System

Abstract

The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims.SOLUTION: A system according to an embodiment includes a generation AI, earphones, and AR glasses. The generation AI generates voice guidance based on a query from a user. The earphones provide the user with the audio guidance generated by the generation AI. The AR glasses provide visual guidance.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, there was a problem that performing work while looking at a manual was time-consuming and prone to mistakes.

[0005] The system according to the embodiment aims to enable a user to perform work efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, earphones, and AR glasses. The generation AI generates an audio guide based on a user's inquiry. The earphones provide the user with the audio guide generated by the generation AI. The AR glasses provide a visual guide. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to perform work efficiently. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The audio AR system according to the embodiment of the present invention uses earphones to ask a generating AI to teach you how to do something using audio AR. This reduces training costs and resources and improves work efficiency.

[0029] An audio AR system according to an embodiment includes a generation AI, earphones, and AR glasses. The generation AI generates audio guidance based on a user's inquiry. For example, if a user asks, "Please tell me how to display products," the generation AI responds with specific steps, such as, "First, arrange the products on the shelves, and then attach price tags." Similarly, if a factory worker asks, "Please tell me how to operate this machine," the generation AI responds with specific steps, such as, "First, turn it on, then press the start button." The earphones provide the audio guidance generated by the generation AI to the user. For example, by providing audio guidance through the earphones, the user can work hands-free. The AR glasses provide visual guidance. For example, instructions such as "Press here" are displayed through the AR glasses. This allows the audio AR system to provide information both audibly and visually, improving work efficiency.

[0030] The generation AI can learn a user's past inquiry history and provide an individually customized voice guide. For example, the generation AI analyzes a user's past inquiry history and learns frequently asked questions and specific operating procedures. For example, if a convenience store clerk repeatedly asks how to display products, the generation AI will provide that user with more detailed display instructions. The generation AI also generates individually customized voice guides based on a user's past inquiry history. For example, if a specific user repeatedly asks how to operate a specific machine, the generation AI will provide that user with simplified operating instructions. The generation AI also learns a user's past inquiry history and provides voice guides tailored to the user's preferences. For example, the generation AI generates voice guides that incorporate words and phrases that the user has used frequently in the past. This allows the generation AI to provide a more appropriate voice guide to the user.

[0031] The generation AI can monitor the user's work progress in real time and provide voice guidance on the next step at the appropriate time. For example, the generation AI can monitor the user's work progress in real time and provide voice guidance on the next step. For example, when the user finishes displaying the products, it instructs the user to next attach price tags. The generation AI can also monitor the user's work progress with sensors and cameras and provide voice guidance on the next step at the appropriate time. For example, it instructs the user to next press the start button immediately after turning on the machine. The generation AI can also analyze the user's work progress in real time and provide voice guidance on the next step according to the work progress. For example, it can guide the user on the next work procedure when the user completes a specific task. This allows the generation AI to provide guidance on the next step at the appropriate time according to the user's work progress.

[0032] The generation AI can recognize the user's gestures and movements and provide corresponding feedback in the form of audio guidance. For example, the generation AI recognizes the user's gestures and movements with a camera and provides corresponding feedback in the form of audio. For example, if the user raises their hand, the generation AI will issue appropriate instructions for that movement. The generation AI can also build a system that detects the user's movements with a sensor and provides corresponding feedback in the form of audio. For example, when the user performs a specific movement, the generation AI will provide advice on that movement. The generation AI can also analyze the user's gestures and movements in real time and provide corresponding feedback in the form of audio. For example, if the user performs an incorrect movement, the generation AI will issue instructions to correct that movement. This makes it possible to provide appropriate feedback according to the user's movements.

[0033] Generative AI can provide audio guidance to multiple users simultaneously, supporting teamwork. For example, generative AI can provide audio guidance to multiple users simultaneously, building a system that supports teamwork. For example, when multiple workers are learning how to operate a machine at the same time, the same instructions are given to everyone. Generative AI can also provide appropriate audio guidance to each user when multiple users are working simultaneously. For example, when a team is arranging products on display, different instructions are given to each member. Generative AI can also provide audio guidance to multiple users simultaneously, making teamwork more efficient. For example, when working on a factory line, instructions are given to each worker in real time. This makes it possible to provide audio guidance to multiple users simultaneously, improving the efficiency of teamwork.

[0034] Generative AI can acquire sensor information from equipment in real time and provide instant feedback on how to operate it. For example, generative AI can build a system that acquires sensor information from equipment in real time and provides instant feedback on how to operate it. For example, it can monitor the temperature and pressure of a machine and instruct the appropriate operating procedure. Generative AI can also analyze sensor information from equipment in real time and provide feedback on how to operate it. For example, it can monitor the operating status of a machine and instruct the appropriate way to deal with an abnormality if one occurs. Generative AI can also provide real-time feedback on how to operate it based on sensor information from the equipment. For example, it can monitor the operating status of a machine and provide voice guidance on the optimal operating procedure. This makes it possible to provide feedback in real time based on sensor information from the equipment.

[0035] Generative AI can learn the operation history of a device and suggest optimal operation procedures. Generative AI, for example, builds a system that learns the operation history of a device and suggests optimal operation procedures. For example, it provides voice guidance on efficient operation procedures based on past operation data. Generative AI can also analyze the operation history of a device and suggest optimal operation procedures. For example, it can learn from past operation mistakes and give instructions to prevent the same mistakes from being made. Generative AI can also suggest optimal operation procedures based on the operation history of a device. For example, it can analyze the operation history and provide voice guidance on the most efficient procedure. This makes it possible to suggest optimal operation procedures based on the operation history of a device.

[0036] Generative AI can provide instructions on how to operate a device not only through audio guides, but also through visual guides and videos. For example, generative AI can build a system that provides instructions on how to operate a device not only through audio guides, but also through visual guides and videos. For example, it can show the operating procedures in a video while providing supplemental audio explanations. Generative AI can also provide visual guides when a user is learning how to operate a device. For example, it can show the operating procedures in diagrams while providing detailed audio explanations. Generative AI can also provide instructions on how to operate a device in video, making it easier for users to understand visually. For example, it can play a video demonstrating the operating procedures while providing supplemental audio explanations. This makes it possible to provide instructions on how to operate a device through visual guides and videos in addition to audio guides.

[0037] The generation AI can find commonalities in operation methods between different devices and provide unified audio guidance. For example, the generation AI can analyze commonalities in operation methods between different devices and build a system that provides unified operation guidance. For example, it can integrate the operation procedures of multiple machines and provide audio guidance for the common procedures. The generation AI can also compare the operation methods of different devices and find commonalities. For example, it can extract operation procedures common to multiple devices and provide unified guidance. The generation AI can also find commonalities in operation methods between different devices and provide unified operation guidance for users. For example, it can provide audio guidance for common operation procedures to prevent users from becoming confused. This makes it possible to find commonalities in operation methods between different devices and provide unified operation guidance.

[0038] The generative AI can track the user's gaze through AR displays and provide detailed information about the object in front of their eyes. For example, the generative AI can build a system that tracks the user's gaze through AR displays and provides detailed information about the object in front of their eyes. For example, when a user looks at a specific machine part, instructions on how to operate that part are displayed. The generative AI can also track the user's gaze with a camera and provide information about the object in front of their eyes through AR displays. For example, when a user looks at a product shelf, detailed information about that product is displayed. The generative AI can also analyze the user's gaze in real time through AR displays and provide information about the object in front of their eyes. For example, when a user looks at a specific operation panel, instructions for operating that panel are displayed. This makes it possible to track the user's gaze and provide detailed information about the object in front of their eyes.

[0039] The generative AI can analyze the user's actions in real time through AR displays and visually guide them to the optimal operating procedures. For example, the generative AI can build a system that analyzes the user's actions in real time through AR displays and visually guides them to the optimal operating procedures. For example, when the user operates a machine, the next step is shown through AR displays. The generative AI can also analyze the user's actions with a camera and guide them to the optimal operating procedures through AR displays. For example, when the user performs a specific operation, the next operation to be performed is visually shown. The generative AI can also analyze the user's actions in real time through AR displays and visually guide them to the optimal operating procedures. For example, when the user performs an incorrect operation, the correct operating procedure is shown through AR displays. This makes it possible to analyze the user's actions in real time and visually guide them to the optimal operating procedures.

[0040] Generative AI enables real-time information sharing among multiple users through AR displays, and can support collaborative work. For example, generative AI builds a system that enables real-time information sharing among multiple users through AR displays. For example, team members work together while viewing the same AR display. Generative AI also allows multiple users to use AR displays simultaneously to share information in real time. For example, it displays the progress of a project to everyone, supporting collaborative work. Generative AI also allows real-time information sharing among multiple users through AR displays, making collaborative work more efficient. For example, the work content that each member is responsible for is shared through AR displays. This allows real-time information sharing among multiple users, making collaborative work more efficient.

[0041] The generative AI can obtain information about the user's surrounding environment through AR display and suggest the optimal work environment. For example, the generative AI can obtain information about the user's surrounding environment through AR display and build a system that proposes the optimal work environment. For example, it can monitor the lighting and temperature of the work area and propose the optimal environment. The generative AI can also obtain information about the user's surrounding environment using sensors and propose the optimal work environment. For example, it can measure noise levels and suggest working in a quiet place. The generative AI can also obtain information about the user's surrounding environment in real time through AR display and propose the optimal work environment. For example, it can analyze the layout of the work area and propose efficient placement. This makes it possible to propose the optimal work environment based on the user's surrounding environmental information.

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

[0043] The generation AI can recognize the user's gestures and movements and provide appropriate feedback in the form of voice guidance. For example, if the user raises their hand, it can provide appropriate instructions for that movement. It can also provide advice on a specific movement when the user performs it. Furthermore, if the user makes an incorrect movement, it can provide instructions to correct that movement. This makes it possible to provide appropriate feedback according to the user's movements.

[0044] Generative AI can obtain sensor information from devices in real time and provide immediate feedback on how to operate them. For example, it can monitor the temperature and pressure of a machine and provide instructions on appropriate operating procedures. It can also monitor the operating status of a machine and provide instructions on appropriate countermeasures if an abnormality occurs. It can also monitor the operating status of a device and provide voice guidance on the optimal operating procedures. This allows it to provide feedback in real time based on device sensor information.

[0045] Generative AI can find commonalities in the operation methods of different devices and provide unified audio guidance. For example, it can integrate the operation procedures of multiple machines and provide audio guidance for the common procedures. It can also compare the operation methods of different devices and find commonalities. Furthermore, it can provide audio guidance for common operation procedures to prevent users from becoming confused. This makes it possible to find commonalities in the operation methods of different devices and provide unified operation guidance.

[0046] The generative AI can track the user's gaze through the AR display and provide detailed information about the object in front of their eyes. For example, when a user looks at a specific machine part, it can display instructions on how to operate that part. Also, when a user looks at a product shelf, it can display detailed information about that product. Furthermore, when a user looks at a specific operation panel, it can display instructions on how to operate that panel. This allows the system to track the user's gaze and provide detailed information about the object in front of their eyes.

[0047] Generative AI enables real-time information sharing among multiple users through AR displays, supporting collaborative work. For example, team members can work together while viewing the same AR display. Multiple users can also use AR displays simultaneously to share information in real time. Furthermore, project progress can be displayed to everyone, supporting collaborative work. This allows real-time information sharing among multiple users, making collaborative work more efficient.

[0048] The generative AI can learn from a user's past inquiry history and provide individually customized voice guidance. For example, it can analyze a user's past inquiry history and learn frequently asked questions and specific operating procedures. Also, if a specific user repeatedly asks how to operate a specific machine, it can provide simplified operating procedures to that user. Furthermore, it can provide voice guidance tailored to the user's preferences. This allows for a more appropriate voice guidance to be provided to the user.

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

[0050] Step 1: The generation AI generates audio guidance based on user inquiries. For example, if a user asks, "Please tell me how to display products," the AI ​​will respond in voice with specific steps, such as, "First, arrange the products on the shelves, then attach price tags." Similarly, if a factory worker asks, "Please tell me how to operate this machine," the AI ​​will respond in voice with specific steps, such as, "First, turn it on, then press the start button." Step 2: The earphones provide the user with the audio guidance generated by the generation AI. For example, the generation AI can provide audio guidance through the earphones, allowing the user to work hands-free. Step 3: The AR glasses provide visual guidance. For example, instructions such as "Please press here" are displayed through the AR glasses. This allows users to obtain information both visually and audibly, improving work efficiency.

[0051] (Example 2) The audio AR system according to the embodiment of the present invention uses earphones to ask a generating AI to teach you how to do something using audio AR. This reduces training costs and resources and improves work efficiency.

[0052] An audio AR system according to an embodiment includes a generation AI, earphones, and AR glasses. The generation AI generates audio guidance based on a user's inquiry. For example, if a user asks, "Please tell me how to display products," the generation AI responds with specific steps, such as, "First, arrange the products on the shelves, and then attach price tags." Similarly, if a factory worker asks, "Please tell me how to operate this machine," the generation AI responds with specific steps, such as, "First, turn it on, then press the start button." The earphones provide the audio guidance generated by the generation AI to the user. For example, by providing audio guidance through the earphones, the user can work hands-free. The AR glasses provide visual guidance. For example, instructions such as "Press here" are displayed through the AR glasses. This allows the audio AR system to provide information both audibly and visually, improving work efficiency.

[0053] The generation AI can learn a user's past inquiry history and provide an individually customized voice guide. For example, the generation AI analyzes a user's past inquiry history and learns frequently asked questions and specific operating procedures. For example, if a convenience store clerk repeatedly asks how to display products, the generation AI will provide that user with more detailed display instructions. The generation AI also generates individually customized voice guides based on a user's past inquiry history. For example, if a specific user repeatedly asks how to operate a specific machine, the generation AI will provide that user with simplified operating instructions. The generation AI also learns a user's past inquiry history and provides voice guides tailored to the user's preferences. For example, the generation AI generates voice guides that incorporate words and phrases that the user has used frequently in the past. This allows the generation AI to provide a more appropriate voice guide to the user.

[0054] The generation AI can monitor the user's work progress in real time and provide voice guidance on the next step at the appropriate time. For example, the generation AI can monitor the user's work progress in real time and provide voice guidance on the next step. For example, when the user finishes displaying the products, it instructs the user to next attach price tags. The generation AI can also monitor the user's work progress with sensors and cameras and provide voice guidance on the next step at the appropriate time. For example, it instructs the user to next press the start button immediately after turning on the machine. The generation AI can also analyze the user's work progress in real time and provide voice guidance on the next step according to the work progress. For example, it can guide the user on the next work procedure when the user completes a specific task. This allows the generation AI to provide guidance on the next step at the appropriate time according to the user's work progress.

[0055] The generation AI can use the emotion estimation function to analyze the user's emotional state and provide audio guidance in a relaxed tone to reduce stress. For example, the generation AI can use the emotion estimation function to analyze the user's emotional state in real time and provide audio guidance in a relaxed tone to reduce stress. For example, if the user is nervous, it can give instructions in a calm voice. The generation AI can also build a system that analyzes the user's emotional state and provides audio guidance in a relaxed tone to reduce stress. For example, if the user is impatient, it can give instructions at a slower pace. The generation AI can also use the emotion estimation function to analyze the user's emotional state and provide audio guidance in a relaxed tone. For example, if the user is tired, it can give instructions in a gentle voice. This reduces the user's stress and allows them to work in a relaxed state.

[0056] The generation AI can recognize the user's gestures and movements and provide corresponding feedback in the form of audio guidance. For example, the generation AI recognizes the user's gestures and movements with a camera and provides corresponding feedback in the form of audio. For example, if the user raises their hand, the generation AI will issue appropriate instructions for that movement. The generation AI can also build a system that detects the user's movements with a sensor and provides corresponding feedback in the form of audio. For example, when the user performs a specific movement, the generation AI will provide advice on that movement. The generation AI can also analyze the user's gestures and movements in real time and provide corresponding feedback in the form of audio. For example, if the user performs an incorrect movement, the generation AI will issue instructions to correct that movement. This makes it possible to provide appropriate feedback according to the user's movements.

[0057] Generative AI can provide audio guidance to multiple users simultaneously, supporting teamwork. For example, generative AI can provide audio guidance to multiple users simultaneously, building a system that supports teamwork. For example, when multiple workers are learning how to operate a machine at the same time, the same instructions are given to everyone. Generative AI can also provide appropriate audio guidance to each user when multiple users are working simultaneously. For example, when a team is arranging products on display, different instructions are given to each member. Generative AI can also provide audio guidance to multiple users simultaneously, making teamwork more efficient. For example, when working on a factory line, instructions are given to each worker in real time. This makes it possible to provide audio guidance to multiple users simultaneously, improving the efficiency of teamwork.

[0058] The generation AI can use the emotion estimation function to select the audio guide style that is easiest for the user to understand and adjust it individually. For example, the generation AI can build a system that uses the emotion estimation function to select the audio guide style that is easiest for the user to understand and adjust it individually. For example, if the user is relaxed, it will give instructions in a calm voice. The generation AI can also analyze the user's emotional state and select the audio guide style that is easiest to understand. For example, if the user is concentrating, it will give clear and concise instructions. The generation AI can also use the emotion estimation function to individually adjust the audio guide style according to the user's emotional state. For example, if the user is tired, it will give instructions at a slower pace. This makes it possible to provide the user with the optimal audio guide style.

[0059] Generative AI can acquire sensor information from equipment in real time and provide instant feedback on how to operate it. For example, generative AI can build a system that acquires sensor information from equipment in real time and provides instant feedback on how to operate it. For example, it can monitor the temperature and pressure of a machine and instruct the appropriate operating procedure. Generative AI can also analyze sensor information from equipment in real time and provide feedback on how to operate it. For example, it can monitor the operating status of a machine and instruct the appropriate way to deal with an abnormality if one occurs. Generative AI can also provide real-time feedback on how to operate it based on sensor information from the equipment. For example, it can monitor the operating status of a machine and provide voice guidance on the optimal operating procedure. This makes it possible to provide feedback in real time based on sensor information from the equipment.

[0060] Generative AI can learn the operation history of a device and suggest optimal operation procedures. Generative AI, for example, builds a system that learns the operation history of a device and suggests optimal operation procedures. For example, it provides voice guidance on efficient operation procedures based on past operation data. Generative AI can also analyze the operation history of a device and suggest optimal operation procedures. For example, it can learn from past operation mistakes and give instructions to prevent the same mistakes from being made. Generative AI can also suggest optimal operation procedures based on the operation history of a device. For example, it can analyze the operation history and provide voice guidance on the most efficient procedure. This makes it possible to suggest optimal operation procedures based on the operation history of a device.

[0061] The generation AI can use its emotion estimation function to detect in real time any anxiety or doubts the user may feel while operating the device, and provide a reassuring voice guide. For example, the generation AI can use its emotion estimation function to build a system that detects in real time any anxiety or doubt the user may feel while operating the device, and provide a reassuring voice guide. For example, if the user feels anxious, the generation AI can offer words of encouragement. The generation AI can also analyze the user's emotional state and detect any anxiety or doubt the user may feel while operating the device. For example, if the user is confused about how to operate the device, the generation AI can repeatedly explain the specific steps. The generation AI can also use its emotion estimation function to detect in real time any anxiety or doubt the user may feel while operating the device, and provide a reassuring voice guide. For example, it can provide positive feedback to help the user feel confident in their operation. This makes it possible to detect the user's anxiety or doubts in real time, and provide a reassuring voice guide.

[0062] Generative AI can provide instructions on how to operate a device not only through audio guides, but also through visual guides and videos. For example, generative AI can build a system that provides instructions on how to operate a device not only through audio guides, but also through visual guides and videos. For example, it can show the operating procedures in a video while providing supplemental audio explanations. Generative AI can also provide visual guides when a user is learning how to operate a device. For example, it can show the operating procedures in diagrams while providing detailed audio explanations. Generative AI can also provide instructions on how to operate a device in video, making it easier for users to understand visually. For example, it can play a video demonstrating the operating procedures while providing supplemental audio explanations. This makes it possible to provide instructions on how to operate a device through visual guides and videos in addition to audio guides.

[0063] The generation AI can find commonalities in operation methods between different devices and provide unified audio guidance. For example, the generation AI can analyze commonalities in operation methods between different devices and build a system that provides unified operation guidance. For example, it can integrate the operation procedures of multiple machines and provide audio guidance for the common procedures. The generation AI can also compare the operation methods of different devices and find commonalities. For example, it can extract operation procedures common to multiple devices and provide unified guidance. The generation AI can also find commonalities in operation methods between different devices and provide unified operation guidance for users. For example, it can provide audio guidance for common operation procedures to prevent users from becoming confused. This makes it possible to find commonalities in operation methods between different devices and provide unified operation guidance.

[0064] The generation AI can use the emotion estimation function to suggest an environment in which the user can operate most comfortably and adjust the operation guide. For example, the generation AI can use the emotion estimation function to build a system that suggests an environment in which the user can operate most comfortably. For example, it can suggest an environment in which the user can relax and adjust the operation guide to match that environment. The generation AI can also analyze the user's emotional state and suggest an environment in which the user can operate most comfortably. For example, it can suggest an environment in which the user can concentrate and adjust the operation guide to match that environment. The generation AI can also use the emotion estimation function to suggest an environment in which the user can operate comfortably and adjust the operation guide. For example, it can suggest an environment in which the user does not feel stressed and adjust the operation guide to match that environment. In this way, it can suggest an environment in which the user can operate most comfortably and adjust the operation guide.

[0065] The generative AI can track the user's gaze through AR displays and provide detailed information about the object in front of their eyes. For example, the generative AI can build a system that tracks the user's gaze through AR displays and provides detailed information about the object in front of their eyes. For example, when a user looks at a specific machine part, instructions on how to operate that part are displayed. The generative AI can also track the user's gaze with a camera and provide information about the object in front of their eyes through AR displays. For example, when a user looks at a product shelf, detailed information about that product is displayed. The generative AI can also analyze the user's gaze in real time through AR displays and provide information about the object in front of their eyes. For example, when a user looks at a specific operation panel, instructions for operating that panel are displayed. This makes it possible to track the user's gaze and provide detailed information about the object in front of their eyes.

[0066] The generative AI can analyze the user's actions in real time through AR displays and visually guide them to the optimal operating procedures. For example, the generative AI can build a system that analyzes the user's actions in real time through AR displays and visually guides them to the optimal operating procedures. For example, when the user operates a machine, the next step is shown through AR displays. The generative AI can also analyze the user's actions with a camera and guide them to the optimal operating procedures through AR displays. For example, when the user performs a specific operation, the next operation to be performed is visually shown. The generative AI can also analyze the user's actions in real time through AR displays and visually guide them to the optimal operating procedures. For example, when the user performs an incorrect operation, the correct operating procedure is shown through AR displays. This makes it possible to analyze the user's actions in real time and visually guide them to the optimal operating procedures.

[0067] The generation AI uses the emotion estimation function to change the color and design of the AR display according to the user's emotional state, thereby reducing visual stress. For example, the generation AI uses the emotion estimation function to build a system that changes the color and design of the AR display according to the user's emotional state. For example, if the user is feeling stressed, the color may be changed to a calmer hue. The generation AI also analyzes the user's emotional state and adjusts the design of the AR display to reduce visual stress. For example, the color may be changed to a softer hue to help the user relax. The generation AI also uses the emotion estimation function to change the color and design of the AR display according to the user's emotional state, thereby reducing visual stress. For example, the design may be changed to a simpler one to help the user concentrate. In this way, the color and design of the AR display can be changed according to the user's emotional state, thereby reducing visual stress.

[0068] Generative AI enables real-time information sharing among multiple users through AR displays, and can support collaborative work. For example, generative AI builds a system that enables real-time information sharing among multiple users through AR displays. For example, team members work together while viewing the same AR display. Generative AI also allows multiple users to use AR displays simultaneously to share information in real time. For example, it displays the progress of a project to everyone, supporting collaborative work. Generative AI also allows real-time information sharing among multiple users through AR displays, making collaborative work more efficient. For example, the work content that each member is responsible for is shared through AR displays. This allows real-time information sharing among multiple users, making collaborative work more efficient.

[0069] The generative AI can obtain information about the user's surrounding environment through AR display and suggest the optimal work environment. For example, the generative AI can obtain information about the user's surrounding environment through AR display and build a system that proposes the optimal work environment. For example, it can monitor the lighting and temperature of the work area and propose the optimal environment. The generative AI can also obtain information about the user's surrounding environment using sensors and propose the optimal work environment. For example, it can measure noise levels and suggest working in a quiet place. The generative AI can also obtain information about the user's surrounding environment in real time through AR display and propose the optimal work environment. For example, it can analyze the layout of the work area and propose efficient placement. This makes it possible to propose the optimal work environment based on the user's surrounding environmental information.

[0070] The generation AI can use the emotion estimation function to select the AR display style that is most relaxing for the user and adjust it individually. For example, the generation AI uses the emotion estimation function to build a system that selects the AR display style that is most relaxing for the user and adjusts it individually. For example, it selects colors and designs that will relax the user. The generation AI also analyzes the user's emotional state and selects the AR display style that is most relaxing. For example, it selects soft colors and a simple design so that the user does not feel stressed. The generation AI also uses the emotion estimation function to individually adjust the AR display style according to the user's emotional state. For example, it selects a visually pleasant design so that the user can relax. This makes it possible to provide the user with the AR display style that is most relaxing.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The generative AI can estimate the user's emotional state and, based on the estimated emotions, select and individually adjust the voice guide style that is easiest for the user to understand. For example, if the user is relaxed, it can give instructions in a calm voice. If the user is concentrating, it can give clear and concise instructions. Furthermore, if the user is tired, it can give instructions at a slower pace. This allows it to provide the optimal voice guide style for the user.

[0073] The generation AI can recognize the user's gestures and movements and provide appropriate feedback in the form of voice guidance. For example, if the user raises their hand, it can provide appropriate instructions for that movement. It can also provide advice on a specific movement when the user performs it. Furthermore, if the user makes an incorrect movement, it can provide instructions to correct that movement. This makes it possible to provide appropriate feedback according to the user's movements.

[0074] Using its emotion estimation function, the generative AI can detect in real time any anxiety or doubts the user may have while operating the device, and provide a reassuring voice guide. For example, if the user feels anxious, it can offer words of encouragement. Also, if the user is confused about how to operate the device, it can repeatedly explain the specific steps. It can also provide positive feedback to help the user gain confidence in their operation. This makes it possible to detect the user's anxiety or doubts in real time and provide a reassuring voice guide.

[0075] Generative AI can obtain sensor information from devices in real time and provide immediate feedback on how to operate them. For example, it can monitor the temperature and pressure of a machine and provide instructions on appropriate operating procedures. It can also monitor the operating status of a machine and provide instructions on appropriate countermeasures if an abnormality occurs. It can also monitor the operating status of a device and provide voice guidance on the optimal operating procedures. This allows it to provide feedback in real time based on device sensor information.

[0076] The generation AI can use its emotion estimation function to select and individually adjust the AR display style that is most relaxing for the user. For example, it can select colors and designs that make the user feel relaxed. It can also select soft colors and simple designs so that the user does not feel stressed. It can also select visually pleasing designs to help the user relax. This allows it to provide the user with the most relaxing AR display style.

[0077] Generative AI can find commonalities in the operation methods of different devices and provide unified audio guidance. For example, it can integrate the operation procedures of multiple machines and provide audio guidance for the common procedures. It can also compare the operation methods of different devices and find commonalities. Furthermore, it can provide audio guidance for common operation procedures to prevent users from becoming confused. This makes it possible to find commonalities in the operation methods of different devices and provide unified operation guidance.

[0078] The generation AI uses its emotion estimation function to change the color and design of the AR display according to the user's emotional state, thereby reducing visual stress. For example, if the user is feeling stressed, the color can be changed to a calmer color. It can also change to a softer color to help the user relax. It can also change to a simpler design to help the user concentrate. This allows the color and design of the AR display to be changed according to the user's emotional state, reducing visual stress.

[0079] The generative AI can track the user's gaze through the AR display and provide detailed information about the object in front of their eyes. For example, when a user looks at a specific machine part, it can display instructions on how to operate that part. Also, when a user looks at a product shelf, it can display detailed information about that product. Furthermore, when a user looks at a specific operation panel, it can display instructions on how to operate that panel. This allows the system to track the user's gaze and provide detailed information about the object in front of their eyes.

[0080] Generative AI enables real-time information sharing among multiple users through AR displays, supporting collaborative work. For example, team members can work together while viewing the same AR display. Multiple users can also use AR displays simultaneously to share information in real time. Furthermore, project progress can be displayed to everyone, supporting collaborative work. This allows real-time information sharing among multiple users, making collaborative work more efficient.

[0081] The generative AI can learn from a user's past inquiry history and provide individually customized voice guidance. For example, it can analyze a user's past inquiry history and learn frequently asked questions and specific operating procedures. Also, if a specific user repeatedly asks how to operate a specific machine, it can provide simplified operating procedures to that user. Furthermore, it can provide voice guidance tailored to the user's preferences. This allows for a more appropriate voice guidance to be provided to the user.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The generation AI generates audio guidance based on user inquiries. For example, if a user asks, "Please tell me how to display products," the AI ​​will respond in voice with specific steps, such as, "First, arrange the products on the shelves, then attach price tags." Similarly, if a factory worker asks, "Please tell me how to operate this machine," the AI ​​will respond in voice with specific steps, such as, "First, turn it on, then press the start button." Step 2: The earphones provide the user with the audio guidance generated by the generation AI. For example, the generation AI can provide audio guidance through the earphones, allowing the user to work hands-free. Step 3: The AR glasses provide visual guidance. For example, instructions such as "Please press here" are displayed through the AR glasses. This allows users to obtain information both visually and audibly, improving work efficiency.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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. Generative AI and Earphones and Equipped with AR glasses, The generated AI is Generates audio guidance based on user inquiries, providing the audio guide to the user through the earphone; Provide visual guidance through the AR glasses A system characterized by:

2. The generated AI is The voice guide is provided by learning the user's past inquiry history and individually customized.

2. The system of claim 1.

3. The generated AI is The user's work progress is monitored in real time, and the next step is announced at the appropriate time by the voice guide.

2. The system of claim 1.

4. The generated AI is Analyzing the user's emotional state and providing the audio guidance in a relaxing tone to reduce stress 2. The system of claim 1.

5. The generated AI is Recognizing the user's gestures and actions and providing corresponding feedback via the audio guide 2. The system of claim 1.

Images