System

Abstract

An object of a system according to an embodiment is to efficiently automate an existing machine.SOLUTION: A system according to an embodiment includes a generation AI, an electric signal generation unit, and a machine control unit. The generation AI generates an electrical signal. The electric signal generation unit causes the electric signal generated by the generation AI to flow into the substrate. The machine controller controls the existing machine using electrical signals injected into the board.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the automation of existing machines has not progressed sufficiently, making it difficult to control them efficiently.

[0005] The system according to the embodiment aims to efficiently automate existing machines. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, an electrical signal generation unit, and a machine control unit. The generation AI generates an electrical signal. The electrical signal generation unit inputs the electrical signal generated by the generation AI into a circuit board. The machine control unit controls an existing machine using the electrical signal input into the circuit board. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently automate existing machinery. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The automation system according to the embodiment of the present invention is a system in which a generating AI generates an electric signal and sends the electric signal to a base to operate an existing machine. In this way, the automation system can automate an existing machine using the generating AI.

[0029] An automation system according to an embodiment includes a generation AI, an electrical signal generation unit, a board, and a machine control unit. The generation AI generates electrical signals required for machine operation. For example, the generation AI generates control signals for a tractor's engine, steering, brakes, etc. The generation AI can also generate control signals for a truck's engine, steering, brakes, etc. The generation AI can also generate control signals for factory machines. For example, the generation AI generates control signals for a tractor's engine, steering, brakes, etc. The electrical signal generation unit sends the electrical signals generated by the generation AI to the board. For example, the electrical signal generation unit sends the electrical signals generated by the generation AI to the board to control the tractor's engine, steering, brakes, etc. The electrical signal generation unit can also send the electrical signals generated by the generation AI to the board to control the truck's engine, steering, brakes, etc. The electrical signal generation unit can also send the electrical signals generated by the generation AI to the board to control the operation of factory machines. The board receives the electrical signals generated by the generation AI and controls existing machines via the machine control unit. For example, the platform receives the electrical signals generated by the generation AI and controls the engine, steering, brakes, etc. of a tractor. The platform can also receive the electrical signals generated by the generation AI and control the engine, steering, brakes, etc. of a truck. The platform can also receive the electrical signals generated by the generation AI and control the operation of factory machinery. The machine control unit controls existing machinery using the electrical signals input into the platform. For example, the machine control unit controls the engine, steering, brakes, etc. of a tractor using the electrical signals input into the platform. The machine control unit can also control the engine, steering, brakes, etc. of a truck using the electrical signals input into the platform. The machine control unit can also control the operation of factory machinery using the electrical signals input into the platform. As a result, the automation system according to the embodiment can automate existing machinery using the generation AI. For example, the automation of a tractor can be achieved by inputting the electrical signals generated by the generation AI into the platform and controlling the engine, steering, brakes, etc. of the tractor.In addition, automated truck driving becomes possible by feeding the electrical signals generated by the generative AI into a circuit board to control the truck's engine, steering, brakes, etc. In addition, automation of factory machinery becomes possible by feeding the electrical signals generated by the generative AI into a circuit board to control the operation of factory machinery.

[0030] The generation AI can generate control signals for the tractor's engine, steering, brakes, etc. The generation AI can generate control signals for the tractor's engine, steering, brakes, etc. For example, the generation AI can generate a signal to control the output of the tractor's engine. The generation AI can also generate a signal to control the tractor's steering angle. The generation AI can also generate a control signal for the tractor's brakes. This makes it possible to automate tractors.

[0031] The generation AI can generate control signals for the truck's engine, steering, brakes, etc. The generation AI can generate control signals for the truck's engine, steering, brakes, etc. For example, the generation AI can generate a signal to control the output of the truck's engine. The generation AI can also generate a signal to control the steering angle of the truck. The generation AI can also generate a control signal for the truck's brakes. This makes it possible for trucks to drive autonomously.

[0032] The generative AI can generate control signals for factory machines. The generative AI generates control signals for factory machines, for example. For example, the generative AI generates signals that control the operation of factory machines. The generative AI can also learn the operation patterns of factory machines and generate control signals based on those patterns. The generative AI can also generate control signals to optimize the operation of factory machines. This enables the automation of factory machines.

[0033] When learning a machine's operating patterns, generative AI incorporates an anomaly detection function and can automatically correct abnormal operating patterns. For example, when learning a machine's operating patterns, generative AI incorporates an anomaly detection function. For example, when learning a tractor's operating patterns, generative AI can detect abnormal vibrations or sounds and automatically adjust the engine output. When learning a truck's operating patterns, generative AI can also detect abnormal behavior and automatically adjust the steering angle. When learning the operating patterns of factory machinery, generative AI can also detect abnormal behavior and automatically correct it. This allows the machine to operate stably by automatically correcting abnormal operating patterns.

[0034] The generative AI receives feedback in real time on the electrical signals it generates and is able to optimize its operation. The generative AI receives feedback in real time on the electrical signals it generates. For example, the generative AI receives feedback in real time while a tractor is operating and optimizes engine output and steering angle. The generative AI can also receive feedback in real time while a truck is operating and optimize brake control. The generative AI can also receive feedback in real time while factory machinery is operating and optimize its operation. This makes it possible to receive feedback in real time and optimize operation.

[0035] Generative AI can generalize the electrical signals it generates so that they can be applied to different types of machines. For example, generative AI can learn the movement patterns of a household robot and apply the generated electrical signals to the household robot. Generative AI can also learn the movement patterns of medical equipment and apply the generated electrical signals to the medical equipment. Generative AI can also learn the movement patterns of industrial robots and apply the generated electrical signals to the industrial robot. This generalization so that it can be applied to different types of machines makes automation possible in a wide range of fields.

[0036] Generative AI can generate electrical signals based on voice commands or gesture input, enabling more intuitive operation. Generative AI generates electrical signals based on voice commands or gesture input, for example. For example, generative AI can analyze voice commands and generate electrical signals that control the operation of a tractor based on the voice commands. Generative AI can also analyze gesture input and generate electrical signals that control the operation of a truck based on the voice commands or gesture input. Generative AI can also generate electrical signals that control the operation of factory machinery based on voice commands or gesture input. This allows for more intuitive operation by generating electrical signals based on voice commands or gesture input.

[0037] The electric signal generation unit can automatically adjust the strength and frequency of the generated electric signal to achieve optimal operation. The electric signal generation unit automatically adjusts the strength and frequency of the generated electric signal, for example. For example, the electric signal generation unit can automatically adjust the strength of the electric signal generated by the generation AI to optimize the output of a tractor engine. The electric signal generation unit can also automatically adjust the frequency of the electric signal generated by the generation AI to optimize the steering angle of a truck. The electric signal generation unit can also automatically adjust the strength and frequency of the electric signal generated by the generation AI to optimize the operation of factory machinery. In this way, optimal operation can be achieved by automatically adjusting the strength and frequency of the electric signal.

[0038] The electrical signal generation unit can increase versatility by making the generated electrical signal compatible with different communication protocols. The electrical signal generation unit, for example, makes the generated electrical signal compatible with different communication protocols. For example, the electrical signal generation unit can make the electrical signal generated by the generation AI compatible with Bluetooth. The electrical signal generation unit can also make the electrical signal generated by the generation AI compatible with Wi-Fi. The electrical signal generation unit can also control the operation of factory machines by making the electrical signal generated by the generation AI compatible with different communication protocols. This makes it possible to increase versatility by making the signal compatible with different communication protocols.

[0039] The electrical signal generation unit can extend battery life by sending the generated electrical signals to a circuit board in an energy-efficient manner. For example, the electrical signal generation unit sends the generated electrical signals to a circuit board in an energy-efficient manner. For example, the electrical signal generation unit sends electrical signals generated by the generation AI to a circuit board using a low-power communication protocol to control the engine, steering, brakes, etc. of a tractor. The electrical signal generation unit can also send electrical signals generated by the generation AI to a circuit board in an energy-efficient manner to control the engine, steering, brakes, etc. of a truck. The electrical signal generation unit can also send electrical signals generated by the generation AI to a circuit board in an energy-efficient manner to control the operation of factory machinery. This makes it possible to extend battery life by sending electrical signals in an energy-efficient manner.

[0040] Generative AI can automatically generate maintenance schedules for machinery and achieve preventive maintenance. Generative AI can, for example, automatically generate maintenance schedules for machinery. For example, generative AI can analyze tractor operation data, predict when maintenance will be required, and automatically generate a schedule. Generative AI can also analyze truck operation data, predict when maintenance will be required, and automatically generate a schedule. Generative AI can also analyze factory machine operation data, predict when maintenance will be required, and automatically generate a schedule. In this way, by automatically generating maintenance schedules for machinery, preventive maintenance becomes possible.

[0041] Generative AI can incorporate safety features that monitor the operation of machinery and automatically stop it if an abnormality is detected. Generative AI can, for example, monitor the operation of machinery and incorporate safety features that automatically stop it if an abnormality is detected. For example, generative AI can monitor the operation of a tractor and automatically stop the engine if abnormal vibrations or sounds are detected. Generative AI can also monitor the operation of a truck and automatically apply the brakes if abnormal behavior is detected. Generative AI can also monitor the operation of factory machinery and incorporate safety features that automatically stop it if an abnormality is detected. This makes it possible to ensure safety by monitoring the operation of machinery and automatically stopping it if an abnormality is detected.

[0042] Generative AI can apply the automation of existing machinery to different industries, achieving efficiency in a wide range of fields. Generative AI can, for example, apply the automation of existing machinery to different industries. For example, generative AI can realize the automation of agricultural machinery, improving efficiency in the agricultural field. Generative AI can also realize the automation of logistics machinery, improving efficiency in the logistics field. Generative AI can also realize the automation of manufacturing machinery, improving efficiency in the manufacturing field. As a result, by applying it to different industries, efficiency can be improved in a wide range of fields.

[0043] Generative AI can link the automation of existing machinery with cloud-based management systems, enabling remote operation and monitoring. Generative AI, for example, links the automation of existing machinery with cloud-based management systems. For example, generative AI can link with cloud-based management systems to enable remote operation and monitoring of tractors. Generative AI can also link with cloud-based management systems to enable remote operation and monitoring of trucks. Generative AI can also link with cloud-based management systems to enable remote operation and monitoring of factory machinery. This makes it possible to link with cloud-based management systems to enable remote operation and monitoring.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] Generative AI can learn the operating patterns of machines and issue a warning if it detects abnormal behavior. For example, if generative AI detects abnormal vibrations while a tractor is operating, it will sound an alarm. Generative AI can also display a warning to the driver if it detects abnormal behavior while a truck is operating. Generative AI can also issue a warning to workers if it detects abnormal behavior while a factory machine is operating. This makes it possible to detect abnormal behavior early and take appropriate measures.

[0046] Generative AI can send machine operation data to the cloud, enabling remote monitoring and control. For example, generative AI can send tractor operation data to the cloud, allowing farm managers to monitor remotely. Generative AI can also send truck operation data to the cloud, allowing logistics center managers to monitor remotely. Generative AI can also send factory machine operation data to the cloud, allowing factory managers to monitor remotely. This enables remote monitoring and control, resulting in efficient management.

[0047] Generative AI can analyze the operation patterns of machinery and propose optimal maintenance schedules. For example, generative AI can analyze tractor operation data and propose when engine maintenance is required. Generative AI can also analyze truck operation data and propose when brake maintenance is required. Generative AI can also analyze factory machinery operation data and propose when regular maintenance is required. By proposing optimal maintenance schedules, this can extend the lifespan of machinery and enable efficient operation.

[0048] Generative AI can analyze machine operation data and optimize energy efficiency. For example, generative AI can analyze tractor operation data and optimize engine output. Generative AI can also analyze truck operation data and minimize fuel consumption. Generative AI can also analyze factory machine operation data and optimize energy consumption. By optimizing energy efficiency, it is possible to reduce operating costs and alleviate environmental impact.

[0049] Generative AI can analyze machine operation data and be equipped with the ability to automatically correct any abnormalities that are detected. For example, generative AI can analyze tractor operation data and adjust engine output if abnormal vibrations are detected. Generative AI can also analyze truck operation data and adjust the steering angle if abnormal behavior is detected. Generative AI can also analyze factory machine operation data and correct operation if an abnormality is detected. This allows for stable machine operation by automatically correcting any abnormalities that are detected.

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

[0051] Step 1: Generative AI generates the electrical signals required for machine operation. For example, generative AI generates control signals for the engine, steering, and brakes of a tractor or truck. It can also generate control signals for factory machinery. Step 2: The electrical signal generator sends the electrical signals generated by the AI ​​to the circuit board. For example, the electrical signals generated by the AI ​​can be sent to the circuit board to control the engine, steering, and brakes of a tractor or truck. They can also control the operation of factory machinery. Step 3: The machine control unit controls existing machinery using the electrical signals sent to the board. For example, the electrical signals sent to the board can be used to control the engine, steering, and brakes of a tractor or truck. It can also control the operation of factory machinery.

[0052] (Example 2) The automation system according to the embodiment of the present invention is a system in which a generating AI generates an electric signal and sends the electric signal to a base to operate an existing machine. In this way, the automation system can automate an existing machine using the generating AI.

[0053] An automation system according to an embodiment includes a generation AI, an electrical signal generation unit, a board, and a machine control unit. The generation AI generates electrical signals required for machine operation. For example, the generation AI generates control signals for a tractor's engine, steering, brakes, etc. The generation AI can also generate control signals for a truck's engine, steering, brakes, etc. The generation AI can also generate control signals for factory machines. For example, the generation AI generates control signals for a tractor's engine, steering, brakes, etc. The electrical signal generation unit sends the electrical signals generated by the generation AI to the board. For example, the electrical signal generation unit sends the electrical signals generated by the generation AI to the board to control the tractor's engine, steering, brakes, etc. The electrical signal generation unit can also send the electrical signals generated by the generation AI to the board to control the truck's engine, steering, brakes, etc. The electrical signal generation unit can also send the electrical signals generated by the generation AI to the board to control the operation of factory machines. The board receives the electrical signals generated by the generation AI and controls existing machines via the machine control unit. For example, the platform receives the electrical signals generated by the generation AI and controls the engine, steering, brakes, etc. of a tractor. The platform can also receive the electrical signals generated by the generation AI and control the engine, steering, brakes, etc. of a truck. The platform can also receive the electrical signals generated by the generation AI and control the operation of factory machinery. The machine control unit controls existing machinery using the electrical signals input into the platform. For example, the machine control unit controls the engine, steering, brakes, etc. of a tractor using the electrical signals input into the platform. The machine control unit can also control the engine, steering, brakes, etc. of a truck using the electrical signals input into the platform. The machine control unit can also control the operation of factory machinery using the electrical signals input into the platform. As a result, the automation system according to the embodiment can automate existing machinery using the generation AI. For example, the automation of a tractor can be achieved by inputting the electrical signals generated by the generation AI into the platform and controlling the engine, steering, brakes, etc. of the tractor.In addition, automated truck driving becomes possible by feeding the electrical signals generated by the generative AI into a circuit board to control the truck's engine, steering, brakes, etc. In addition, automation of factory machinery becomes possible by feeding the electrical signals generated by the generative AI into a circuit board to control the operation of factory machinery.

[0054] The generation AI can generate control signals for the tractor's engine, steering, brakes, etc. The generation AI can generate control signals for the tractor's engine, steering, brakes, etc. For example, the generation AI can generate a signal to control the output of the tractor's engine. The generation AI can also generate a signal to control the tractor's steering angle. The generation AI can also generate a control signal for the tractor's brakes. This makes it possible to automate tractors.

[0055] The generation AI can generate control signals for the truck's engine, steering, brakes, etc. The generation AI can generate control signals for the truck's engine, steering, brakes, etc. For example, the generation AI can generate a signal to control the output of the truck's engine. The generation AI can also generate a signal to control the steering angle of the truck. The generation AI can also generate a control signal for the truck's brakes. This makes it possible for trucks to drive autonomously.

[0056] The generative AI can generate control signals for factory machines. The generative AI generates control signals for factory machines, for example. For example, the generative AI generates signals that control the operation of factory machines. The generative AI can also learn the operation patterns of factory machines and generate control signals based on those patterns. The generative AI can also generate control signals to optimize the operation of factory machines. This enables the automation of factory machines.

[0057] When learning a machine's operating patterns, generative AI incorporates an anomaly detection function and can automatically correct abnormal operating patterns. For example, when learning a machine's operating patterns, generative AI incorporates an anomaly detection function. For example, when learning a tractor's operating patterns, generative AI can detect abnormal vibrations or sounds and automatically adjust the engine output. When learning a truck's operating patterns, generative AI can also detect abnormal behavior and automatically adjust the steering angle. When learning the operating patterns of factory machinery, generative AI can also detect abnormal behavior and automatically correct it. This allows the machine to operate stably by automatically correcting abnormal operating patterns.

[0058] The generative AI receives feedback in real time on the electrical signals it generates and is able to optimize its operation. The generative AI receives feedback in real time on the electrical signals it generates. For example, the generative AI receives feedback in real time while a tractor is operating and optimizes engine output and steering angle. The generative AI can also receive feedback in real time while a truck is operating and optimize brake control. The generative AI can also receive feedback in real time while factory machinery is operating and optimize its operation. This makes it possible to receive feedback in real time and optimize operation.

[0059] The generative AI can generate motion patterns based on the user's emotions and realize motion that reduces the user's stress. The generative AI generates motion patterns based on the user's emotions, for example. For example, the generative AI can estimate the user's emotions while operating a tractor and generate a motion pattern that reduces stress. The generative AI can also estimate the user's emotions while operating a truck and generate a motion pattern that reduces stress. The generative AI can also estimate the user's emotions while operating factory machinery and generate a motion pattern that reduces stress. In this way, by generating motion patterns based on the user's emotions, it becomes possible to reduce the user's stress.

[0060] Generative AI can generalize the electrical signals it generates so that they can be applied to different types of machines. For example, generative AI can learn the movement patterns of a household robot and apply the generated electrical signals to the household robot. Generative AI can also learn the movement patterns of medical equipment and apply the generated electrical signals to the medical equipment. Generative AI can also learn the movement patterns of industrial robots and apply the generated electrical signals to the industrial robot. This generalization so that it can be applied to different types of machines makes automation possible in a wide range of fields.

[0061] Generative AI can generate electrical signals based on voice commands or gesture input, enabling more intuitive operation. Generative AI generates electrical signals based on voice commands or gesture input, for example. For example, generative AI can analyze voice commands and generate electrical signals that control the operation of a tractor based on the voice commands. Generative AI can also analyze gesture input and generate electrical signals that control the operation of a truck based on the voice commands or gesture input. Generative AI can also generate electrical signals that control the operation of factory machinery based on voice commands or gesture input. This allows for more intuitive operation by generating electrical signals based on voice commands or gesture input.

[0062] The generative AI generates voice feedback according to the user's emotions, thereby improving the user's satisfaction with the machine's operation. The generative AI generates voice feedback according to the user's emotions, for example. For example, the generative AI can estimate the user's emotions while operating a tractor and generate appropriate voice feedback. The generative AI can also estimate the user's emotions while operating a truck and generate appropriate voice feedback. The generative AI can also estimate the user's emotions while operating a factory machine and generate appropriate voice feedback. In this way, by generating voice feedback according to the user's emotions, it is possible to improve the user's satisfaction with the machine's operation.

[0063] The electric signal generation unit can automatically adjust the strength and frequency of the generated electric signal to achieve optimal operation. The electric signal generation unit automatically adjusts the strength and frequency of the generated electric signal, for example. For example, the electric signal generation unit can automatically adjust the strength of the electric signal generated by the generation AI to optimize the output of a tractor engine. The electric signal generation unit can also automatically adjust the frequency of the electric signal generated by the generation AI to optimize the steering angle of a truck. The electric signal generation unit can also automatically adjust the strength and frequency of the electric signal generated by the generation AI to optimize the operation of factory machinery. In this way, optimal operation can be achieved by automatically adjusting the strength and frequency of the electric signal.

[0064] The electrical signal generation unit adjusts the electrical signal based on the user's emotions, enabling more flexible control of machine operation. The electrical signal generation unit adjusts the electrical signal based on the user's emotions, for example. For example, the electrical signal generation unit uses a generation AI to estimate the user's emotions and adjust the strength and frequency of the electrical signal. The electrical signal generation unit can also use a generation AI to estimate the user's emotions and slow down the output of a tractor engine. The electrical signal generation unit can also use a generation AI to estimate the user's emotions and flexibly control the operation of factory machinery. This makes it possible to control the operation of machines more flexibly by adjusting the electrical signal based on the user's emotions.

[0065] The electrical signal generation unit can increase versatility by making the generated electrical signal compatible with different communication protocols. The electrical signal generation unit, for example, makes the generated electrical signal compatible with different communication protocols. For example, the electrical signal generation unit can make the electrical signal generated by the generation AI compatible with Bluetooth. The electrical signal generation unit can also make the electrical signal generated by the generation AI compatible with Wi-Fi. The electrical signal generation unit can also control the operation of factory machines by making the electrical signal generated by the generation AI compatible with different communication protocols. This makes it possible to increase versatility by making the signal compatible with different communication protocols.

[0066] The electrical signal generation unit can extend battery life by sending the generated electrical signals to a circuit board in an energy-efficient manner. For example, the electrical signal generation unit sends the generated electrical signals to a circuit board in an energy-efficient manner. For example, the electrical signal generation unit sends electrical signals generated by the generation AI to a circuit board using a low-power communication protocol to control the engine, steering, brakes, etc. of a tractor. The electrical signal generation unit can also send electrical signals generated by the generation AI to a circuit board in an energy-efficient manner to control the engine, steering, brakes, etc. of a truck. The electrical signal generation unit can also send electrical signals generated by the generation AI to a circuit board in an energy-efficient manner to control the operation of factory machinery. This makes it possible to extend battery life by sending electrical signals in an energy-efficient manner.

[0067] The electrical signal generation unit generates light and sound feedback according to the user's emotions, thereby intuitively communicating the operating status of the machine. The electrical signal generation unit generates, for example, light and sound feedback according to the user's emotions. For example, the electrical signal generation unit uses a generation AI to estimate the user's emotions and generate appropriate light and sound feedback. The electrical signal generation unit can also use a generation AI to estimate the user's emotions and play relaxing music while operating a tractor. The electrical signal generation unit can also use a generation AI to estimate the user's emotions and generate appropriate light and sound feedback while operating a factory machine. This makes it possible to intuitively communicate the operating status of the machine by generating light and sound feedback according to the user's emotions.

[0068] Generative AI can automatically generate maintenance schedules for machinery and achieve preventive maintenance. Generative AI can, for example, automatically generate maintenance schedules for machinery. For example, generative AI can analyze tractor operation data, predict when maintenance will be required, and automatically generate a schedule. Generative AI can also analyze truck operation data, predict when maintenance will be required, and automatically generate a schedule. Generative AI can also analyze factory machine operation data, predict when maintenance will be required, and automatically generate a schedule. In this way, by automatically generating maintenance schedules for machinery, preventive maintenance becomes possible.

[0069] Generative AI can incorporate safety features that monitor the operation of machinery and automatically stop it if an abnormality is detected. Generative AI can, for example, monitor the operation of machinery and incorporate safety features that automatically stop it if an abnormality is detected. For example, generative AI can monitor the operation of a tractor and automatically stop the engine if abnormal vibrations or sounds are detected. Generative AI can also monitor the operation of a truck and automatically apply the brakes if abnormal behavior is detected. Generative AI can also monitor the operation of factory machinery and incorporate safety features that automatically stop it if an abnormality is detected. This makes it possible to ensure safety by monitoring the operation of machinery and automatically stopping it if an abnormality is detected.

[0070] The generative AI can select an operating mode based on the user's emotions and maximize work efficiency. The generative AI can select an operating mode based on the user's emotions, for example. For example, the generative AI can estimate the user's emotions while operating a tractor and select an appropriate operating mode. The generative AI can also estimate the user's emotions while operating a truck and select an appropriate operating mode. The generative AI can also estimate the user's emotions while operating factory machinery and select an appropriate operating mode. This makes it possible to maximize work efficiency by selecting an operating mode based on the user's emotions.

[0071] Generative AI can apply the automation of existing machinery to different industries, achieving efficiency in a wide range of fields. Generative AI can, for example, apply the automation of existing machinery to different industries. For example, generative AI can realize the automation of agricultural machinery, improving efficiency in the agricultural field. Generative AI can also realize the automation of logistics machinery, improving efficiency in the logistics field. Generative AI can also realize the automation of manufacturing machinery, improving efficiency in the manufacturing field. As a result, by applying it to different industries, efficiency can be improved in a wide range of fields.

[0072] Generative AI can link the automation of existing machinery with cloud-based management systems, enabling remote operation and monitoring. Generative AI, for example, links the automation of existing machinery with cloud-based management systems. For example, generative AI can link with cloud-based management systems to enable remote operation and monitoring of tractors. Generative AI can also link with cloud-based management systems to enable remote operation and monitoring of trucks. Generative AI can also link with cloud-based management systems to enable remote operation and monitoring of factory machinery. This makes it possible to link with cloud-based management systems to enable remote operation and monitoring.

[0073] Generative AI can provide customized settings according to the user's emotions, realizing automation tailored to individual needs. Generative AI can provide customized settings according to the user's emotions, for example. For example, generative AI can estimate the user's emotions while operating a tractor and provide customized settings. Generative AI can also estimate the user's emotions while operating a truck and provide customized settings. Generative AI can also estimate the user's emotions while operating factory machinery and provide customized settings. This makes it possible to provide customized settings according to the user's emotions, enabling automation tailored to individual needs.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] Generative AI can estimate the user's emotions and adjust the machine's operation based on the estimated user's emotions. For example, if the generative AI estimates that the user is feeling stressed while operating a tractor, it can reduce engine output. Alternatively, if the generative AI estimates that the user is relaxed while operating a truck, it can speed up the steering response. Alternatively, if the generative AI estimates that the user is tired while operating a factory machine, it can slow down the operating speed. This makes it possible to improve the comfort of operation by adjusting the machine's operation based on the user's emotions.

[0076] Generative AI can learn the operating patterns of machines and issue a warning if it detects abnormal behavior. For example, if generative AI detects abnormal vibrations while a tractor is operating, it will sound an alarm. Generative AI can also display a warning to the driver if it detects abnormal behavior while a truck is operating. Generative AI can also issue a warning to workers if it detects abnormal behavior while a factory machine is operating. This makes it possible to detect abnormal behavior early and take appropriate measures.

[0077] The generative AI can automatically switch the machine's operating mode based on the user's emotions. For example, if the generative AI estimates that the user is concentrating while operating a tractor, it can switch to high-precision mode. The generative AI can also switch to energy-saving mode if it estimates that the user is tired while operating a truck. The generative AI can also switch to safety mode if it estimates that the user is feeling stressed while operating a factory machine. This makes it possible to improve work efficiency and safety by selecting the optimal operating mode according to the user's emotions.

[0078] Generative AI can send machine operation data to the cloud, enabling remote monitoring and control. For example, generative AI can send tractor operation data to the cloud, allowing farm managers to monitor remotely. Generative AI can also send truck operation data to the cloud, allowing logistics center managers to monitor remotely. Generative AI can also send factory machine operation data to the cloud, allowing factory managers to monitor remotely. This enables remote monitoring and control, resulting in efficient management.

[0079] The generative AI can adjust the operating speed of the machine based on the user's emotions. For example, if the generative AI estimates that the user is impatient while operating a tractor, it will slow down the operating speed. Alternatively, if the generative AI estimates that the user is relaxed while operating a truck, it can speed up the operating speed. Alternatively, if the generative AI estimates that the user is tired while operating a factory machine, it can slow down the operating speed. This makes it possible to improve the safety and comfort of operation by adjusting the operating speed based on the user's emotions.

[0080] Generative AI can analyze the operation patterns of machinery and propose optimal maintenance schedules. For example, generative AI can analyze tractor operation data and propose when engine maintenance is required. Generative AI can also analyze truck operation data and propose when brake maintenance is required. Generative AI can also analyze factory machinery operation data and propose when regular maintenance is required. By proposing optimal maintenance schedules, this can extend the lifespan of machinery and enable efficient operation.

[0081] Generative AI can customize the machine's operation interface based on the user's emotions. For example, if the generative AI estimates that the user is feeling stressed while operating a tractor, it can simplify the operation interface. Alternatively, if the generative AI estimates that the user is relaxed while operating a truck, it can display detailed operation information. Alternatively, if the generative AI estimates that the user is tired while operating a factory machine, it can make the operation interface more intuitive. This makes it possible to customize the operation interface based on the user's emotions, thereby improving the comfort and efficiency of operation.

[0082] Generative AI can analyze machine operation data and optimize energy efficiency. For example, generative AI can analyze tractor operation data and optimize engine output. Generative AI can also analyze truck operation data and minimize fuel consumption. Generative AI can also analyze factory machine operation data and optimize energy consumption. By optimizing energy efficiency, it is possible to reduce operating costs and alleviate environmental impact.

[0083] The generative AI can provide feedback on machine operation based on the user's emotions. For example, if the generative AI estimates that the user is feeling anxious while operating a tractor, it can provide reassuring feedback. Alternatively, if the generative AI estimates that the user is tired while operating a truck, it can provide feedback encouraging the user to take a break. Alternatively, if the generative AI estimates that the user is feeling stressed while operating factory machinery, it can provide feedback to help the user relax. This makes it possible to provide appropriate feedback based on the user's emotions, thereby improving the comfort and safety of operation.

[0084] Generative AI can analyze machine operation data and be equipped with the ability to automatically correct any abnormalities that are detected. For example, generative AI can analyze tractor operation data and adjust engine output if abnormal vibrations are detected. Generative AI can also analyze truck operation data and adjust the steering angle if abnormal behavior is detected. Generative AI can also analyze factory machine operation data and correct operation if an abnormality is detected. This allows for stable machine operation by automatically correcting any abnormalities that are detected.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: Generative AI generates the electrical signals required for machine operation. For example, generative AI generates control signals for the engine, steering, and brakes of a tractor or truck. It can also generate control signals for factory machinery. Step 2: The electrical signal generator sends the electrical signals generated by the AI ​​to the circuit board. For example, the electrical signals generated by the AI ​​can be sent to the circuit board to control the engine, steering, and brakes of a tractor or truck. They can also control the operation of factory machinery. Step 3: The machine control unit controls existing machinery using the electrical signals sent to the board. For example, the electrical signals sent to the board can be used to control the engine, steering, and brakes of a tractor or truck. It can also control the operation of factory machinery.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0100] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 7, a 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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]

[0154] 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. Equipped with generative AI, an electrical signal generation unit that feeds the electrical signal generated by the generation AI into a circuit board; a machine control unit that controls an existing machine using the electrical signal sent to the board; A system characterized by:

2. The generated AI is Receives real-time feedback on the electrical signals generated and optimizes movement 2. The system of claim 1.

3. The generated AI is Generates electrical signals based on voice commands and gesture inputs, enabling more intuitive operation 2. The system of claim 1.

4. The electrical signal generating unit The generated electrical signal is sent to the board using wireless communication technology, eliminating the need for complicated wiring.

2. The system of claim 1.

5. The generated AI is Incorporate safety features that monitor machine operation and automatically shut it down if an abnormality is detected 2. The system of claim 1.

6. The generated AI is Generates movement patterns based on the user's emotions, realizing movements that reduce the user's stress 2. The system of claim 1.

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