System

Abstract

To provide a system that recognizes the situation at a work site over a wide area and provides efficient work support.SOLUTION: A system according to the embodiment comprises a recognition unit, a generation unit, and a monitoring unit. The recognition unit recognizes the situation at a work site. The generation unit analyzes the information recognized by the recognition unit, and generates voice guidance. The monitoring unit monitors a worker's progress and work accuracy in real time based on the voice guidance generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is only possible to recognize the situation at the work site from a limited field of view, making it difficult to provide efficient work support.

[0005] The system according to the embodiment aims to recognize the situation at a work site over a wide range and provide efficient work support. [Means for solving the problem]

[0006] The system according to the embodiment includes a recognition unit, a generation unit, and a monitoring unit. The recognition unit recognizes the situation at the work site. The generation unit analyzes the information recognized by the recognition unit and generates voice guidance. The monitoring unit monitors the progress of the worker and the accuracy of the work in real time based on the voice guidance generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can recognize the situation at a work site over a wide range and provide efficient work support. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AR utilization system according to an embodiment of the present invention is a system that realizes AR utilization with reduced reliance on visual field by combining AR (augmented reality) and generative AI in the transfer of skills and management of work processes. This system recognizes the situation at the work site and transmits the information to the generative AI. The generative AI analyzes the received information and generates appropriate voice guidance for the worker. This allows the worker to proceed with the work through the voice guidance without relying on the visual field. As a result, the AR utilization system reduces the worker's reliance on the visual field and enables the work to proceed efficiently through the voice guidance.

[0029] The AR utilization system according to the embodiment includes a recognition unit, a generation unit, and a monitoring unit. The recognition unit recognizes the situation of the work site. The situation of the work site includes, for example, the movement of the worker, the state of the equipment, and the environmental conditions, but is not limited to such examples. The recognition unit collects images and data of the work site using, for example, a camera or a sensor. The cameras and sensors include fixed cameras, wearable sensors, infrared sensors, and the like. The collected images and data include image data, voice data, temperature data, and the like. The generation unit uses a generation AI to analyze the information recognized by the recognition unit and generate voice guidance. The generation unit generates voice guidance using, for example, natural language processing technology or voice synthesis technology. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, and the like. Voice synthesis technology includes text-to-speech synthesis, the use of voice samples, and the like. The generation unit can also analyze the voice input of the worker and provide an appropriate answer by voice. For example, the generation unit uses voice recognition technology to analyze the voice input of the worker and generate an appropriate answer. The monitoring unit monitors the progress of the worker and the accuracy of the work in real time based on the voice guidance generated by the generation unit. The monitoring unit monitors the progress of the worker and the accuracy of the work using, for example, a sensor or a camera. The sensor or camera includes a motion sensor, a surveillance camera, a wearable camera, and the like. The monitoring unit performs monitoring in real time and can adjust the data update frequency and the monitoring timing. As a result, the AR utilization system according to the embodiment can reduce the dependency of the worker on the field of vision and efficiently proceed with the work through the voice guidance.

[0030] The recognition unit recognizes the situation at the work site. The situation at the work site includes, for example, the behavior of the worker, the state of the equipment, the environmental conditions, and the like, but is not limited to such examples. The recognition unit collects images and data at the work site using, for example, a camera or a sensor. The cameras and sensors include fixed cameras, wearable sensors, infrared sensors, and the like. The collected images and data include image data, audio data, temperature data, and the like. Specifically, the fixed cameras monitor the entire work site over a wide area and record the behavior of the worker and the state of the equipment in detail. The wearable sensors are worn on the body of the worker and collect biometric information such as the worker's movement, posture, and heart rate in real time. The infrared sensors detect temperature changes and the location of heat sources, and are useful for temperature management of the work environment and abnormality detection. These data are managed centrally by the recognition unit and used to comprehensively grasp the situation at the work site. Furthermore, the recognition unit can analyze the collected data in real time and immediately detect abnormal behavior and changes in the environment. For example, if a worker does not perform a prescribed action or if a device is operating abnormally, the recognition unit immediately notifies the generation unit and monitoring unit of that information, thereby improving the safety and efficiency of the workplace.

[0031] The generation unit uses the generation AI to analyze the information recognized by the recognition unit and generate voice guidance. The generation unit generates voice guidance using, for example, natural language processing technology or voice synthesis technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. Voice synthesis technology includes text-to-speech synthesis and the use of voice samples. Specifically, the generation AI analyzes the data provided by the recognition unit and provides appropriate instructions and advice to the worker by voice. For example, if the worker forgets a specific work procedure, the generation unit guides the worker through the procedure by voice. In addition, when the worker inputs voice, the generation unit analyzes the voice and generates an appropriate answer. The voice recognition technology is used to analyze the worker's voice input and generate an appropriate answer. For example, when the worker asks, "What should I do next?", the generation unit analyzes the question and guides the worker through the next work procedure by voice. In addition, the generation unit can dynamically change the content of the voice guidance depending on the progress of the worker and the environmental conditions. For example, if the temperature of the work environment becomes high, the generation unit guides the worker through voice to take a break. This enables the generation unit to provide appropriate instructions to workers in real time, improving work efficiency and safety.

[0032] The monitoring unit monitors the progress of the worker and the accuracy of the work in real time based on the voice guidance generated by the generation unit. The monitoring unit monitors the progress of the worker and the accuracy of the work using, for example, a sensor or a camera. The sensors and cameras include a motion sensor, a surveillance camera, a wearable camera, and the like. Specifically, the motion sensor records the movement of the worker in detail and monitors whether the specified movement is being performed accurately. The surveillance camera monitors the entire work site over a wide area and records the movement of the worker and the state of the equipment in detail. The wearable camera provides images from the viewpoint of the worker in real time, allowing the progress of the work to be grasped in detail. The monitoring unit performs monitoring in real time and can adjust the frequency of updating the data and the timing of monitoring. For example, if the worker does not perform the specified movement or if the operation of the equipment is abnormal, the monitoring unit immediately notifies the generation unit or the recognition unit of the information. This can improve the safety and efficiency of the work site. Furthermore, the monitoring unit can analyze the collected data and evaluate the progress and accuracy of the work. For example, the monitoring department evaluates whether workers are completing tasks within the specified time and whether work procedures are being followed accurately, and points out areas for improvement as necessary. This allows the monitoring department to improve the quality of work and also contribute to improving the skills of workers.

[0033] The recognition unit can collect images or data of the work site using a camera or a sensor. The camera or sensor includes, for example, a fixed camera, a wearable sensor, an infrared sensor, and the like. The collected images and data include, for example, image data, audio data, and temperature data. The recognition unit can collect images of the work site using, for example, a fixed camera. The recognition unit can also collect operation data of the worker using a wearable sensor. Furthermore, the recognition unit can collect temperature data of the work environment using an infrared sensor. This allows the recognition unit to accurately recognize the situation of the work site. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recognition unit can input image data acquired by a camera to the generation AI and cause the generation AI to execute a process of recognizing the situation of the work site from the image data.

[0034] The generation unit can generate the voice guidance using natural language processing technology or voice synthesis technology. The generation unit can generate the voice guidance using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, and the like. The generation unit can, for example, divide a sentence using morphological analysis, analyze the structure of the sentence using grammatical analysis, and understand the meaning of the sentence using semantic analysis. The generation unit can also generate the voice guidance using voice synthesis technology. Voice synthesis technology includes text-to-speech synthesis, the use of voice samples, and the like. The generation unit can, for example, convert text data into voice data using text-to-speech synthesis. The generation unit can also generate natural voice guidance using voice samples. This allows the generation unit to generate appropriate voice guidance. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis result using the natural language processing technology to the generation AI, and the generation AI can execute a process of generating voice guidance.

[0035] The monitoring unit can monitor the progress or accuracy of the work of the worker in real time using a sensor or a camera. The monitoring unit can monitor the progress of the worker in real time using, for example, a sensor. The sensor includes a motion sensor, a surveillance camera, a wearable camera, and the like. The monitoring unit can monitor the motion of the worker using, for example, a motion sensor to grasp the progress. The monitoring unit can also monitor the work procedure of the worker using a surveillance camera to evaluate the accuracy of the work. Furthermore, the monitoring unit can also monitor the progress of the work from the viewpoint of the worker using a wearable camera. This allows the monitoring unit to monitor the progress and accuracy of the work in real time. Part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data acquired by the sensor to the generation AI, and the generation AI can execute a process of monitoring the progress and accuracy of the work.

[0036] The generation unit can analyze the voice input of the worker and provide an appropriate answer by voice. The generation unit can analyze the voice input of the worker using, for example, voice recognition technology. Voice recognition technology includes voice feature extraction, voice to text conversion, and voice semantic analysis. The generation unit can extract features from voice data using, for example, voice feature extraction, convert voice data to text data using voice to text conversion, and understand the content of the voice using voice semantic analysis. The generation unit can generate an appropriate answer based on the result of the analysis using the voice recognition technology. For example, the generation unit can generate an appropriate answer to a question of the worker and provide it by voice. This allows the generation unit to provide an appropriate answer to a question or confirmation of the worker. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis result using the voice recognition technology to the generation AI, and the generation AI can execute a process of generating an appropriate answer.

[0037] The generation unit can provide additional instructions or corrections according to the progress of the worker. The generation unit can, for example, monitor the progress of the worker in real time and provide additional instructions or corrections according to the progress. The generation unit can monitor the worker's actions and work procedures using a sensor or a camera to grasp the progress. The generation unit can provide additional instructions, for example, when the progress of the worker is delayed. In addition, the generation unit can also provide correction instructions when the worker is performing an incorrect procedure. This allows the generation unit to provide appropriate instructions and corrections according to the progress of the work. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input data monitoring the progress to the generation AI, and the generation AI can execute a process of generating additional instructions and corrections.

[0038] The recognition unit can improve the recognition accuracy by adding the movement pattern of the worker to the collected data. The recognition unit can, for example, track the movement of the worker's hands and recognize a specific movement pattern to determine the progress of the work. The recognition unit can, for example, analyze the walking pattern of the worker and recognize the movement in the work area to grasp the progress of the work. The recognition unit can also track the movement of the worker's line of sight and recognize an object in the line of sight to support the progress of the work. In this way, the recognition unit can improve the recognition accuracy by adding the movement pattern of the worker. A part or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input the movement data of the worker to the generation AI, and the generation AI can execute a process of analyzing the movement pattern.

[0039] The recognition unit can improve the recognition accuracy by adding environmental information of the temperature or humidity of the work environment to the collected data. The recognition unit can, for example, monitor the temperature of the work environment and adjust the work procedure according to the temperature change. The recognition unit can, for example, monitor the humidity of the work environment and guide the user to avoid a specific task when the humidity is high. The recognition unit can also monitor the illuminance of the work environment and provide visual assistance when the illuminance is low. This allows the recognition unit to improve the recognition accuracy by adding environmental information. Some or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI or may be performed without using AI. For example, the recognition unit can input temperature data of the work environment to the generation AI, and the generation AI can execute a process of adjusting the work procedure according to the temperature change.

[0040] The recognition unit can improve the recognition accuracy by adding the worker's past work history to the collected data. The recognition unit can, for example, analyze the worker's past work history, recognize similar work procedures, and provide guidance. The recognition unit can, for example, preferentially recognize a specific work procedure from the worker's past work history. The recognition unit can also predict the progress of the work based on the worker's past work history and provide guidance. This allows the recognition unit to improve the recognition accuracy by adding the past work history. A part or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input the worker's past work history data to the generation AI, and the generation AI can execute a process of recognizing the work procedure.

[0041] The recognition unit can improve the recognition accuracy by adding the worker's biometric information (heart rate, body temperature, etc.) to the collected data. For example, the recognition unit can monitor the worker's heart rate and adjust the progress of the work when the heart rate is high. For example, the recognition unit can monitor the worker's body temperature and encourage the worker to take a break when the body temperature is high. The recognition unit can also monitor the worker's breathing pattern and advise the worker to stop working when there is an abnormality. In this way, the recognition unit can improve the recognition accuracy by adding the biometric information. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the worker's biometric information data to the generation AI, and the generation AI can execute a process of analyzing the biometric information.

[0042] The generation unit can customize the guidance content by adding the worker's past work history to the generated voice guidance. The generation unit can, for example, provide guidance by emphasizing a specific procedure based on the worker's past work history. The generation unit can, for example, provide guidance by focusing on points where the worker often makes mistakes based on the worker's past work history. The generation unit can also analyze the worker's past work history and provide guidance on optimal work procedures. This allows the generation unit to customize the guidance content by adding the past work history. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the worker's past work history data into the generation AI, and the generation AI can execute a process of customizing the guidance content.

[0043] The generation unit can adjust the content of the generated voice guidance by taking into account the situation of the work environment. For example, when the noise level of the work environment is high, the generation unit can increase the volume of the voice guidance. For example, when the noise level of the work environment is low, the generation unit can decrease the volume of the voice guidance. The generation unit can also simplify the content of the voice guidance according to the noise level of the work environment. This allows the generation unit to adjust the content of the guidance by taking into account the situation of the work environment. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input noise level data of the work environment to the generation AI, and the generation AI can execute a process of adjusting the content of the voice guidance.

[0044] The generation unit can adjust the guidance content by adding the worker's biometric information to the generated voice guidance. For example, when the worker's heart rate is high, the generation unit can provide voice guidance with calming content. For example, when the worker's body temperature is high, the generation unit can provide voice guidance with content encouraging the worker to take a break. In addition, when the worker's breathing pattern is disturbed, the generation unit can also provide voice guidance with content encouraging the worker to take a deep breath. This allows the generation unit to adjust the guidance content by adding the biometric information. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the worker's biometric information data to the generation AI, and the generation AI can execute a process of adjusting the guidance content.

[0045] The generation unit can customize the guidance content by adding the geographical location information of the worker to the generated voice guidance. For example, when the worker is in a specific area, the generation unit can provide voice guidance of information related to the area. For example, when the worker is moving, the generation unit can provide voice guidance of information related to the destination. In addition, when the worker is in a specific location, the generation unit can also provide voice guidance of points to note related to the location. This allows the generation unit to customize the guidance content by adding the geographical location information. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the geographical location information data of the worker to the generation AI, and the generation AI can execute a process of customizing the guidance content.

[0046] The monitoring unit can improve the monitoring accuracy by adding the worker's past work history to the monitored data. The monitoring unit can, for example, focus on monitoring a specific procedure based on the worker's past work history. The monitoring unit can, for example, focus on monitoring points where the worker often makes mistakes based on the worker's past work history. The monitoring unit can also analyze the worker's past work history and suggest an optimal monitoring method. This allows the monitoring unit to improve the monitoring accuracy by adding the past work history. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the worker's past work history data to the generation AI, and the generation AI can execute a process of suggesting a monitoring method.

[0047] The monitoring unit can improve the monitoring accuracy by adding the status of the work environment to the monitored data. The monitoring unit can, for example, monitor the temperature of the work environment and adjust the monitoring accuracy according to the temperature change. The monitoring unit can, for example, monitor the humidity of the work environment and focus on monitoring a specific task when the humidity is high. The monitoring unit can also monitor the illuminance of the work environment and provide visual assistance when the illuminance is low. This allows the monitoring unit to improve the monitoring accuracy by adding environmental information. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input temperature data of the work environment to the generation AI, and the generation AI can execute a process of adjusting the monitoring accuracy according to the temperature change.

[0048] The monitoring unit can improve the monitoring accuracy by adding the worker's biometric information to the monitored data. The monitoring unit can, for example, monitor the worker's heart rate and adjust the progress of the work when the heart rate is high. The monitoring unit can, for example, monitor the worker's body temperature and encourage the worker to take a break when the body temperature is high. The monitoring unit can also monitor the worker's breathing pattern and advise the worker to stop working when an abnormality is detected. In this way, the monitoring unit can improve the monitoring accuracy by adding the biometric information. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the worker's biometric information data to the generation AI, and the generation AI can execute a process of analyzing the biometric information.

[0049] The monitoring unit can improve the monitoring accuracy by adding the geographical location information of the worker to the monitored data. For example, when the worker is in a specific area, the monitoring unit can focus on monitoring the work related to the area. For example, when the worker is moving, the monitoring unit can focus on monitoring the work related to the destination. In addition, when the worker is in a specific location, the monitoring unit can also focus on monitoring points related to the location. Thereby, the monitoring unit can improve the monitoring accuracy by adding the geographical location information. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the geographical location information data of the worker to the generation AI, and execute a process in which the generation AI suggests a monitoring method.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The AR utilization system may further include a feedback unit. The feedback unit may collect feedback from workers and use it to improve the system. For example, the feedback unit may collect the ease of use and improvements felt by workers regarding the voice guidance. The feedback unit may also collect stress and difficulties felt by workers while working and use it to adjust the system. Furthermore, the feedback unit may collect new functions and improvements proposed by workers and reflect them in system updates. In this way, the feedback unit may realize continuous improvements to the system that reflect the opinions of workers.

[0052] The recognition unit can use a vital sensor to monitor the health condition of the worker. The vital sensor can collect biological information such as heart rate, blood pressure, and oxygen saturation in real time. For example, the recognition unit can generate voice guidance encouraging the worker to take a break if the worker's heart rate is abnormally high. The recognition unit can also suggest interrupting work if the worker's blood pressure is high. Furthermore, the recognition unit can provide voice guidance encouraging the worker to take a deep breath if the oxygen saturation is low. This allows the recognition unit to monitor the worker's health condition and take appropriate measures.

[0053] The generation unit can provide audio guidance according to the individual learning style of the worker. For example, audio guidance including detailed explanations can be provided to a worker who prefers visual information. Also, audio guidance that is concise and to the point can be provided to a worker who prefers auditory information. Furthermore, audio guidance including specific procedures and examples can be provided to a worker who prefers practical learning. In this way, the generation unit can provide customized audio guidance according to the learning style of the worker.

[0054] The monitoring unit can suggest optimization of the work environment to improve the work efficiency of the worker. For example, if the lighting in the work area is insufficient, the monitoring unit can suggest installing additional lighting. Also, if the temperature in the work area is too high, the monitoring unit can suggest using a cooling device. Furthermore, if the noise level in the work area is high, the monitoring unit can suggest using earplugs. In this way, the monitoring unit can improve the work efficiency of the worker through optimization of the work environment.

[0055] The recognition unit can predict the progress of work based on the worker's past work history and provide guidance. For example, the recognition unit can analyze the work procedures that the worker has performed in the past and provide guidance on similar procedures. The recognition unit can also provide guidance that focuses on points where the worker made mistakes in the past. Furthermore, the recognition unit can suggest optimal work procedures based on the worker's past work history. This allows the recognition unit to predict the progress of work by taking into account the worker's past work history and provide appropriate guidance.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The recognition unit recognizes the situation at the work site. The situation at the work site includes the actions of the worker, the state of the equipment, the environmental conditions, etc. The recognition unit collects images and data at the work site using cameras and sensors. The cameras and sensors include fixed cameras, wearable sensors, infrared sensors, etc., and the collected images and data include image data, audio data, temperature data, etc. Step 2: The generation unit analyzes the information recognized by the recognition unit and generates voice guidance. The generation unit uses natural language processing technology and voice synthesis technology with generative AI to generate voice guidance. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis, while voice synthesis technology includes the use of text-to-speech synthesis and voice samples. The generation unit can also analyze the worker's voice input and provide appropriate answers by voice. Step 3: The monitoring unit monitors the worker's progress and the accuracy of the work in real time based on the voice guidance generated by the generation unit. The monitoring unit monitors the worker's progress and the accuracy of the work using sensors and cameras, and can adjust the data update frequency and monitoring timing in real time. This reduces the worker's reliance on their field of vision and enables the work to be carried out efficiently through voice guidance.

[0058] (Example 2) The AR utilization system according to an embodiment of the present invention is a system that realizes AR utilization with reduced reliance on visual field by combining AR (augmented reality) and generative AI in the transfer of skills and management of work processes. This system recognizes the situation at the work site and transmits the information to the generative AI. The generative AI analyzes the received information and generates appropriate voice guidance for the worker. This allows the worker to proceed with the work through the voice guidance without relying on the visual field. As a result, the AR utilization system reduces the worker's reliance on the visual field and enables the work to proceed efficiently through the voice guidance.

[0059] The AR utilization system according to the embodiment includes a recognition unit, a generation unit, and a monitoring unit. The recognition unit recognizes the situation of the work site. The situation of the work site includes, for example, the movement of the worker, the state of the equipment, and the environmental conditions, but is not limited to such examples. The recognition unit collects images and data of the work site using, for example, a camera or a sensor. The cameras and sensors include fixed cameras, wearable sensors, infrared sensors, and the like. The collected images and data include image data, voice data, temperature data, and the like. The generation unit uses a generation AI to analyze the information recognized by the recognition unit and generate voice guidance. The generation unit generates voice guidance using, for example, natural language processing technology or voice synthesis technology. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, and the like. Voice synthesis technology includes text-to-speech synthesis, the use of voice samples, and the like. The generation unit can also analyze the voice input of the worker and provide an appropriate answer by voice. For example, the generation unit uses voice recognition technology to analyze the voice input of the worker and generate an appropriate answer. The monitoring unit monitors the progress of the worker and the accuracy of the work in real time based on the voice guidance generated by the generation unit. The monitoring unit monitors the progress of the worker and the accuracy of the work using, for example, a sensor or a camera. The sensor or camera includes a motion sensor, a surveillance camera, a wearable camera, and the like. The monitoring unit performs monitoring in real time and can adjust the data update frequency and the monitoring timing. As a result, the AR utilization system according to the embodiment can reduce the dependency of the worker on the field of vision and efficiently proceed with the work through the voice guidance.

[0060] The recognition unit recognizes the situation at the work site. The situation at the work site includes, for example, the behavior of the worker, the state of the equipment, the environmental conditions, and the like, but is not limited to such examples. The recognition unit collects images and data at the work site using, for example, a camera or a sensor. The cameras and sensors include fixed cameras, wearable sensors, infrared sensors, and the like. The collected images and data include image data, audio data, temperature data, and the like. Specifically, the fixed cameras monitor the entire work site over a wide area and record the behavior of the worker and the state of the equipment in detail. The wearable sensors are worn on the body of the worker and collect biometric information such as the worker's movement, posture, and heart rate in real time. The infrared sensors detect temperature changes and the location of heat sources, and are useful for temperature management of the work environment and abnormality detection. These data are managed centrally by the recognition unit and used to comprehensively grasp the situation at the work site. Furthermore, the recognition unit can analyze the collected data in real time and immediately detect abnormal behavior and changes in the environment. For example, if a worker does not perform a prescribed action or if a device is operating abnormally, the recognition unit immediately notifies the generation unit and monitoring unit of that information, thereby improving the safety and efficiency of the workplace.

[0061] The generation unit uses the generation AI to analyze the information recognized by the recognition unit and generate voice guidance. The generation unit generates voice guidance using, for example, natural language processing technology or voice synthesis technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. Voice synthesis technology includes text-to-speech synthesis and the use of voice samples. Specifically, the generation AI analyzes the data provided by the recognition unit and provides appropriate instructions and advice to the worker by voice. For example, if the worker forgets a specific work procedure, the generation unit guides the worker through the procedure by voice. In addition, when the worker inputs voice, the generation unit analyzes the voice and generates an appropriate answer. The voice recognition technology is used to analyze the worker's voice input and generate an appropriate answer. For example, when the worker asks, "What should I do next?", the generation unit analyzes the question and guides the worker through the next work procedure by voice. In addition, the generation unit can dynamically change the content of the voice guidance depending on the progress of the worker and the environmental conditions. For example, if the temperature of the work environment becomes high, the generation unit guides the worker through voice to take a break. This enables the generation unit to provide appropriate instructions to workers in real time, improving work efficiency and safety.

[0062] The monitoring unit monitors the progress of the worker and the accuracy of the work in real time based on the voice guidance generated by the generation unit. The monitoring unit monitors the progress of the worker and the accuracy of the work using, for example, a sensor or a camera. The sensors and cameras include a motion sensor, a surveillance camera, a wearable camera, and the like. Specifically, the motion sensor records the movement of the worker in detail and monitors whether the specified movement is being performed accurately. The surveillance camera monitors the entire work site over a wide area and records the movement of the worker and the state of the equipment in detail. The wearable camera provides images from the viewpoint of the worker in real time, allowing the progress of the work to be grasped in detail. The monitoring unit performs monitoring in real time and can adjust the frequency of updating the data and the timing of monitoring. For example, if the worker does not perform the specified movement or if the operation of the equipment is abnormal, the monitoring unit immediately notifies the generation unit or the recognition unit of the information. This can improve the safety and efficiency of the work site. Furthermore, the monitoring unit can analyze the collected data and evaluate the progress and accuracy of the work. For example, the monitoring department evaluates whether workers are completing tasks within the specified time and whether work procedures are being followed accurately, and points out areas for improvement as necessary. This allows the monitoring department to improve the quality of work and also contribute to improving the skills of workers.

[0063] The recognition unit can collect images or data of the work site using a camera or a sensor. The camera or sensor includes, for example, a fixed camera, a wearable sensor, an infrared sensor, and the like. The collected images and data include, for example, image data, audio data, and temperature data. The recognition unit can collect images of the work site using, for example, a fixed camera. The recognition unit can also collect operation data of the worker using a wearable sensor. Furthermore, the recognition unit can collect temperature data of the work environment using an infrared sensor. This allows the recognition unit to accurately recognize the situation of the work site. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recognition unit can input image data acquired by a camera to the generation AI and cause the generation AI to execute a process of recognizing the situation of the work site from the image data.

[0064] The generation unit can generate the voice guidance using natural language processing technology or voice synthesis technology. The generation unit can generate the voice guidance using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, and the like. The generation unit can, for example, divide a sentence using morphological analysis, analyze the structure of the sentence using grammatical analysis, and understand the meaning of the sentence using semantic analysis. The generation unit can also generate the voice guidance using voice synthesis technology. Voice synthesis technology includes text-to-speech synthesis, the use of voice samples, and the like. The generation unit can, for example, convert text data into voice data using text-to-speech synthesis. The generation unit can also generate natural voice guidance using voice samples. This allows the generation unit to generate appropriate voice guidance. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis result using the natural language processing technology to the generation AI, and the generation AI can execute a process of generating voice guidance.

[0065] The monitoring unit can monitor the progress or accuracy of the work of the worker in real time using a sensor or a camera. The monitoring unit can monitor the progress of the worker in real time using, for example, a sensor. The sensor includes a motion sensor, a surveillance camera, a wearable camera, and the like. The monitoring unit can monitor the motion of the worker using, for example, a motion sensor to grasp the progress. The monitoring unit can also monitor the work procedure of the worker using a surveillance camera to evaluate the accuracy of the work. Furthermore, the monitoring unit can also monitor the progress of the work from the viewpoint of the worker using a wearable camera. This allows the monitoring unit to monitor the progress and accuracy of the work in real time. Part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data acquired by the sensor to the generation AI, and the generation AI can execute a process of monitoring the progress and accuracy of the work.

[0066] The generation unit can analyze the voice input of the worker and provide an appropriate answer by voice. The generation unit can analyze the voice input of the worker using, for example, voice recognition technology. Voice recognition technology includes voice feature extraction, voice to text conversion, and voice semantic analysis. The generation unit can extract features from voice data using, for example, voice feature extraction, convert voice data to text data using voice to text conversion, and understand the content of the voice using voice semantic analysis. The generation unit can generate an appropriate answer based on the result of the analysis using the voice recognition technology. For example, the generation unit can generate an appropriate answer to a question of the worker and provide it by voice. This allows the generation unit to provide an appropriate answer to a question or confirmation of the worker. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis result using the voice recognition technology to the generation AI, and the generation AI can execute a process of generating an appropriate answer.

[0067] The generation unit can provide additional instructions or corrections according to the progress of the worker. The generation unit can, for example, monitor the progress of the worker in real time and provide additional instructions or corrections according to the progress. The generation unit can monitor the worker's actions and work procedures using a sensor or a camera to grasp the progress. The generation unit can provide additional instructions, for example, when the progress of the worker is delayed. In addition, the generation unit can also provide correction instructions when the worker is performing an incorrect procedure. This allows the generation unit to provide appropriate instructions and corrections according to the progress of the work. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input data monitoring the progress to the generation AI, and the generation AI can execute a process of generating additional instructions and corrections.

[0068] The recognition unit can estimate the emotion of the worker and determine the priority of the information to be recognized based on the estimated emotion of the worker. The recognition unit can use, for example, techniques such as facial expression recognition, voice analysis, and behavioral pattern analysis to estimate the emotion of the worker. The recognition unit can estimate the emotion from the facial expression of the worker using facial expression recognition. The recognition unit can also estimate the emotion from the voice of the worker using voice analysis. Furthermore, the recognition unit can estimate the emotion from the movement of the worker using behavioral pattern analysis. The recognition unit can determine the priority of the information to be recognized based on the estimated emotion of the worker. For example, if the worker is stressed, important information can be recognized with priority and provided quickly. In addition, if the worker is relaxed, detailed information can be recognized and comprehensive guidance can be provided. Furthermore, if the worker is impatient, the most important procedures and points of attention can be recognized with priority and guidance can be provided immediately. This allows the recognition unit to determine the priority of information according to the emotion of the worker. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit may input facial expression data of a worker to the generation AI, and the generation AI may execute a process of estimating emotions.

[0069] The recognition unit can improve the recognition accuracy by adding the movement pattern of the worker to the collected data. The recognition unit can, for example, track the movement of the worker's hands and recognize a specific movement pattern to determine the progress of the work. The recognition unit can, for example, analyze the walking pattern of the worker and recognize the movement in the work area to grasp the progress of the work. The recognition unit can also track the movement of the worker's line of sight and recognize an object in the line of sight to support the progress of the work. In this way, the recognition unit can improve the recognition accuracy by adding the movement pattern of the worker. A part or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input the movement data of the worker to the generation AI, and the generation AI can execute a process of analyzing the movement pattern.

[0070] The recognition unit can improve the recognition accuracy by adding environmental information of the temperature or humidity of the work environment to the collected data. The recognition unit can, for example, monitor the temperature of the work environment and adjust the work procedure according to the temperature change. The recognition unit can, for example, monitor the humidity of the work environment and guide the user to avoid a specific task when the humidity is high. The recognition unit can also monitor the illuminance of the work environment and provide visual assistance when the illuminance is low. This allows the recognition unit to improve the recognition accuracy by adding environmental information. Some or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI or may be performed without using AI. For example, the recognition unit can input temperature data of the work environment to the generation AI, and the generation AI can execute a process of adjusting the work procedure according to the temperature change.

[0071] The recognition unit can estimate the emotion of the worker and filter the information to be recognized based on the estimated emotion of the worker. For example, the recognition unit can use techniques such as facial expression recognition, voice analysis, and behavior pattern analysis to estimate the emotion of the worker. The recognition unit can estimate the emotion from the facial expression of the worker using facial expression recognition. The recognition unit can also estimate the emotion from the voice of the worker using voice analysis. Furthermore, the recognition unit can estimate the emotion from the movement of the worker using behavior pattern analysis. The recognition unit can filter the information to be recognized based on the estimated emotion of the worker. For example, when the worker is stressed, unnecessary information can be filtered and only important information can be provided. Furthermore, when the worker is relaxed, detailed information can be provided without filtering. Furthermore, when the worker is impatient, the most important procedures and points to note can be provided without filtering. This allows the recognition unit to filter information according to the emotion of the worker. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit may input facial expression data of a worker to the generation AI, and the generation AI may execute a process of estimating emotions.

[0072] The recognition unit can improve the recognition accuracy by adding the worker's past work history to the collected data. The recognition unit can, for example, analyze the worker's past work history, recognize similar work procedures, and provide guidance. The recognition unit can, for example, preferentially recognize a specific work procedure from the worker's past work history. The recognition unit can also predict the progress of the work based on the worker's past work history and provide guidance. This allows the recognition unit to improve the recognition accuracy by adding the past work history. A part or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input the worker's past work history data to the generation AI, and the generation AI can execute a process of recognizing the work procedure.

[0073] The recognition unit can improve the recognition accuracy by adding the worker's biometric information (heart rate, body temperature, etc.) to the collected data. For example, the recognition unit can monitor the worker's heart rate and adjust the progress of the work when the heart rate is high. For example, the recognition unit can monitor the worker's body temperature and encourage the worker to take a break when the body temperature is high. The recognition unit can also monitor the worker's breathing pattern and advise the worker to stop working when there is an abnormality. In this way, the recognition unit can improve the recognition accuracy by adding the biometric information. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the worker's biometric information data to the generation AI, and the generation AI can execute a process of analyzing the biometric information.

[0074] The generation unit can estimate the emotion of the worker and adjust the tone and speed of the voice guidance based on the estimated emotion of the worker. For example, the generation unit can use techniques such as facial expression recognition, voice analysis, and behavior pattern analysis to estimate the emotion of the worker. The generation unit can estimate the emotion from the facial expression of the worker using facial expression recognition. The generation unit can also estimate the emotion from the voice of the worker using voice analysis. Furthermore, the generation unit can estimate the emotion from the movement of the worker using behavior pattern analysis. The generation unit can adjust the tone and speed of the voice guidance based on the estimated emotion of the worker. For example, if the worker is relaxed, the voice guidance can be provided in a relaxed tone. Furthermore, if the worker is in a hurry, the voice guidance can be provided in a quick tone. Furthermore, if the worker is nervous, the voice guidance can be provided in a calm tone. This enables the generation unit to adjust the tone and speed of the voice guidance according to the emotion of the worker. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may input facial expression data of a worker to the generation AI, and the generation AI may execute a process of estimating emotions.

[0075] The generation unit can customize the guidance content by adding the worker's past work history to the generated voice guidance. The generation unit can, for example, provide guidance by emphasizing a specific procedure based on the worker's past work history. The generation unit can, for example, provide guidance by focusing on points where the worker often makes mistakes based on the worker's past work history. The generation unit can also analyze the worker's past work history and provide guidance on optimal work procedures. This allows the generation unit to customize the guidance content by adding the past work history. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the worker's past work history data into the generation AI, and the generation AI can execute a process of customizing the guidance content.

[0076] The generation unit can adjust the content of the generated voice guidance by taking into account the situation of the work environment. For example, when the noise level of the work environment is high, the generation unit can increase the volume of the voice guidance. For example, when the noise level of the work environment is low, the generation unit can decrease the volume of the voice guidance. The generation unit can also simplify the content of the voice guidance according to the noise level of the work environment. This allows the generation unit to adjust the content of the guidance by taking into account the situation of the work environment. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input noise level data of the work environment to the generation AI, and the generation AI can execute a process of adjusting the content of the voice guidance.

[0077] The generation unit can estimate the emotion of the worker and adjust the content of the voice guidance based on the estimated emotion of the worker. For example, the generation unit can use technologies such as facial expression recognition, voice analysis, and behavior pattern analysis to estimate the emotion of the worker. The generation unit can estimate the emotion from the facial expression of the worker using facial expression recognition. The generation unit can also estimate the emotion from the voice of the worker using voice analysis. Furthermore, the generation unit can estimate the emotion from the movement of the worker using behavior pattern analysis. The generation unit can adjust the content of the voice guidance based on the estimated emotion of the worker. For example, if the worker is relaxed, voice guidance including detailed explanations can be provided. Furthermore, if the worker is in a hurry, concise voice guidance with the main points can be provided. Furthermore, if the worker is nervous, voice guidance with content that gives a sense of security can be provided. This enables the generation unit to adjust the content of the voice guidance according to the emotion of the worker. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may input facial expression data of a worker to the generation AI, and the generation AI may execute a process of estimating emotions.

[0078] The generation unit can adjust the guidance content by adding the worker's biometric information to the generated voice guidance. For example, when the worker's heart rate is high, the generation unit can provide voice guidance with calming content. For example, when the worker's body temperature is high, the generation unit can provide voice guidance with content encouraging the worker to take a break. In addition, when the worker's breathing pattern is disturbed, the generation unit can also provide voice guidance with content encouraging the worker to take a deep breath. This allows the generation unit to adjust the guidance content by adding the biometric information. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the worker's biometric information data to the generation AI, and the generation AI can execute a process of adjusting the guidance content.

[0079] The generation unit can customize the guidance content by adding the geographical location information of the worker to the generated voice guidance. For example, when the worker is in a specific area, the generation unit can provide voice guidance of information related to the area. For example, when the worker is moving, the generation unit can provide voice guidance of information related to the destination. In addition, when the worker is in a specific location, the generation unit can also provide voice guidance of points to note related to the location. This allows the generation unit to customize the guidance content by adding the geographical location information. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the geographical location information data of the worker to the generation AI, and the generation AI can execute a process of customizing the guidance content.

[0080] The monitoring unit can estimate the emotion of the worker and adjust the frequency and accuracy of monitoring based on the estimated emotion of the worker. For example, the monitoring unit can use technologies such as facial expression recognition, voice analysis, and behavior pattern analysis to estimate the emotion of the worker. The monitoring unit can estimate the emotion from the facial expression of the worker using facial expression recognition. The monitoring unit can also estimate the emotion from the voice of the worker using voice analysis. Furthermore, the monitoring unit can estimate the emotion from the movement of the worker using behavior pattern analysis. The monitoring unit can adjust the frequency and accuracy of monitoring based on the estimated emotion of the worker. For example, if the worker is stressed, the frequency of monitoring can be increased to strengthen support. Also, if the worker is relaxed, the frequency of monitoring can be reduced to respect the worker's autonomy. Furthermore, if the worker is impatient, the accuracy of monitoring can be increased to provide quick feedback. This enables the monitoring unit to adjust the frequency and accuracy of monitoring according to the emotion of the worker. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit may input facial expression data of a worker to the generation AI, and the generation AI may execute a process of estimating emotions.

[0081] The monitoring unit can improve the monitoring accuracy by adding the worker's past work history to the monitored data. The monitoring unit can, for example, focus on monitoring a specific procedure based on the worker's past work history. The monitoring unit can, for example, focus on monitoring points where the worker often makes mistakes based on the worker's past work history. The monitoring unit can also analyze the worker's past work history and suggest an optimal monitoring method. This allows the monitoring unit to improve the monitoring accuracy by adding the past work history. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the worker's past work history data to the generation AI, and the generation AI can execute a process of suggesting a monitoring method.

[0082] The monitoring unit can improve the monitoring accuracy by adding the status of the work environment to the monitored data. The monitoring unit can, for example, monitor the temperature of the work environment and adjust the monitoring accuracy according to the temperature change. The monitoring unit can, for example, monitor the humidity of the work environment and focus on monitoring a specific task when the humidity is high. The monitoring unit can also monitor the illuminance of the work environment and provide visual assistance when the illuminance is low. This allows the monitoring unit to improve the monitoring accuracy by adding environmental information. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input temperature data of the work environment to the generation AI, and the generation AI can execute a process of adjusting the monitoring accuracy according to the temperature change.

[0083] The monitoring unit can estimate the emotion of the worker and determine the monitoring priority based on the estimated emotion of the worker. The monitoring unit can use, for example, techniques such as facial expression recognition, voice analysis, and behavior pattern analysis to estimate the emotion of the worker. The monitoring unit can estimate the emotion from the facial expression of the worker using facial expression recognition. The monitoring unit can also estimate the emotion from the voice of the worker using voice analysis. Furthermore, the monitoring unit can estimate the emotion from the movement of the worker using behavior pattern analysis. The monitoring unit can determine the monitoring priority based on the estimated emotion of the worker. For example, when the worker is stressed, important work can be monitored with priority. Furthermore, when the worker is relaxed, the overall work can be monitored evenly. Furthermore, when the worker is in a hurry, the most important procedures and points of attention can be monitored with priority. This enables the monitoring unit to determine the monitoring priority according to the emotion of the worker. The emotion estimation is realized using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input facial expression data of the worker to a generation AI, and the generation AI may execute a process of estimating the emotion.

[0084] The monitoring unit can improve the monitoring accuracy by adding the worker's biometric information to the monitored data. The monitoring unit can, for example, monitor the worker's heart rate and adjust the progress of the work when the heart rate is high. The monitoring unit can, for example, monitor the worker's body temperature and encourage the worker to take a break when the body temperature is high. The monitoring unit can also monitor the worker's breathing pattern and advise the worker to stop working when an abnormality is detected. In this way, the monitoring unit can improve the monitoring accuracy by adding the biometric information. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the worker's biometric information data to the generation AI, and the generation AI can execute a process of analyzing the biometric information.

[0085] The monitoring unit can improve the monitoring accuracy by adding the geographical location information of the worker to the monitored data. For example, when the worker is in a specific area, the monitoring unit can focus on monitoring the work related to the area. For example, when the worker is moving, the monitoring unit can focus on monitoring the work related to the destination. In addition, when the worker is in a specific location, the monitoring unit can also focus on monitoring points related to the location. Thereby, the monitoring unit can improve the monitoring accuracy by adding the geographical location information. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the geographical location information data of the worker to the generation AI, and execute a process in which the generation AI suggests a monitoring method.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The AR utilization system may further include a feedback unit. The feedback unit may collect feedback from workers and use it to improve the system. For example, the feedback unit may collect the ease of use and improvements felt by workers regarding the voice guidance. The feedback unit may also collect stress and difficulties felt by workers while working and use it to adjust the system. Furthermore, the feedback unit may collect new functions and improvements proposed by workers and reflect them in system updates. In this way, the feedback unit may realize continuous improvements to the system that reflect the opinions of workers.

[0088] The recognition unit can use a vital sensor to monitor the health condition of the worker. The vital sensor can collect biological information such as heart rate, blood pressure, and oxygen saturation in real time. For example, the recognition unit can generate voice guidance encouraging the worker to take a break if the worker's heart rate is abnormally high. The recognition unit can also suggest interrupting work if the worker's blood pressure is high. Furthermore, the recognition unit can provide voice guidance encouraging the worker to take a deep breath if the oxygen saturation is low. This allows the recognition unit to monitor the worker's health condition and take appropriate measures.

[0089] The generation unit can provide audio guidance according to the individual learning style of the worker. For example, audio guidance including detailed explanations can be provided to a worker who prefers visual information. Also, audio guidance that is concise and to the point can be provided to a worker who prefers auditory information. Furthermore, audio guidance including specific procedures and examples can be provided to a worker who prefers practical learning. In this way, the generation unit can provide customized audio guidance according to the learning style of the worker.

[0090] The monitoring unit can suggest optimization of the work environment to improve the work efficiency of the worker. For example, if the lighting in the work area is insufficient, the monitoring unit can suggest installing additional lighting. Also, if the temperature in the work area is too high, the monitoring unit can suggest using a cooling device. Furthermore, if the noise level in the work area is high, the monitoring unit can suggest using earplugs. In this way, the monitoring unit can improve the work efficiency of the worker through optimization of the work environment.

[0091] The generation unit can estimate the emotion of the worker and adjust the content of the voice guidance based on the estimated emotion. For example, if the worker is feeling stressed, voice guidance including advice for relaxing can be provided. Also, if the worker is tired, voice guidance encouraging the worker to take a break can be provided. Furthermore, if the worker is concentrating, voice guidance including detailed procedures can be provided. In this way, the generation unit can provide appropriate voice guidance according to the emotion of the worker.

[0092] The recognition unit can estimate the emotion of the worker and adjust the priority of tasks based on the estimated emotion. For example, if the worker is in a hurry, the most important task can be given priority. Also, if the worker is relaxed, tasks including detailed steps can be guided. Furthermore, if the worker is feeling stressed, the worker can be guided to start with an easy task. In this way, the recognition unit can adjust the priority of tasks according to the emotion of the worker.

[0093] The generation unit can estimate the emotion of the worker and adjust the tone and speed of the voice guidance based on the estimated emotion. For example, if the worker is relaxed, the voice guidance can be provided in a relaxed tone. If the worker is in a hurry, the voice guidance can be provided in a quick tone. Furthermore, if the worker is nervous, the voice guidance can be provided in a calm tone. This enables the generation unit to adjust the tone and speed of the voice guidance according to the emotion of the worker.

[0094] The monitoring unit can estimate the emotion of the worker and adjust the frequency and accuracy of monitoring based on the estimated emotion. For example, if the worker is feeling stressed, the frequency of monitoring can be increased to strengthen support. Also, if the worker is relaxed, the frequency of monitoring can be decreased to respect the worker's autonomy. Furthermore, if the worker is impatient, the accuracy of monitoring can be increased to provide quick feedback. This enables the monitoring unit to adjust the frequency and accuracy of monitoring according to the emotion of the worker.

[0095] The generation unit can estimate the emotion of the worker and adjust the content of the voice guidance based on the estimated emotion. For example, if the worker is relaxed, voice guidance including detailed explanations can be provided. If the worker is in a hurry, concise voice guidance focusing on the main points can be provided. Furthermore, if the worker is nervous, voice guidance with content that gives a sense of security can be provided. This enables the generation unit to adjust the content of the voice guidance according to the emotion of the worker.

[0096] The recognition unit can predict the progress of work based on the worker's past work history and provide guidance. For example, the recognition unit can analyze the work procedures that the worker has performed in the past and provide guidance on similar procedures. The recognition unit can also provide guidance that focuses on points where the worker made mistakes in the past. Furthermore, the recognition unit can suggest optimal work procedures based on the worker's past work history. This allows the recognition unit to predict the progress of work by taking into account the worker's past work history and provide appropriate guidance.

[0097] The process flow of the second embodiment will be briefly described below.

[0098] Step 1: The recognition unit recognizes the situation at the work site. The situation at the work site includes the actions of the worker, the state of the equipment, the environmental conditions, etc. The recognition unit collects images and data at the work site using cameras and sensors. The cameras and sensors include fixed cameras, wearable sensors, infrared sensors, etc., and the collected images and data include image data, audio data, temperature data, etc. Step 2: The generation unit analyzes the information recognized by the recognition unit and generates voice guidance. The generation unit uses natural language processing technology and voice synthesis technology with generative AI to generate voice guidance. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis, while voice synthesis technology includes the use of text-to-speech synthesis and voice samples. The generation unit can also analyze the worker's voice input and provide appropriate answers by voice. Step 3: The monitoring unit monitors the worker's progress and the accuracy of the work in real time based on the voice guidance generated by the generation unit. The monitoring unit monitors the worker's progress and the accuracy of the work using sensors and cameras, and can adjust the data update frequency and monitoring timing in real time. This reduces the worker's reliance on their field of vision and enables the work to be carried out efficiently through voice guidance.

[0099] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0101] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements including the recognition unit, generation unit, and monitoring unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recognition unit collects images and data of the work site using the camera 42 and sensors of the smart device 14, and recognizes the situation of the work site by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the recognized information using a generation AI, and generates voice guidance. The monitoring unit monitors the progress of the worker and the accuracy of the work by using, for example, the camera 42 and sensors of the smart device 14, and monitors in real time by the control unit 46A. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0110] Fig. 4 shows an example of 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.

[0111] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0115] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0117] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements including the above-mentioned recognition unit, generation unit, and monitoring unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recognition unit collects images and data of the work site using the camera 42 and sensors of the smart glasses 214, and recognizes the situation of the work site by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the recognized information using a generation AI, and generates voice guidance. The monitoring unit monitors the progress of the worker and the accuracy of the work by using, for example, the camera 42 and sensors of the smart glasses 214, and monitors in real time by the control unit 46A. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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, the RAM 30, and the 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.

[0122] 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.

[0123] 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 the voice according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0125] 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 performed in a secure state.

[0126] 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.

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

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

[0129] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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 identification processing unit 290 using these models.

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

[0131] 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 voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0133] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements including the above-mentioned recognition unit, generation unit, and monitoring unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the recognition unit collects images and data of the work site using the camera 42 and sensors of the headset type terminal 314, and recognizes the situation of the work site by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the recognized information using a generation AI, and generates voice guidance. The monitoring unit monitors the progress of the worker and the accuracy of the work by using, for example, the camera 42 and sensors of the headset type terminal 314, and monitors in real time by the control unit 46A. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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, the RAM 30, and the 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.

[0138] 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.

[0139] 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 the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0141] 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 performed in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0143] 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.

[0144] 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.

[0145] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0146] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0147] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0148] 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 a voice indicating a user input for 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.

[0149] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0150] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0151] Each of the multiple elements including the above-mentioned recognition unit, generation unit, and monitoring unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recognition unit collects images and data of the work site using the camera 42 and sensors of the robot 414, and recognizes the situation of the work site by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the recognized information using a generation AI, and generates voice guidance. The monitoring unit monitors the progress of the worker and the accuracy of the work by using, for example, the camera 42 and sensors of the robot 414, and monitors in real time by the control unit 46A. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0152] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0153] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0155] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0157] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0159] 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.

[0160] 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 Universal Serial Bus (USB) 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.

[0161] In addition, 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 upon request from the data processing device 12.

[0162] 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.

[0163] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0164] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0165] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0166] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0168] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0170] (Appendix 1) A recognition unit that recognizes the situation at the work site; A generation unit that analyzes the information recognized by the recognition unit and generates voice guidance; A monitoring unit that monitors the progress and accuracy of the work of the worker in real time based on the voice guidance generated by the generating unit. A system characterized by: (Appendix 2) The recognition unit is Collecting images or data from the work site using cameras or sensors 2. The system of claim 1, (Appendix 3) The generation unit is Generate voice guidance using natural language processing or speech synthesis technology 2. The system of claim 1, (Appendix 4) The monitoring unit is Use sensors or cameras to monitor worker progress or task accuracy in real time 2. The system of claim 1, (Appendix 5) The generation unit is Analyzes the worker's voice input and provides appropriate responses via voice 2. The system of claim 1, (Appendix 6) The generation unit is Provide additional instructions or corrections according to the worker's progress 2. The system of claim 1, (Appendix 7) The recognition unit is Estimate the emotions of the worker and determine the priority of information to be recognized based on the estimated emotions of the worker. 2. The system of claim 1, (Appendix 8) The recognition unit is Improve recognition accuracy by incorporating worker movement patterns into collected data 2. The system of claim 1, (Appendix 9) The recognition unit is Improve recognition accuracy by adding environmental information such as temperature or humidity of the work environment to the collected data 2. The system of claim 1, (Appendix 10) The recognition unit is Estimate the worker's emotions and filter the information to be recognized based on the estimated worker's emotions. 2. The system of claim 1, (Appendix 11) The recognition unit is Improve recognition accuracy by incorporating the worker's past work history into the collected data 2. The system of claim 1, (Appendix 12) The recognition unit is Improve recognition accuracy by incorporating workers' biometric information into collected data 2. The system of claim 1, (Appendix 13) The generation unit is Estimate the worker's emotions and adjust the tone and speed of voice guidance based on the estimated worker's emotions. 2. The system of claim 1, (Appendix 14) The generation unit is Customize the generated voice guidance by taking into account the worker's past work history 2. The system of claim 1, (Appendix 15) The generation unit is Adjust the generated voice guidance content by taking into account the working environment. 2. The system of claim 1, (Appendix 16) The generation unit is Estimating the emotions of the worker and adjusting the content of the voice guidance based on the estimated emotions of the worker 2. The system of claim 1, (Appendix 17) The generation unit is The voice guidance content is adjusted by taking into account the worker's biometric information. 2. The system of claim 1, (Appendix 18) The generation unit is Customize the voice guidance content by adding the worker's geographic location information to the generated voice guidance 2. The system of claim 1, (Appendix 19) The monitoring unit is Estimate the emotions of workers and adjust the frequency and accuracy of monitoring based on the estimated emotions of workers. 2. The system of claim 1, (Appendix 20) The monitoring unit is Improve monitoring accuracy by adding the worker's past work history to the monitoring data 2. The system of claim 1, (Appendix 21) The monitoring unit is Improve monitoring accuracy by adding the working environment conditions to the monitored data 2. The system of claim 1, (Appendix 22) The monitoring unit is Estimating worker emotions and determining monitoring priorities based on the estimated worker emotions 2. The system of claim 1, (Appendix 23) The monitoring unit is Improve monitoring accuracy by adding workers' biological information to the monitoring data 2. The system of claim 1, (Appendix 24) The monitoring unit is Improve monitoring accuracy by adding workers' geographic location information to the monitoring data 2. The system of claim 1, [Explanation of symbols]

[0171] 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 recognition unit that recognizes the situation at the work site; A generation unit that analyzes the information recognized by the recognition unit and generates voice guidance; A monitoring unit that monitors the progress and accuracy of the work of the worker in real time based on the voice guidance generated by the generating unit. A system characterized by:

2. The recognition unit is Collecting images or data from the work site using cameras or sensors 2. The system of claim 1 .

3. The generation unit is Generate voice guidance using natural language processing or speech synthesis technology 2. The system of claim 1 .

4. The monitoring unit is Use sensors or cameras to monitor worker progress or task accuracy in real time 2. The system of claim 1 .

5. The generation unit is Analyzes the worker's voice input and provides appropriate responses via voice 2. The system of claim 1 .

6. The generation unit is Provide additional instructions or corrections according to the worker's progress 2. The system of claim 1 .

7. The recognition unit is Estimate the emotions of the worker and determine the priority of information to be recognized based on the estimated emotions of the worker.

2. The system of claim 1 .

8. The recognition unit is Improve recognition accuracy by incorporating worker movement patterns into collected data 2. The system of claim 1 .

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