities to express their individuality. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses AI technology to maximize human emotions and behavior, allowing physically disabled people to express their individuality. This system enables physically disabled people to exercise at a level comparable to that of able-bodied people. This allows physically disabled people to express their individuality and achieve physical exercise at a level comparable to that of able-bodied people.

[0029] The system according to the embodiment includes an EEG acquisition unit, a behavior identification unit, and a robot control unit. The EEG acquisition unit acquires the EEG of the individual. For example, it monitors the individual's EEG in real time using an EEG sensor and collects the data. The EEG acquisition unit can also record the EEG pattern when the individual wants to perform a specific action. The behavior identification unit analyzes the EEG data acquired by the EEG acquisition unit and identifies the behavior the individual wants to perform. For example, the generation AI uses a large amount of EEG data collected from healthy individuals and individuals with disabilities to identify behaviors that match the individual's EEG pattern. The generation AI can also analyze the EEG pattern when the individual thinks, "I want to move my hand," and identify the individual's intention. The robot control unit controls a robot device based on the behavior identified by the behavior identification unit. For example, a device such as a robotic arm or prosthetic limb operates based on the behavior identified by the generation AI. Furthermore, when the individual thinks, "I want to move my hand," the generation AI can identify the individual's intention and cause the robotic arm to actually move the individual's hand. As a result, the system according to the embodiment analyzes the brainwave data of the person, identifies their intention, and controls the robotic device accordingly, allowing the person to express themselves. For example, if the person thinks, "I want to move my hand," the generation AI identifies that intention, and the robotic arm actually moves the hand, allowing the person to move their body as they wish.

[0030] The EEG acquisition unit can simultaneously collect environmental sound or visual information about the person and analyze the effects of environmental factors on their EEG. For example, when acquiring EEG data, the EEG acquisition unit simultaneously collects environmental sounds around the person and analyzes the results in an integrated manner with the EEG data. For example, it analyzes whether the person exhibits different EEG patterns in quiet and noisy environments. To collect visual information, the EEG acquisition unit also installs a camera in the person's field of view and analyzes the EEG data in an integrated manner. For example, it can record the EEG patterns observed when the person views a specific video. To analyze the effects of environmental factors on EEG, the EEG acquisition unit also records the temperature and lighting conditions around the person and compares and analyzes the EEG data. For example, it analyzes the EEG patterns exhibited by the person under different temperature conditions. This allows for more accurate behavior identification by analyzing the effects of environmental factors on EEG.

[0031] The behavior identification unit can improve the accuracy of behavior identification by referring to the person's past behavioral history. For example, when the generation AI analyzes brain wave data, the behavior identification unit references the person's past behavioral history from a database to build a system that improves the accuracy of behavior identification. For example, the behavior identification unit identifies behavior based on actions that the person has taken in the past. The behavior identification unit also analyzes the person's past behavioral history and develops an algorithm that the generation AI uses to identify behavior based on that data. For example, it can reference the brain wave patterns that the person had when they previously thought, "I want to move my hands." The behavior identification unit also develops a system that improves the accuracy of behavior identification by referring to the person's past behavioral history in real time when the generation AI analyzes brain wave data. For example, it identifies behavior based on actions that the person has taken in the past. In this way, the accuracy of behavior identification is improved by referring to past behavioral history.

[0032] The behavior identification unit learns the individual brainwave patterns of the relevant person and can identify behaviors that are individually optimized. For example, the behavior identification unit constructs a system in which a generation AI learns the individual brainwave patterns of the relevant person and, based on that data, identifies behaviors that are individually optimized. For example, it learns the brainwave pattern when the relevant person thinks, "I want to move my hands." The behavior identification unit also analyzes the brainwave data of the relevant person and develops an algorithm that learns the individual brainwave patterns. For example, it can learn the brainwave pattern when the relevant person thinks, "I want to walk." The behavior identification unit also develops a system in which a generation AI learns the individual brainwave patterns of the relevant person in real time and identifies behaviors that are individually optimized. For example, it learns the brainwave pattern when the relevant person thinks, "I want to lift something." In this way, individually optimized behavior identification becomes possible by learning individual brainwave patterns.

[0033] The robot control unit can optimize the movement to more naturally reflect the person's intentions. For example, the robot control unit builds a system that optimizes the movement of the robot device to more naturally reflect the person's intentions, based on the behavior identified by the generation AI. For example, when the person thinks, "I want to move my hand," the robot arm moves naturally. The robot control unit also develops an algorithm that adjusts the movement of the robot device in real time to more naturally reflect the person's intentions. For example, when the person thinks, "I want to walk," the robotic prosthetic leg can also perform natural walking movements. The robot control unit also builds a feedback system to optimize the movement of the robot device, based on the behavior identified by the generation AI. For example, when the person thinks, "I want to lift something," the robotic arm moves with an appropriate amount of force. This optimizes the movement of the robot device by more naturally reflecting the person's intentions.

[0034] The robot control unit can adjust the movement in real time to match the body movements of the person. For example, the robot control unit builds a system in which the robot device is adjusted in real time to match the body movements of the person based on the behavior identified by the generation AI. For example, when the person thinks, "I want to move my hand," the robot arm instantly adjusts its movement. The robot control unit also develops an algorithm that adjusts the movement of the robot device in real time to match the body movements of the person. For example, when the person thinks, "I want to walk," the robotic prosthetic leg can instantly adjust its walking movement. The robot control unit also builds a feedback system to adjust the movement of the robot device in real time based on the behavior identified by the generation AI. For example, when the person thinks, "I want to lift something," the robotic arm instantly adjusts the force. This allows for real-time adjustments to match the body movements of the person, enabling more natural movements.

[0035] The robot control unit can adjust the behavior to adapt to different environments and situations of the person. For example, the robot control unit builds a system in which a robotic device is designed to adapt to different environments and situations based on the behavior identified by the generative AI. For example, a robotic arm that performs different actions indoors and outdoors can be developed. The robot control unit also develops an algorithm that adjusts the behavior of the robotic device in real time to adapt to different environments and situations. For example, a robotic prosthetic leg that performs different actions on flat ground and uneven ground can be developed. The robot control unit also builds a feedback system for the robotic device to adapt to different environments and situations based on the behavior identified by the generative AI. For example, a robotic arm that responds to changes in temperature and humidity can be developed. This allows the robotic device to operate more flexibly by adapting to different environments and situations.

[0036] The robot control unit can support different types of physical movements of the individual. For example, based on the behavior identified by the generative AI, the robot control unit builds a system in which a robotic device supports different types of physical movements. For example, it develops a robotic arm that performs movements such as walking, lifting, and manipulation. The robot control unit also develops an algorithm that adjusts the behavior of the robotic device in real time to support different types of physical movements. For example, it can develop a robotic prosthetic leg that can switch between walking and lifting movements. The robot control unit also builds a feedback system in which a robotic device supports different types of physical movements based on the behavior identified by the generative AI. For example, it develops a robotic arm that can switch between manipulating and lifting movements. This allows for a greater variety of movements by supporting different types of physical movements.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The behavior identification unit can acquire biosignals such as heart rate and electrodermal response in addition to the brain wave data of the person in question, and can integrate these data to identify behavior. For example, it can analyze the increase in heart rate and changes in electrodermal response when the person in question is nervous, and combine this data with the brain wave data to identify behavior. The behavior identification unit can also monitor the person's biosignals in real time and develop an algorithm to identify behavior based on that data. For example, it can learn the biosignal patterns when the person in question is relaxed, and identify behavior based on that data. Furthermore, the behavior identification unit can collect the person's biosignals over a long period of time and improve the accuracy of behavior identification based on that data. In this way, by integrating the person's biosignals, it becomes possible to identify behavior with higher accuracy.

[0039] The EEG acquisition unit can acquire environmental data such as the ambient temperature and humidity in addition to the EEG data of the individual, and can integrate this data to analyze the impact of environmental factors on EEG. For example, the EEG acquisition unit can analyze the EEG patterns exhibited by the individual in a high-temperature environment and identify behavior based on the data. The EEG acquisition unit can also monitor environmental data around the individual in real time and develop an algorithm that analyzes the impact of environmental factors on EEG based on the data. For example, the EEG acquisition unit can learn the EEG patterns exhibited by the individual in a low-humidity environment and identify behavior based on the data. Furthermore, the EEG acquisition unit can collect environmental data around the individual over a long period of time and analyze the impact of environmental factors on EEG based on the data. In this way, integrating environmental data enables more accurate behavior identification.

[0040] The behavior identification unit can monitor the person's physical movements in addition to the person's brain wave data and integrate these data to identify the person's behavior. For example, it can analyze the person's physical movements when they move their hands and combine this data with the brain wave data to identify the person's behavior. The behavior identification unit can also monitor the person's physical movements in real time and develop an algorithm to identify the person's behavior based on the data. For example, it can learn the person's physical movements while walking and identify the person's behavior based on the data. Furthermore, the behavior identification unit can collect the person's physical movements over a long period of time and improve the accuracy of behavior identification based on the data. In this way, by integrating the person's physical movements, it becomes possible to identify the person's behavior with higher accuracy.

[0041] The robot control unit can acquire the subject's myoelectric potential signals and optimize the robotic device's operation based on this data to more naturally reflect the subject's intentions. For example, it can analyze the myoelectric potential signals when the subject moves their hand and adjust the robotic arm's operation based on that data. The robot control unit can also monitor the subject's myoelectric potential signals in real time and develop algorithms to optimize the robotic device's operation based on that data. For example, it can learn the myoelectric potential signals the subject exhibits while walking and adjust the operation of the robotic prosthetic leg based on that data. Furthermore, the robot control unit can collect the subject's myoelectric potential signals over a long period of time and optimize the robotic device's operation based on that data. This allows for more natural movement by integrating the subject's myoelectric potential signals.

[0042] The robot control unit can acquire the person's joint angle and muscle strength data to adjust the robotic device's movement in real time to match the person's body movements, and can optimize the robotic device's movement based on this data. For example, the robot control unit can analyze the joint angle and muscle strength data when the person moves their hand and adjust the movement of the robotic arm based on that data. The robot control unit can also monitor the person's joint angle and muscle strength data in real time and develop an algorithm to optimize the robotic device's movement based on that data. For example, the robot control unit can learn the joint angle and muscle strength data displayed by the person while walking and adjust the movement of the robotic prosthetic leg based on that data. Furthermore, the robot control unit can collect the person's joint angle and muscle strength data over a long period of time and optimize the robotic device's movement based on that data. In this way, by integrating the person's joint angle and muscle strength data, more natural movement is possible.

[0043] The robot control unit can acquire the individual's location information and movement speed to adapt to different environments and situations, and can optimize the robot device's operation based on this data. For example, the robot control unit can analyze the individual's location information and movement speed when moving indoors and adjust the robot arm's operation based on that data. The robot control unit can also monitor the individual's location information and movement speed in real time and develop algorithms to optimize the robot device's operation based on that data. For example, the robot control unit can learn the individual's location information and movement speed when moving outdoors and adjust the operation of the robotic prosthetic leg based on that data. Furthermore, the robot control unit can collect the individual's location information and movement speed over a long period of time and optimize the robot device's operation based on that data. This allows for more flexible operation by integrating the individual's location information and movement speed.

[0044] The robot control unit can acquire the individual's strength training data and optimize the robotic device's movements based on this data to support different types of physical movements of the individual. For example, the robot control unit can analyze data from the individual's strength training and adjust the movement of the robotic arm based on that data. The robot control unit can also monitor the individual's strength training data in real time and develop algorithms to optimize the movement of the robotic device based on that data. For example, the robot control unit can learn data from the individual's strength training and adjust the movement of the robotic prosthetic leg based on that data. Furthermore, the robot control unit can collect the individual's strength training data over a long period of time and optimize the movement of the robotic device based on that data. In this way, a wider variety of movements can be achieved by integrating the individual's strength training data.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The brain wave acquisition unit acquires the brain waves of the person in question. For example, an brain wave sensor is used to monitor the person's brain waves in real time and collect the data. The brain wave acquisition unit can also record the brain wave patterns when the person in question wants to perform a specific action. Step 2: The behavior identification unit analyzes the brainwave data acquired by the brainwave acquisition unit and identifies the behavior the person wants to perform. For example, the generation AI uses a huge amount of brainwave data collected from healthy and disabled people to identify behaviors that match the person's brainwave patterns. The generation AI can also analyze the brainwave patterns when the person thinks, "I want to move my hands," and identify their intention. Step 3: The robot control unit controls the robot device based on the behavior identified by the behavior identification unit. For example, devices such as a robot arm or prosthetic limb operate based on the behavior identified by the generation AI. Also, if the person wants to "move their hand," the robot control unit can have the generation AI identify that intention and have the robot arm actually move their hand.

[0047] (Example 2) A system according to an embodiment of the present invention uses AI technology to maximize human emotions and behavior, allowing physically disabled people to express their individuality. This system enables physically disabled people to exercise at a level comparable to that of able-bodied people. This allows physically disabled people to express their individuality and achieve physical exercise at a level comparable to that of able-bodied people.

[0048] The system according to the embodiment includes an EEG acquisition unit, a behavior identification unit, and a robot control unit. The EEG acquisition unit acquires the EEG of the individual. For example, it monitors the individual's EEG in real time using an EEG sensor and collects the data. The EEG acquisition unit can also record the EEG pattern when the individual wants to perform a specific action. The behavior identification unit analyzes the EEG data acquired by the EEG acquisition unit and identifies the behavior the individual wants to perform. For example, the generation AI uses a large amount of EEG data collected from healthy individuals and individuals with disabilities to identify behaviors that match the individual's EEG pattern. The generation AI can also analyze the EEG pattern when the individual thinks, "I want to move my hand," and identify the individual's intention. The robot control unit controls a robot device based on the behavior identified by the behavior identification unit. For example, a device such as a robotic arm or prosthetic limb operates based on the behavior identified by the generation AI. Furthermore, when the individual thinks, "I want to move my hand," the generation AI can identify the individual's intention and cause the robotic arm to actually move the individual's hand. As a result, the system according to the embodiment analyzes the brainwave data of the person, identifies their intention, and controls the robotic device accordingly, allowing the person to express themselves. For example, if the person thinks, "I want to move my hand," the generation AI identifies that intention, and the robotic arm actually moves the hand, allowing the person to move their body as they wish.

[0049] The EEG acquisition unit can simultaneously record the emotional state of the person and analyze the relationship between emotion and behavior. The EEG acquisition unit, for example, builds a system that records the emotional state of the person in real time when acquiring EEG data. For example, it records EEG patterns when the person feels a specific emotion and analyzes the data. In addition, to record the emotional state, the EEG acquisition unit simultaneously collects the person's facial expressions and voice data and analyzes the data in combination with the EEG data. For example, it can record EEG patterns when the person smiles. In addition, in order to quantify the emotional state, the EEG acquisition unit collects self-reported data from the person and compares and analyzes it with the EEG data. For example, it records EEG patterns when the person feels "happy" and analyzes the data. In this way, by recording the person's emotional state and analyzing the relationship between emotion and behavior, it is possible to identify behavior with higher accuracy.

[0050] The EEG acquisition unit can simultaneously collect environmental sound or visual information about the person and analyze the effects of environmental factors on their EEG. For example, when acquiring EEG data, the EEG acquisition unit simultaneously collects environmental sounds around the person and analyzes the results in an integrated manner with the EEG data. For example, it analyzes whether the person exhibits different EEG patterns in quiet and noisy environments. To collect visual information, the EEG acquisition unit also installs a camera in the person's field of view and analyzes the EEG data in an integrated manner. For example, it can record the EEG patterns observed when the person views a specific video. To analyze the effects of environmental factors on EEG, the EEG acquisition unit also records the temperature and lighting conditions around the person and compares and analyzes the EEG data. For example, it analyzes the EEG patterns exhibited by the person under different temperature conditions. This allows for more accurate behavior identification by analyzing the effects of environmental factors on EEG.

[0051] The behavior identification unit can improve the accuracy of behavior identification by referring to the person's past behavioral history. For example, when the generation AI analyzes brain wave data, the behavior identification unit references the person's past behavioral history from a database to build a system that improves the accuracy of behavior identification. For example, the behavior identification unit identifies behavior based on actions that the person has taken in the past. The behavior identification unit also analyzes the person's past behavioral history and develops an algorithm that the generation AI uses to identify behavior based on that data. For example, it can reference the brain wave patterns that the person had when they previously thought, "I want to move my hands." The behavior identification unit also develops a system that improves the accuracy of behavior identification by referring to the person's past behavioral history in real time when the generation AI analyzes brain wave data. For example, it identifies behavior based on actions that the person has taken in the past. In this way, the accuracy of behavior identification is improved by referring to past behavioral history.

[0052] The behavior identification unit learns the individual brainwave patterns of the relevant person and can identify behaviors that are individually optimized. For example, the behavior identification unit constructs a system in which a generation AI learns the individual brainwave patterns of the relevant person and, based on that data, identifies behaviors that are individually optimized. For example, it learns the brainwave pattern when the relevant person thinks, "I want to move my hands." The behavior identification unit also analyzes the brainwave data of the relevant person and develops an algorithm that learns the individual brainwave patterns. For example, it can learn the brainwave pattern when the relevant person thinks, "I want to walk." The behavior identification unit also develops a system in which a generation AI learns the individual brainwave patterns of the relevant person in real time and identifies behaviors that are individually optimized. For example, it learns the brainwave pattern when the relevant person thinks, "I want to lift something." In this way, individually optimized behavior identification becomes possible by learning individual brainwave patterns.

[0053] The robot control unit can optimize the movement to more naturally reflect the person's intentions. For example, the robot control unit builds a system that optimizes the movement of the robot device to more naturally reflect the person's intentions, based on the behavior identified by the generation AI. For example, when the person thinks, "I want to move my hand," the robot arm moves naturally. The robot control unit also develops an algorithm that adjusts the movement of the robot device in real time to more naturally reflect the person's intentions. For example, when the person thinks, "I want to walk," the robotic prosthetic leg can also perform natural walking movements. The robot control unit also builds a feedback system to optimize the movement of the robot device, based on the behavior identified by the generation AI. For example, when the person thinks, "I want to lift something," the robotic arm moves with an appropriate amount of force. This optimizes the movement of the robot device by more naturally reflecting the person's intentions.

[0054] The robot control unit can adjust the movement in real time to match the body movements of the person. For example, the robot control unit builds a system in which the robot device is adjusted in real time to match the body movements of the person based on the behavior identified by the generation AI. For example, when the person thinks, "I want to move my hand," the robot arm instantly adjusts its movement. The robot control unit also develops an algorithm that adjusts the movement of the robot device in real time to match the body movements of the person. For example, when the person thinks, "I want to walk," the robotic prosthetic leg can instantly adjust its walking movement. The robot control unit also builds a feedback system to adjust the movement of the robot device in real time based on the behavior identified by the generation AI. For example, when the person thinks, "I want to lift something," the robotic arm instantly adjusts the force. This allows for real-time adjustments to match the body movements of the person, enabling more natural movements.

[0055] The robot control unit can adjust the operation according to the emotional state of the person and increase the emotional satisfaction. The robot control unit, for example, uses an emotion estimation function to build a system that adjusts the operation of the robot device according to the emotional state of the person. For example, when the person feels "joy," the robot arm moves more smoothly. The robot control unit can also analyze the emotional state of the person in real time and develop an algorithm that adjusts the operation of the robot device based on the data. For example, when the person feels "surprise," the robot arm can instantly adjust its operation. The robot control unit can also use the emotion estimation function to build a system that monitors the emotional state of the person in real time and adjusts the operation of the robot device based on the data. For example, when the person feels "sadness," the robot arm moves more carefully. In this way, adjusting the operation according to the emotional state of the person increases emotional satisfaction.

[0056] The robot control unit can adjust the behavior to adapt to different environments and situations of the person. For example, the robot control unit builds a system in which a robotic device is designed to adapt to different environments and situations based on the behavior identified by the generative AI. For example, a robotic arm that performs different actions indoors and outdoors can be developed. The robot control unit also develops an algorithm that adjusts the behavior of the robotic device in real time to adapt to different environments and situations. For example, a robotic prosthetic leg that performs different actions on flat ground and uneven ground can be developed. The robot control unit also builds a feedback system for the robotic device to adapt to different environments and situations based on the behavior identified by the generative AI. For example, a robotic arm that responds to changes in temperature and humidity can be developed. This allows the robotic device to operate more flexibly by adapting to different environments and situations.

[0057] The robot control unit can support different types of physical movements of the individual. For example, based on the behavior identified by the generative AI, the robot control unit builds a system in which a robotic device supports different types of physical movements. For example, it develops a robotic arm that performs movements such as walking, lifting, and manipulation. The robot control unit also develops an algorithm that adjusts the behavior of the robotic device in real time to support different types of physical movements. For example, it can develop a robotic prosthetic leg that can switch between walking and lifting movements. The robot control unit also builds a feedback system in which a robotic device supports different types of physical movements based on the behavior identified by the generative AI. For example, it develops a robotic arm that can switch between manipulating and lifting movements. This allows for a greater variety of movements by supporting different types of physical movements.

[0058] The robot control unit can adjust the operation according to the emotional state of the person and increase the emotional satisfaction. The robot control unit, for example, uses an emotion estimation function to build a system that adjusts the operation of the robot device according to the emotional state of the person. For example, when the person feels "joy," the robot arm moves more smoothly. The robot control unit can also analyze the emotional state of the person in real time and develop an algorithm that adjusts the operation of the robot device based on the data. For example, when the person feels "surprise," the robot arm can instantly adjust its operation. The robot control unit can also use the emotion estimation function to build a system that monitors the emotional state of the person in real time and adjusts the operation of the robot device based on the data. For example, when the person feels "sadness," the robot arm moves more carefully. In this way, adjusting the operation according to the emotional state of the person increases emotional satisfaction.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The behavior identification unit can acquire biosignals such as heart rate and electrodermal response in addition to the brain wave data of the person in question, and can integrate these data to identify behavior. For example, it can analyze the increase in heart rate and changes in electrodermal response when the person in question is nervous, and combine this data with the brain wave data to identify behavior. The behavior identification unit can also monitor the person's biosignals in real time and develop an algorithm to identify behavior based on that data. For example, it can learn the biosignal patterns when the person in question is relaxed, and identify behavior based on that data. Furthermore, the behavior identification unit can collect the person's biosignals over a long period of time and improve the accuracy of behavior identification based on that data. In this way, by integrating the person's biosignals, it becomes possible to identify behavior with higher accuracy.

[0061] The EEG acquisition unit can also use voice recognition technology to analyze the person's tone of voice and speaking style to estimate their emotional state. For example, it can analyze changes in the person's tone of voice when they are excited and integrate that data with EEG data to estimate their emotional state. The EEG acquisition unit can also monitor the person's tone of voice and speaking style in real time and develop an algorithm to estimate their emotional state based on that data. For example, it can learn the tone of voice when the person is calm and estimate their emotional state based on that data. Furthermore, the EEG acquisition unit can collect the person's tone of voice and speaking style over a long period of time and use that data to improve the accuracy of emotion estimation. This allows for more accurate emotion estimation by analyzing the person's tone of voice and speaking style.

[0062] The EEG acquisition unit can acquire environmental data such as the ambient temperature and humidity in addition to the EEG data of the individual, and can integrate this data to analyze the impact of environmental factors on EEG. For example, the EEG acquisition unit can analyze the EEG patterns exhibited by the individual in a high-temperature environment and identify behavior based on the data. The EEG acquisition unit can also monitor environmental data around the individual in real time and develop an algorithm that analyzes the impact of environmental factors on EEG based on the data. For example, the EEG acquisition unit can learn the EEG patterns exhibited by the individual in a low-humidity environment and identify behavior based on the data. Furthermore, the EEG acquisition unit can collect environmental data around the individual over a long period of time and analyze the impact of environmental factors on EEG based on the data. In this way, integrating environmental data enables more accurate behavior identification.

[0063] The behavior identification unit can also refer to the person's past emotional states in addition to the person's brain wave data to improve the accuracy of behavior identification. For example, it can refer to the brain wave patterns of the person when he or she felt "joy" in the past and identify behavior based on that data. The behavior identification unit can also analyze the person's past emotional states and develop an algorithm for behavior identification based on that data. For example, it can refer to the brain wave patterns of the person when he or she felt "surprise" in the past. Furthermore, the behavior identification unit can develop a system that refers to the person's past emotional states in real time to improve the accuracy of behavior identification. In this way, the accuracy of behavior identification is improved by referring to past emotional states.

[0064] The behavior identification unit can monitor the person's physical movements in addition to the person's brain wave data and integrate these data to identify the person's behavior. For example, it can analyze the person's physical movements when they move their hands and combine this data with the brain wave data to identify the person's behavior. The behavior identification unit can also monitor the person's physical movements in real time and develop an algorithm to identify the person's behavior based on the data. For example, it can learn the person's physical movements while walking and identify the person's behavior based on the data. Furthermore, the behavior identification unit can collect the person's physical movements over a long period of time and improve the accuracy of behavior identification based on the data. In this way, by integrating the person's physical movements, it becomes possible to identify the person's behavior with higher accuracy.

[0065] The robot control unit can acquire the subject's myoelectric potential signals and optimize the robotic device's operation based on this data to more naturally reflect the subject's intentions. For example, it can analyze the myoelectric potential signals when the subject moves their hand and adjust the robotic arm's operation based on that data. The robot control unit can also monitor the subject's myoelectric potential signals in real time and develop algorithms to optimize the robotic device's operation based on that data. For example, it can learn the myoelectric potential signals the subject exhibits while walking and adjust the operation of the robotic prosthetic leg based on that data. Furthermore, the robot control unit can collect the subject's myoelectric potential signals over a long period of time and optimize the robotic device's operation based on that data. This allows for more natural movement by integrating the subject's myoelectric potential signals.

[0066] The robot control unit can acquire the person's joint angle and muscle strength data to adjust the robotic device's movement in real time to match the person's body movements, and can optimize the robotic device's movement based on this data. For example, the robot control unit can analyze the joint angle and muscle strength data when the person moves their hand and adjust the movement of the robotic arm based on that data. The robot control unit can also monitor the person's joint angle and muscle strength data in real time and develop an algorithm to optimize the robotic device's movement based on that data. For example, the robot control unit can learn the joint angle and muscle strength data displayed by the person while walking and adjust the movement of the robotic prosthetic leg based on that data. Furthermore, the robot control unit can collect the person's joint angle and muscle strength data over a long period of time and optimize the robotic device's movement based on that data. In this way, by integrating the person's joint angle and muscle strength data, more natural movement is possible.

[0067] The robot control unit can estimate the emotional state of the person using facial expression recognition technology and optimize the operation of the robot device based on the data in order to adjust the operation according to the person's emotional state. For example, when the person smiles, the robot arm moves more smoothly. The robot control unit can also monitor the person's facial expressions in real time and develop an algorithm to estimate the emotional state based on the data. For example, the robot control unit can analyze the person's facial expression when surprised and adjust the operation of the robot arm based on the data. Furthermore, the robot control unit can collect facial expression data of the person over a long period of time and improve the accuracy of emotion estimation based on the data. This allows for more accurate emotion estimation by analyzing the person's facial expressions.

[0068] The robot control unit can acquire the individual's location information and movement speed to adapt to different environments and situations, and can optimize the robot device's operation based on this data. For example, the robot control unit can analyze the individual's location information and movement speed when moving indoors and adjust the robot arm's operation based on that data. The robot control unit can also monitor the individual's location information and movement speed in real time and develop algorithms to optimize the robot device's operation based on that data. For example, the robot control unit can learn the individual's location information and movement speed when moving outdoors and adjust the operation of the robotic prosthetic leg based on that data. Furthermore, the robot control unit can collect the individual's location information and movement speed over a long period of time and optimize the robot device's operation based on that data. This allows for more flexible operation by integrating the individual's location information and movement speed.

[0069] The robot control unit can acquire the individual's strength training data and optimize the robotic device's movements based on this data to support different types of physical movements of the individual. For example, the robot control unit can analyze data from the individual's strength training and adjust the movement of the robotic arm based on that data. The robot control unit can also monitor the individual's strength training data in real time and develop algorithms to optimize the movement of the robotic device based on that data. For example, the robot control unit can learn data from the individual's strength training and adjust the movement of the robotic prosthetic leg based on that data. Furthermore, the robot control unit can collect the individual's strength training data over a long period of time and optimize the movement of the robotic device based on that data. In this way, a wider variety of movements can be achieved by integrating the individual's strength training data.

[0070] The robot control unit can acquire the pulse and blood pressure of the individual in question and optimize the robot device's operation based on this data in order to adjust the operation according to the individual's emotional state. For example, it can analyze changes in the individual's pulse and blood pressure when the individual is excited and adjust the operation of the robot arm based on this data. The robot control unit can also monitor the individual's pulse and blood pressure in real time and develop an algorithm to estimate the individual's emotional state based on this data. For example, it can learn the individual's pulse and blood pressure when the individual is relaxed and adjust the operation of the robot arm based on this data. Furthermore, the robot control unit can collect the individual's pulse and blood pressure over a long period of time and improve the accuracy of emotion estimation based on this data. This allows for more accurate emotion estimation by analyzing the individual's pulse and blood pressure.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The brain wave acquisition unit acquires the brain waves of the person in question. For example, an brain wave sensor is used to monitor the person's brain waves in real time and collect the data. The brain wave acquisition unit can also record the brain wave patterns when the person in question wants to perform a specific action. Step 2: The behavior identification unit analyzes the brainwave data acquired by the brainwave acquisition unit and identifies the behavior the person wants to perform. For example, the generation AI uses a huge amount of brainwave data collected from healthy and disabled people to identify behaviors that match the person's brainwave patterns. The generation AI can also analyze the brainwave patterns when the person thinks, "I want to move my hands," and identify their intention. Step 3: The robot control unit controls the robot device based on the behavior identified by the behavior identification unit. For example, devices such as a robot arm or prosthetic limb operate based on the behavior identified by the generation AI. Also, if the person wants to "move their hand," the robot control unit can have the generation AI identify that intention and have the robot arm actually move their hand.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an electroencephalogram acquisition unit for acquiring the electroencephalogram of the person; a behavior identification unit that analyzes the electroencephalogram data acquired by the electroencephalogram acquisition unit and identifies a behavior that the person wants to do; a robot control unit that controls a robot device based on the behavior identified by the behavior identifying unit. A system characterized by:

2. The electroencephalogram acquisition unit The emotional state of the person is simultaneously recorded and the relationship between emotions and behavior is analyzed.

2. The system of claim 1.

3. The electroencephalogram acquisition unit Environmental sound or visual information of the person is also collected at the same time, and the influence of environmental factors on the brain waves is analyzed.

2. The system of claim 1.

4. The behavior identification unit Refer to the past behavioral history of the person in question to improve the accuracy of behavior identification 2. The system of claim 1.

5. The behavior identification unit The individual brain wave patterns of the individual are learned, and individually optimized behavior is identified.

2. The system of claim 1.

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