System

Abstract

To provide an intuitive and easy-to-understand manual in a system.SOLUTION: A system includes a generation part, an interaction part, and a visual support part. The generation unit supports the administrator in creating a manual by using the generated AI. The interaction unit answers the question of the worker using the interactive AI. The visual aid provides visual support using the AR device.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that paper manuals and text-based explanations are difficult to understand, making it difficult to quickly obtain and update information.

[0005] The system according to the embodiment aims to provide an intuitive and easy-to-understand manual. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a dialogue unit, and a visual support unit. The generation unit uses a generation AI to assist managers in creating manuals. The dialogue unit uses a dialogue AI to answer questions from workers. The visual support unit provides visual support using an AR device. [Effects of the Invention]

[0007] The system according to the embodiment can provide an intuitive and easy-to-understand manual. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The manual system according to an embodiment of the present invention is a new manual system that combines conversational AI and AR to solve the problem of difficulty in acquiring and updating information from paper manuals. This manual system provides an intuitive and easy-to-understand manual by combining the dialogue-style support of conversational AI with the visual support of AR. As a result, the manual system eliminates the difficulty in acquiring and updating information from paper manuals and can provide an intuitive and easy-to-understand manual using conversational AI and AR.

[0029] A manual system according to an embodiment includes a generation unit, a dialogue unit, and a visual support unit. The generation unit uses a generation AI to assist an administrator in creating a manual. For example, when an administrator inputs prompts to the generation AI indicating the content and procedures to be included in the manual, the generation AI generates appropriate manual content based on the prompts. The generation AI generates a manual using, for example, a text generation AI (e.g., LLM). The generation AI can also generate a manual including visual elements using an image generation AI. The dialogue unit answers questions from a worker using a dialogue AI. For example, when a worker asks the dialogue AI, "Which part should be removed next?", the dialogue AI provides a specific answer such as, "The next part to be removed is screw A." The dialogue AI responds to the worker's questions using, for example, a chatbot or a voice assistant. The visual support unit provides visual support using an AR device. For example, a worker can use the AR device to display visual guides for the actual work environment. The AR device provides visual support using, for example, AR glasses or an AR headset. This enables the manual system according to an embodiment to provide an intuitive and easy-to-understand manual. For example, workers can quickly obtain information by asking questions to conversational AI and proceed with their work while receiving visual guidance using AR. Managers can also use generative AI to efficiently create manuals.

[0030] The generation unit can learn the administrator's past manual creation history and automatically generate manuals that suit the style of each individual administrator. For example, the generation AI in the generation unit analyzes the administrator's past manual creation history and learns frequently used phrases and structures. For example, it automatically incorporates expressions and formats preferred by a particular administrator. The generation unit also allows the generation AI to automatically generate manuals that suit the administrator's style. For example, the generation AI generates new manuals that suit the administrator's preferences and style based on data from manuals that the administrator has created in the past. This makes it possible to automatically generate manuals that suit the administrator's style.

[0031] The generation unit can analyze the administrator's voice input and generate a manual directly from the voice. For example, the administrator explains the contents of the manual verbally, and the generation AI analyzes the voice data and converts it into text. For example, an instruction such as "Next, remove screw A" is input verbally. The generation unit also analyzes the voice input and generates a manual directly from the voice. For example, the generation AI uses voice recognition technology to convert the administrator's voice into text and generates a manual based on that text. This makes it possible to generate a manual directly from voice input.

[0032] The dialogue unit learns the worker's past question history and can provide answers that are optimized for each individual worker. For example, the dialogue unit uses a dialogue AI to analyze the worker's past question history and learn frequently asked questions and patterns. For example, it provides the optimal answer based on the questions that a specific worker often asks. The dialogue unit also uses a dialogue AI to learn the worker's past question history and provide the optimal answer for each individual worker. For example, the dialogue AI learns what questions the worker has asked in the past based on the worker's question history and provides the optimal answer based on that information. This makes it possible to provide the optimal answer for each worker.

[0033] The dialogue unit can analyze the voice input of the worker and respond to voice questions. For example, the worker inputs a question by voice, and the dialogue AI analyzes the voice data and converts it into text. For example, a text answer is provided to a voice question such as, "Which part should be removed next?" The dialogue unit can also analyze the voice input of the worker and respond to voice questions. For example, the dialogue AI uses voice recognition technology to convert the worker's voice into text and provides an answer based on that text. This makes it possible to respond to voice questions as well.

[0034] The visual support unit can track the worker's gaze and automatically highlight the part or procedure that is in front of the worker's line of sight. The visual support unit can, for example, use an eye-tracking sensor to track the worker's gaze and automatically highlight the part or procedure that is in front of the worker's line of sight. For example, it can highlight the position of the part that the worker is looking at and visually show the next work procedure that should be performed. The visual support unit can also track the worker's gaze using camera technology and automatically highlight the part or procedure that is in front of the worker's line of sight. For example, a camera can track the worker's gaze in real time and highlight the part that is in front of the worker's line of sight. This makes it possible to automatically highlight the part or procedure that is in front of the worker's line of sight.

[0035] The visual support unit can analyze the hand movements of the worker and provide visual guidance in accordance with the hand movements. For example, the visual support unit uses an AR device to analyze the hand movements of the worker in real time and provide visual guidance in accordance with the hand movements. For example, the next work procedure is displayed in accordance with the hand movements. The visual support unit can also analyze the hand movements of the worker using motion capture technology and provide visual guidance in accordance with the hand movements. For example, the visual support unit can analyze the hand movements of the worker using gesture recognition technology and provide visual guidance in accordance with the hand movements. This makes it possible to provide visual guidance in accordance with the hand movements.

[0036] The visual support unit can support a worker's real-time video calls with other workers and promote collaborative work. For example, an AR device can support real-time video calls with other workers and promote collaborative work. For example, workers share the progress of their work through video calls. The visual support unit can also support real-time communication between workers using video call technology. For example, a worker can use an AR device to make video calls with other workers and advance collaborative work. This can promote collaborative work through real-time video calls.

[0037] The visual support unit can provide visual support in different languages ​​to promote international use. For example, an AR device can provide visual support in different languages ​​to promote international use. For example, a visual guide can be provided that supports multiple languages, such as English, Japanese, and French. The visual support unit can also provide visual support in different languages ​​using translation technology. For example, a visual guide can be provided that supports major international languages ​​to promote international use. This allows visual support to be provided in different languages ​​to promote international use.

[0038] The generation unit automatically updates the contents of the existing manual, and can always provide the latest information. For example, the generation AI periodically analyzes the contents of the existing manual and automatically adds the latest information. For example, the manual is updated when new technologies or procedures are added. The generation unit also analyzes the contents of the existing manual and can always provide the latest information. For example, the generation AI analyzes the contents of the existing manual and automatically updates the manual when new information is added. This allows the contents of the existing manual to be automatically updated and the latest information to be provided.

[0039] The generation unit can analyze the content of existing manuals and automatically add related videos and images. For example, the generation AI in the generation unit analyzes the content of existing manuals and automatically adds related videos and images. For example, videos and images related to work procedures are embedded in the manual. The generation unit can also analyze the content of existing manuals and automatically add related videos and images. For example, the generation AI in the generation unit analyzes the content of existing manuals and automatically adds related videos and images. This makes it possible to automatically add related videos and images.

[0040] The generation unit can link with other digital resources to provide additional information. For example, the generation AI in the generation unit links with other digital resources to provide additional information to an existing manual. For example, the generation AI automatically obtains related information from online forums and technical blogs and adds it to the manual. The generation unit can also link with other digital resources to provide additional information. For example, the generation AI automatically obtains related information from online forums and technical blogs and adds it to the existing manual. This allows the generation unit to link with other digital resources to provide additional information.

[0041] The generation unit can automatically translate the contents of existing manuals into different languages ​​to promote international use. For example, the generation AI can automatically translate the contents of existing manuals into different languages ​​to promote international use. For example, the generation AI can provide manuals that support multiple languages ​​such as English, Japanese, and French. The generation unit can also automatically translate the contents of existing manuals into different languages ​​to promote international use. For example, the generation AI can provide manuals that support major international languages ​​to promote international use. This can automatically translate the contents of existing manuals into different languages ​​to promote international use.

[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 unit can automatically search external databases and resources that administrators want to refer to while creating manuals and provide relevant information. For example, if an administrator needs information on a specific technology, the generation AI can search for reliable technical blogs and papers on the Internet and incorporate that information into the manual. The generation unit can also learn from external resources that administrators have previously referenced and automatically suggest them the next time manuals are created. This allows administrators to efficiently create manuals that incorporate the latest information.

[0044] The dialogue unit can be equipped with a context analysis function to more accurately understand the intent of a question when a worker inputs it. For example, if a worker asks, "What should I do next?", the conversational AI will provide a specific answer based on the worker's current work situation and past question history. Furthermore, even if the worker asks an ambiguous question, the dialogue unit can clarify the intent through additional questions and provide the most appropriate answer. This allows workers to obtain the information they need more quickly and accurately.

[0045] The visual support unit monitors the worker's movements in real time and can display a warning if the worker performs an incorrect procedure. For example, if a worker accidentally attempts to remove the wrong part, the AR device detects the movement and visually shows the correct procedure. The visual support unit can also provide guidelines to help workers perform their work safely and issue a warning if they perform a dangerous action. This allows workers to work safely and efficiently.

[0046] The visual support unit can automatically recognize the tools and parts used by the worker and provide visual guidance accordingly. For example, if a worker picks up a specific tool, the AR device will recognize that tool and display the next work procedure. The visual support unit can also automatically recognize the parts used by the worker and display information and precautions related to those parts. This allows workers to work efficiently.

[0047] The visual support unit can detect problems that occur while workers are working in real time and suggest solutions. For example, if a worker fails to install a part, the AR device will detect the problem and visually show them the correct installation method. The visual support unit can also display a warning if the worker performs an incorrect procedure and show them the correct procedure. This allows workers to quickly solve problems and work efficiently.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The generation unit uses generation AI to assist the administrator in creating a manual. The administrator inputs prompts to the generation AI instructing it on the content and procedures to be included in the manual, and the generation AI generates appropriate manual content based on the prompts. The generation AI can generate manuals using text generation AI (e.g., LLM) and can also generate manuals that include visual elements using image generation AI. Step 2: The dialogue unit uses conversational AI to answer the worker's questions. When the worker asks the conversational AI, "Which part should be removed next?", the conversational AI provides a specific answer, such as, "The next part to be removed is screw A." The conversational AI responds to the worker's questions using a chatbot or voice assistant. Step 3: The visual support unit provides visual support using an AR device. By using the AR device, workers can display visual guides for the actual work environment. The AR device provides visual support using AR glasses or an AR headset.

[0050] (Example 2) The manual system according to an embodiment of the present invention is a new manual system that combines conversational AI and AR to solve the problem of difficulty in acquiring and updating information from paper manuals. This manual system provides an intuitive and easy-to-understand manual by combining the dialogue-style support of conversational AI with the visual support of AR. As a result, the manual system eliminates the difficulty in acquiring and updating information from paper manuals and can provide an intuitive and easy-to-understand manual using conversational AI and AR.

[0051] A manual system according to an embodiment includes a generation unit, a dialogue unit, and a visual support unit. The generation unit uses a generation AI to assist an administrator in creating a manual. For example, when an administrator inputs prompts to the generation AI indicating the content and procedures to be included in the manual, the generation AI generates appropriate manual content based on the prompts. The generation AI generates a manual using, for example, a text generation AI (e.g., LLM). The generation AI can also generate a manual including visual elements using an image generation AI. The dialogue unit answers questions from a worker using a dialogue AI. For example, when a worker asks the dialogue AI, "Which part should be removed next?", the dialogue AI provides a specific answer such as, "The next part to be removed is screw A." The dialogue AI responds to the worker's questions using, for example, a chatbot or a voice assistant. The visual support unit provides visual support using an AR device. For example, a worker can use the AR device to display visual guides for the actual work environment. The AR device provides visual support using, for example, AR glasses or an AR headset. This enables the manual system according to an embodiment to provide an intuitive and easy-to-understand manual. For example, workers can quickly obtain information by asking questions to conversational AI and proceed with their work while receiving visual guidance using AR. Managers can also use generative AI to efficiently create manuals.

[0052] The generation unit can learn the administrator's past manual creation history and automatically generate manuals that suit the style of each individual administrator. For example, the generation AI in the generation unit analyzes the administrator's past manual creation history and learns frequently used phrases and structures. For example, it automatically incorporates expressions and formats preferred by a particular administrator. The generation unit also allows the generation AI to automatically generate manuals that suit the administrator's style. For example, the generation AI generates new manuals that suit the administrator's preferences and style based on data from manuals that the administrator has created in the past. This makes it possible to automatically generate manuals that suit the administrator's style.

[0053] The generation unit can analyze the administrator's voice input and generate a manual directly from the voice. For example, the administrator explains the contents of the manual verbally, and the generation AI analyzes the voice data and converts it into text. For example, an instruction such as "Next, remove screw A" is input verbally. The generation unit also analyzes the voice input and generates a manual directly from the voice. For example, the generation AI uses voice recognition technology to convert the administrator's voice into text and generates a manual based on that text. This makes it possible to generate a manual directly from voice input.

[0054] The generation unit uses the emotion estimation function to analyze the emotional state of the manager and can support the creation of manuals in a low-stress state. For example, the generation unit uses the emotion estimation function to analyze the facial expressions and voice tone of the manager and make suggestions for relaxation if stress is high. For example, a message encouraging the manager to take a break is displayed. The generation unit also uses the emotion estimation function to analyze the emotional state of the manager and support the creation of manuals in a low-stress state. For example, the emotion estimation function monitors the emotional state of the manager in real time using facial expression recognition technology and voice analysis technology and makes suggestions for relaxation if stress is high. This makes it possible to support the creation of manuals while reducing the manager's stress.

[0055] The dialogue unit learns the worker's past question history and can provide answers that are optimized for each individual worker. For example, the dialogue unit uses a dialogue AI to analyze the worker's past question history and learn frequently asked questions and patterns. For example, it provides the optimal answer based on the questions that a specific worker often asks. The dialogue unit also uses a dialogue AI to learn the worker's past question history and provide the optimal answer for each individual worker. For example, the dialogue AI learns what questions the worker has asked in the past based on the worker's question history and provides the optimal answer based on that information. This makes it possible to provide the optimal answer for each worker.

[0056] The dialogue unit can analyze the voice input of the worker and respond to voice questions. For example, the worker inputs a question by voice, and the dialogue AI analyzes the voice data and converts it into text. For example, a text answer is provided to a voice question such as, "Which part should be removed next?" The dialogue unit can also analyze the voice input of the worker and respond to voice questions. For example, the dialogue AI uses voice recognition technology to convert the worker's voice into text and provides an answer based on that text. This makes it possible to respond to voice questions as well.

[0057] The dialogue unit can use the emotion estimation function to analyze the emotional state of the worker and provide support in a low-stress state. For example, the dialogue unit can use the emotion estimation function to analyze the worker's facial expressions and voice tone, and make suggestions to relax if the worker is under high stress. For example, it can display a message encouraging the worker to take a break. The dialogue unit can also use the emotion estimation function to analyze the worker's emotional state and provide support in a low-stress state. For example, the emotion estimation function can monitor the worker's emotional state in real time using facial expression recognition technology and voice analysis technology, and make suggestions to relax if the worker is under high stress. This makes it possible to provide support while reducing the worker's stress.

[0058] The visual support unit can track the worker's gaze and automatically highlight the part or procedure that is in front of the worker's line of sight. The visual support unit can, for example, use an eye-tracking sensor to track the worker's gaze and automatically highlight the part or procedure that is in front of the worker's line of sight. For example, it can highlight the position of the part that the worker is looking at and visually show the next work procedure that should be performed. The visual support unit can also track the worker's gaze using camera technology and automatically highlight the part or procedure that is in front of the worker's line of sight. For example, a camera can track the worker's gaze in real time and highlight the part that is in front of the worker's line of sight. This makes it possible to automatically highlight the part or procedure that is in front of the worker's line of sight.

[0059] The visual support unit can analyze the hand movements of the worker and provide visual guidance in accordance with the hand movements. For example, the visual support unit uses an AR device to analyze the hand movements of the worker in real time and provide visual guidance in accordance with the hand movements. For example, the next work procedure is displayed in accordance with the hand movements. The visual support unit can also analyze the hand movements of the worker using motion capture technology and provide visual guidance in accordance with the hand movements. For example, the visual support unit can analyze the hand movements of the worker using gesture recognition technology and provide visual guidance in accordance with the hand movements. This makes it possible to provide visual guidance in accordance with the hand movements.

[0060] The visual support unit can use the emotion estimation function to analyze the emotional state of the worker and provide visual support in a low-stress state. For example, the visual support unit can use the emotion estimation function to analyze the worker's facial expressions and voice tone, and make suggestions for relaxation if stress is high. For example, it can display a message encouraging the worker to take a break. The visual support unit can also use the emotion estimation function to analyze the worker's emotional state and provide visual support in a low-stress state. For example, the emotion estimation function can monitor the worker's emotional state in real time using facial expression recognition technology and voice analysis technology, and make suggestions for relaxation if stress is high. This makes it possible to provide visual support while reducing the worker's stress.

[0061] The visual support unit can support a worker's real-time video calls with other workers and promote collaborative work. For example, an AR device can support real-time video calls with other workers and promote collaborative work. For example, workers share the progress of their work through video calls. The visual support unit can also support real-time communication between workers using video call technology. For example, a worker can use an AR device to make video calls with other workers and advance collaborative work. This can promote collaborative work through real-time video calls.

[0062] The visual support unit can provide visual support in different languages ​​to promote international use. For example, an AR device can provide visual support in different languages ​​to promote international use. For example, a visual guide can be provided that supports multiple languages, such as English, Japanese, and French. The visual support unit can also provide visual support in different languages ​​using translation technology. For example, a visual guide can be provided that supports major international languages ​​to promote international use. This allows visual support to be provided in different languages ​​to promote international use.

[0063] The visual support unit can use the emotion estimation function to propose a visual guide that is most easily understandable to the worker. For example, the visual support unit uses the emotion estimation function to analyze the worker's facial expressions and voice tone, and proposes a visual guide that is most easily understandable to the worker. For example, the visual support unit suggests a preferred style and level of detail of a visual guide based on the worker's emotional response. The visual support unit also uses the emotion estimation function to analyze the worker's emotional state, and provides a visual guide that is most easily understandable to the worker. For example, the emotion estimation function monitors the worker's emotional state in real time using facial expression recognition technology and voice analysis technology, and proposes a visual guide that is most easily understandable to the worker. This makes it possible to propose a visual guide that is most easily understandable to the worker.

[0064] The generation unit automatically updates the contents of the existing manual, and can always provide the latest information. For example, the generation AI periodically analyzes the contents of the existing manual and automatically adds the latest information. For example, the manual is updated when new technologies or procedures are added. The generation unit also analyzes the contents of the existing manual and can always provide the latest information. For example, the generation AI analyzes the contents of the existing manual and automatically updates the manual when new information is added. This allows the contents of the existing manual to be automatically updated and the latest information to be provided.

[0065] The generation unit can analyze the content of existing manuals and automatically add related videos and images. For example, the generation AI in the generation unit analyzes the content of existing manuals and automatically adds related videos and images. For example, videos and images related to work procedures are embedded in the manual. The generation unit can also analyze the content of existing manuals and automatically add related videos and images. For example, the generation AI in the generation unit analyzes the content of existing manuals and automatically adds related videos and images. This makes it possible to automatically add related videos and images.

[0066] The generation unit can use the emotion estimation function to analyze the emotional state of the worker and provide information about the existing manual in the most understandable format. The generation unit, for example, uses the emotion estimation function to analyze the worker's facial expressions and voice tone and provide information about the existing manual in the most understandable format for the worker. For example, the generation unit suggests a preferred format or design based on the worker's emotional response. The generation unit also uses the emotion estimation function to analyze the worker's emotional state and provide information about the existing manual in the most understandable format for the worker. For example, the emotion estimation function monitors the worker's emotional state in real time using facial expression recognition technology and voice analysis technology and provides information about the existing manual in the most understandable format for the worker. This makes it possible to provide information about the existing manual in the most understandable format for the worker.

[0067] The generation unit can link with other digital resources to provide additional information. For example, the generation AI in the generation unit links with other digital resources to provide additional information to an existing manual. For example, the generation AI automatically obtains related information from online forums and technical blogs and adds it to the manual. The generation unit can also link with other digital resources to provide additional information. For example, the generation AI automatically obtains related information from online forums and technical blogs and adds it to the existing manual. This allows the generation unit to link with other digital resources to provide additional information.

[0068] The generation unit can automatically translate the contents of existing manuals into different languages ​​to promote international use. For example, the generation AI can automatically translate the contents of existing manuals into different languages ​​to promote international use. For example, the generation AI can provide manuals that support multiple languages ​​such as English, Japanese, and French. The generation unit can also automatically translate the contents of existing manuals into different languages ​​to promote international use. For example, the generation AI can provide manuals that support major international languages ​​to promote international use. This can automatically translate the contents of existing manuals into different languages ​​to promote international use.

[0069] The generation unit can use the emotion estimation function to provide information about the existing manual in a format that is most satisfactory to the worker. For example, the generation unit uses the emotion estimation function to analyze the worker's facial expression and voice tone, and provides information about the existing manual in a format that is most satisfactory to the worker. For example, the generation unit suggests a preferred format or design based on the worker's emotional response. The generation unit also uses the emotion estimation function to analyze the worker's emotional state, and provides information about the existing manual in a format that is most satisfactory to the worker. For example, the emotion estimation function monitors the worker's emotional state in real time using facial expression recognition technology and voice analysis technology, and provides information about the existing manual in a format that is most satisfactory to the worker. This makes it possible to provide information about the existing manual in a format that is most satisfactory to the worker.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The generation unit can automatically search external databases and resources that administrators want to refer to while creating manuals and provide relevant information. For example, if an administrator needs information on a specific technology, the generation AI can search for reliable technical blogs and papers on the Internet and incorporate that information into the manual. The generation unit can also learn from external resources that administrators have previously referenced and automatically suggest them the next time manuals are created. This allows administrators to efficiently create manuals that incorporate the latest information.

[0072] The dialogue unit can be equipped with a context analysis function to more accurately understand the intent of a question when a worker inputs it. For example, if a worker asks, "What should I do next?", the conversational AI will provide a specific answer based on the worker's current work situation and past question history. Furthermore, even if the worker asks an ambiguous question, the dialogue unit can clarify the intent through additional questions and provide the most appropriate answer. This allows workers to obtain the information they need more quickly and accurately.

[0073] The visual support unit monitors the worker's movements in real time and can display a warning if the worker performs an incorrect procedure. For example, if a worker accidentally attempts to remove the wrong part, the AR device detects the movement and visually shows the correct procedure. The visual support unit can also provide guidelines to help workers perform their work safely and issue a warning if they perform a dangerous action. This allows workers to work safely and efficiently.

[0074] The generator uses emotion estimation to analyze the manager's emotional state and provide encouragement and advice at the appropriate time while creating manuals. For example, if the manager is feeling stressed, the generator AI will display a message such as "Take a short break and refresh yourself." It can also provide an encouraging message such as "Great progress" if the manager is making steady progress. This allows managers to create manuals efficiently while maintaining their motivation.

[0075] The dialogue unit uses emotion estimation to analyze the worker's emotional state and make suggestions for relaxation at appropriate times. For example, if a worker is feeling stressed, the conversational AI will display a message such as "Take a short break and refresh yourself." If the worker is feeling impatient, it can also provide advice such as "Proceed slowly and steadily." This allows workers to continue their work while reducing stress.

[0076] The visual support unit can automatically recognize the tools and parts used by the worker and provide visual guidance accordingly. For example, if a worker picks up a specific tool, the AR device will recognize that tool and display the next work procedure. The visual support unit can also automatically recognize the parts used by the worker and display information and precautions related to those parts. This allows workers to work efficiently.

[0077] The generation unit can use the emotion estimation function to analyze the manager's emotional state and provide appropriate music or environmental sounds while the manual is being created. For example, if the manager wants to increase their concentration, it can play relaxing music. Also, if the manager wants to relax, it can provide natural or environmental sounds. This allows the manager to create the manual in a comfortable environment.

[0078] The dialogue unit uses its emotion estimation function to analyze the worker's emotional state and provide answers in a format that is most easily understood by the worker. For example, if the worker is feeling impatient, it can provide a concise and easy-to-understand answer. On the other hand, if the worker is relaxed, it can provide a detailed explanation. This allows the worker to obtain information in the format that is most appropriate for their emotional state.

[0079] The visual support unit can detect problems that occur while workers are working in real time and suggest solutions. For example, if a worker fails to install a part, the AR device will detect the problem and visually show them the correct installation method. The visual support unit can also display a warning if the worker performs an incorrect procedure and show them the correct procedure. This allows workers to quickly solve problems and work efficiently.

[0080] The generation unit uses an emotion estimation function to analyze the manager's emotional state and provide appropriate feedback while the manual is being created. For example, if the manager is feeling anxious, the unit can provide advice such as, "This part is very important, so proceed carefully." On the other hand, if the manager is proceeding with the work with confidence, the unit can provide positive feedback such as, "You're making great progress." This allows managers to create manuals while receiving appropriate feedback according to their emotional state.

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

[0082] Step 1: The generation unit uses generation AI to assist the administrator in creating a manual. The administrator inputs prompts to the generation AI instructing it on the content and procedures to be included in the manual, and the generation AI generates appropriate manual content based on the prompts. The generation AI can generate manuals using text generation AI (e.g., LLM) and can also generate manuals that include visual elements using image generation AI. Step 2: The dialogue unit uses conversational AI to answer the worker's questions. When the worker asks the conversational AI, "Which part should be removed next?", the conversational AI provides a specific answer, such as, "The next part to be removed is screw A." The conversational AI responds to the worker's questions using a chatbot or voice assistant. Step 3: The visual support unit provides visual support using an AR device. By using the AR device, workers can display visual guides for the actual work environment. The AR device provides visual support using AR glasses or an AR headset.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, 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.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A generation unit that uses generation AI to assist administrators in creating manuals; A dialogue unit that uses conversational AI to answer questions from workers; a visual support unit that provides visual support using an AR device. A system characterized by:

2. The generation unit The system learns the manager's past manual creation history and automatically generates manuals tailored to the manager's style.

2. The system of claim 1.

3. The dialogue unit Learns the question history of the worker and provides answers optimized for each worker 2. The system of claim 1.

4. The visual support portion includes: Tracks the worker's gaze and automatically highlights the part or procedure in front of the worker's eyes 2. The system of claim 1.

5. The generation unit Analyze the manager's emotional state and support manual creation under low stress conditions 2. The system of claim 1.

6. The dialogue unit Analyze the worker's emotional state and provide support in a low-stress environment 2. The system of claim 1.

7. The visual support portion includes: Analyze the worker's emotional state and provide visual support in a low-stress environment 2. The system of claim 1.

8. The generation unit Analyze the emotional state of the worker and provide information from existing manuals in the most understandable format 2. The system of claim 1.

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