system

Abstract

The system according to this embodiment aims to prevent industrial accidents and create a safe working environment. [Solution] The system according to the embodiment comprises a learning unit, a provision unit, an analysis unit, and a proposal unit. The learning unit learns data on past industrial accidents. The provision unit provides preventive measures based on the data learned by the learning unit. The analysis unit analyzes videos recorded of the workplace. The proposal unit proposes improvements based on the results of the analysis performed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a lack of specific understanding and guidance for preventing all industrial accidents, and there is room for improvement.

[0005] The system according to the embodiment aims to prevent industrial accidents and realize a safe workplace environment.

Means for Solving the Problems

[0006] The system according to the embodiment includes a learning unit, a providing unit, an analyzing unit, and a proposing unit. The learning unit learns data on past industrial accidents. The providing unit provides preventive measures based on the data learned by the learning unit. The analyzing unit analyzes a video recording the situation of the workplace. The proposing unit proposes improvement points based on the results analyzed by the analyzing unit. [Effects of the Invention]

[0007] The system according to this embodiment can prevent industrial accidents and create a safe working environment. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​coaching system according to an embodiment of the present invention is a system that learns detailed reports and causes of various occupational accidents to prevent dangerous situations and actions and provides guidance to realize a safe workplace environment. The AI ​​coaching system learns detailed reports and causes of past occupational accidents. In this process, it analyzes occupational accident occurrence cases in each workplace and trends for each type of equipment used. Next, based on the learned data, the AI ​​coaching system provides specific occupational accident prevention measures tailored to the situation of each workplace. Furthermore, the AI ​​coaching system analyzes videos recorded of the workplace and proposes areas for improvement and points to note from the perspective of occupational accident prevention. This system provides concrete and practical occupational accident prevention measures and improves the safety environment of the workplace. In addition, even if there is a shortage of personnel who can provide guidance, the AI ​​coaching system can provide guidance on their behalf, minimizing the risk of occupational accidents. For example, the AI ​​coaching system provides specific guidance such as: As a safety measure when working at height, it provides guidance on the proper use of safety harnesses and how to inspect scaffolding. As a point of caution when operating machinery, it provides guidance on checking safety devices on machinery and adhering to operating procedures. It analyzes videos recorded of the workplace and proposes areas for improvement regarding worker movements and the work environment. In this way, the AI ​​coaching system provides concrete and practical preventive measures against workplace accidents based on past accident data, thereby creating a safe work environment. Thus, the AI ​​coaching system can provide concrete and practical preventive measures against workplace accidents and create a safe work environment.

[0029] The AI ​​coaching system according to this embodiment comprises a learning unit, a provision unit, an analysis unit, and a proposal unit. The learning unit learns data on past industrial accidents. For example, the learning unit obtains detailed reports and causes of past industrial accidents from a database and learns using a machine learning algorithm. For example, the learning unit can learn data such as the type of accident, the date and time of occurrence, the cause, and the extent of the damage. The provision unit provides preventive measures based on the data learned by the learning unit. For example, the provision unit provides specific industrial accident preventive measures tailored to the situation of each workplace based on the learned data. For example, the provision unit can provide preventive measures such as safety education, equipment improvements, and revisions to work procedures. The analysis unit analyzes videos recorded of the workplace. For example, the analysis unit obtains videos recorded of the workplace and analyzes them using an image analysis algorithm. For example, the analysis unit can identify dangerous behaviors and situations. The proposal unit proposes improvements based on the results analyzed by the analysis unit. For example, the proposal unit presents specific improvement measures based on the analysis results. The proposal department can suggest improvement measures such as changes to work procedures, equipment improvements, and enhanced training. This allows the AI ​​coaching system, according to the embodiment, to learn from past industrial accident data, provide preventative measures, analyze workplace conditions, and propose improvements, thereby creating a safer work environment.

[0030] The learning unit learns from data on past workplace accidents. For example, the learning unit obtains detailed reports and causes of past workplace accidents from a database and learns from them using machine learning algorithms. Specifically, the learning unit collects data such as the type of accident, the date and time of occurrence, the cause, and the extent of the damage, and preprocesses this data before inputting it into the machine learning model. Preprocessing includes data cleaning, normalization, and feature extraction. For example, important keywords are extracted from text data and converted into numerical data to enable the machine learning model to learn efficiently. The learning unit combines different learning methods such as supervised learning, unsupervised learning, and reinforcement learning to identify patterns and causes of workplace accidents with high accuracy. Furthermore, the learning unit regularly incorporates new data and updates the model, enabling predictions and analyses to always be based on the latest information. As a result, the learning unit can effectively learn from past workplace accident data and provide insights that can help prevent future accidents.

[0031] The service provider provides preventive measures based on data learned by the learning provider. For example, the service provider provides specific workplace accident prevention measures tailored to the specific circumstances of each workplace, based on the learned data. Specifically, the service provider conducts risk assessments for each workplace based on the knowledge gained from the learning provider and proposes appropriate preventive measures. For example, it identifies the causes of accidents that frequently occur in a particular work environment and presents specific measures to eliminate those causes. The service provider can provide a wide range of preventive measures, such as designing safety education programs, proposing equipment improvements, and reviewing work procedures. Furthermore, the service provider also has the function of monitoring the implementation status of preventive measures and evaluating their effectiveness. For example, it conducts another risk assessment after the implementation of preventive measures and proposes additional measures as needed. In this way, the service provider can reduce the risk of workplace accidents and contribute to the realization of a safe workplace environment.

[0032] The analysis unit analyzes videos recorded of the workplace. For example, the analysis unit acquires videos of the workplace and analyzes them using image analysis algorithms. Specifically, the analysis unit divides the recorded video data into frames and applies image recognition technology to each frame. This allows for the identification of dangerous behaviors and situations. For example, it can detect cases where workers are not using safety equipment or are entering dangerous areas. The analysis unit utilizes object detection algorithms and behavior recognition algorithms using deep learning to identify risk factors in the workplace with high accuracy. Furthermore, the analysis unit can provide analysis results in real time and can be linked with systems that immediately warn of dangers. This allows the analysis unit to continuously monitor workplace safety and support rapid response.

[0033] The proposal department proposes improvements based on the results analyzed by the analysis department. For example, the proposal department presents specific improvement measures based on the analysis results. Specifically, the proposal department analyzes the data provided by the analysis department in detail and proposes optimal improvement measures to reduce the risk of occupational accidents. For example, it can propose improvement measures such as changing work procedures, improving equipment, and strengthening training. The proposal department also has the function of formulating implementation plans for improvement measures and monitoring their progress. Furthermore, the proposal department evaluates the effectiveness of the improvement measures and proposes additional measures as needed. For example, it conducts another risk assessment after the implementation of the improvement measures and takes further measures if the effect is insufficient. In this way, the proposal department can provide concrete action plans for continuously improving workplace safety and contribute to the realization of a safe working environment.

[0034] The learning unit can analyze workplace accident cases and trends for each piece of equipment used in each workplace. For example, the learning unit can obtain workplace accident cases from a database and analyze them using machine learning algorithms. For example, the learning unit can analyze data such as the type of accident, the date and time of occurrence, the cause, and the extent of the damage. The learning unit can also analyze trends for each piece of equipment used. For example, the learning unit can analyze data such as the type of equipment, the frequency of use, and the failure rate. As a result, by analyzing workplace accident cases and trends for each piece of equipment used in each workplace, the learning unit can provide more specific preventive measures. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on workplace accident cases into AI, and the AI ​​can analyze the data and extract trends.

[0035] The service provider can provide customized preventative measures for each job type. For example, based on data learned by the learning unit, the service provider can provide specific occupational accident prevention measures tailored to each job type. For example, in a manufacturing workplace, the service provider can provide preventative measures related to machine operation. In a construction workplace, the service provider can also provide preventative measures related to working at heights. Furthermore, in a service workplace, the service provider can provide preventative measures related to customer service. This enables more effective safety measures by providing customized preventative measures for each job type. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data learned by the learning unit into AI, and the AI ​​can generate preventative measures tailored to each job type.

[0036] The analysis unit can analyze videos recorded in the workplace to identify dangerous behaviors and situations. For example, the analysis unit can acquire videos recorded in the workplace and analyze them using an image analysis algorithm. For example, the analysis unit can analyze the movements of workers and the work environment to identify dangerous behaviors and situations. For example, the analysis unit can identify things like ignoring safety devices, inappropriate work procedures, and dangerous environmental conditions. As a result, the analysis unit can prevent occupational accidents by analyzing videos recorded in the workplace and identifying dangerous behaviors and situations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input recorded video data into AI, which can analyze the video to identify dangerous behaviors and situations.

[0037] The proposal department can propose specific improvement measures based on the analysis results. For example, the proposal department will propose specific improvement measures based on the results analyzed by the analysis department. The proposal department can propose improvement measures such as changing work procedures, improving equipment, and strengthening training. For example, as a change in work procedures, the proposal department can propose procedures to ensure the thorough use of safety devices. The proposal department can also propose inspection and repair of safety devices on machinery as an improvement to equipment. Furthermore, the proposal department can propose the implementation of safety training as a way to strengthen training. In this way, the proposal department can improve workplace safety by proposing specific improvement measures based on the analysis results. Some or all of the above processes in the proposal department may be performed using AI, for example, or without using AI. For example, the proposal department can input the analysis results into AI, and the AI ​​can generate specific improvement measures.

[0038] The learning unit can optimize its learning algorithm by referring to past occupational accident data during the learning process. For example, the learning unit can retrieve past occupational accident data from a database and optimize the learning algorithm. For example, the learning unit can optimize an algorithm to identify a specific accident pattern. The learning unit can also adjust the weighting of the learning algorithm based on the frequency of occupational accidents. Furthermore, the learning unit can set priorities for the learning algorithm based on the impact of occupational accidents. This improves the accuracy of learning by optimizing the learning algorithm by referring to past occupational accident data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past occupational accident data into AI, and the AI ​​can optimize the learning algorithm.

[0039] The learning unit can determine the learning priority based on the frequency and impact of occupational accidents during the learning process. For example, the learning unit may prioritize learning cases with a high frequency of occupational accidents. For example, the learning unit may prioritize learning cases with a high impact of occupational accidents. The learning unit can also determine the learning priority by considering both the frequency and impact of occupational accidents. This allows the learning unit to prioritize learning important data by determining the learning priority based on the frequency and impact of occupational accidents. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the frequency and impact of occupational accidents into the AI, which can then determine the learning priority.

[0040] The learning unit can select training data while considering the geographical and climatic conditions of each workplace. For example, the learning unit can learn data on occupational accidents that are likely to occur in a particular region. For example, the learning unit can learn data on occupational accidents that are likely to occur in a particular season. The learning unit can also select training data while considering both geographical and climatic conditions. This allows the learning unit to provide more appropriate preventive measures by selecting training data while considering the geographical and climatic conditions of each workplace. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input geographical and climatic data into the AI, and the AI ​​can select the training data.

[0041] The learning unit can customize the learning data based on the worker's age and years of experience during the learning process. For example, the learning unit can learn occupational accident data related to a specific age group. For example, the learning unit can learn occupational accident data related to less experienced workers. The learning unit can also customize the learning data by considering both the worker's age and years of experience. This allows the learning unit to learn more effectively by customizing the learning data based on the worker's age and years of experience. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the worker's age and years of experience into the AI, which can then customize the learning data.

[0042] The service provider can adjust the level of detail of preventive measures based on the risk level of occupational accidents at the time of provision. For example, if the risk level of occupational accidents is high, the service provider can provide detailed preventive measures. For example, if the risk level of occupational accidents is moderate, the service provider can provide general preventive measures. Furthermore, if the risk level of occupational accidents is low, the service provider can provide concise preventive measures. In this way, the service provider can provide appropriate preventive measures by adjusting the level of detail of preventive measures based on the risk level of occupational accidents. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the risk level of occupational accidents into AI, and the AI ​​can adjust the level of detail of the preventive measures.

[0043] The service provider can apply different preventive measures depending on the characteristics of the workplace at the time of provision. For example, in a manufacturing workplace, the service provider can provide preventive measures related to machine operation. In a construction workplace, for example, the service provider can provide preventive measures related to working at heights. In a service workplace, the service provider can also provide preventive measures related to customer service. This allows the service provider to implement more effective safety measures by applying different preventive measures according to the characteristics of the workplace. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the characteristics of the workplace into the AI, and the AI ​​can generate different preventive measures.

[0044] The service provider can provide preventive measures when providing them, taking into account the worker's working hours and shift patterns. For example, the service provider can suggest appropriate break times based on the worker's working hours. For example, the service provider can provide points to note during shift changes based on the worker's shift patterns. The service provider can also provide preventive measures by taking into account both the worker's working hours and shift patterns. This allows the service provider to provide more appropriate preventive measures by taking into account the worker's working hours and shift patterns. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the worker's working hours and shift patterns into AI, and the AI ​​can generate preventive measures.

[0045] The service provider can customize preventive measures at the time of provision, taking into account the worker's past accident history and health condition. For example, the service provider can provide preventive measures for specific risks based on the worker's past accident history. For example, the service provider can provide preventive measures related to maintaining health based on the worker's health condition. The service provider can also customize preventive measures by taking into account both the worker's past accident history and health condition. This allows the service provider to provide more effective preventive measures by customizing them to take into account the worker's past accident history and health condition. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the worker's past accident history and health condition into AI, which can then customize the preventive measures.

[0046] The analysis unit can identify dangerous behaviors by analyzing the worker's movement patterns and work environment in detail during the analysis. For example, the analysis unit can analyze the worker's movement patterns and identify dangerous behaviors. For example, the analysis unit can analyze the work environment and identify dangerous situations. The analysis unit can also analyze both the worker's movement patterns and the work environment to identify dangerous behaviors. As a result, the analysis unit can identify dangerous behaviors by analyzing the worker's movement patterns and work environment in detail, thereby preventing occupational accidents. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the worker's movement patterns and work environment into AI, which can then identify dangerous behaviors.

[0047] The analysis unit can perform analysis while considering the condition of the equipment and tools used by the workers. For example, the analysis unit can analyze the condition of the equipment used by the workers and identify malfunctions or defects. For example, the analysis unit can analyze the condition of the tools used by the workers and confirm whether proper maintenance is being performed. The analysis unit can also provide analysis results while considering the condition of the equipment and tools used by the workers. This allows the analysis unit to perform analysis while considering the condition of the equipment and tools used by the workers, enabling proper maintenance and fault identification. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the equipment and tools used by the workers into the AI, and the AI ​​can perform the analysis.

[0048] The analysis unit can perform analyses while considering the lighting and sound environment of the workplace. For example, the analysis unit can analyze the lighting conditions of the workplace to confirm whether adequate lighting is ensured. For example, the analysis unit can analyze the sound environment of the workplace to confirm whether the noise level is appropriate. Furthermore, the analysis unit can provide analysis results that consider both the lighting and sound environment of the workplace. This allows the analysis unit to maintain an appropriate working environment by performing analyses while considering the lighting and sound environment of the workplace. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the lighting and sound environment of the workplace into AI, and the AI ​​can perform the analysis.

[0049] The analysis unit can perform analysis while considering the worker's work speed and rest patterns. For example, the analysis unit can analyze the worker's work speed to confirm whether an appropriate work pace is maintained. For example, the analysis unit can analyze the worker's rest patterns to confirm whether appropriate rests are being taken. The analysis unit can also provide analysis results considering both the worker's work speed and rest patterns. This allows the analysis unit to ensure an appropriate work pace and rest by performing analysis while considering the worker's work speed and rest patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the worker's work speed and rest patterns into AI, which can then perform the analysis.

[0050] The proposal unit can adjust the level of detail of improvement measures based on the risk level of occupational accidents when making a proposal. For example, if the risk level of occupational accidents is high, the proposal unit can provide detailed improvement measures. For example, if the risk level of occupational accidents is moderate, the proposal unit can provide general improvement measures. Furthermore, if the risk level of occupational accidents is low, the proposal unit can provide concise improvement measures. In this way, the proposal unit can provide appropriate improvement measures by adjusting the level of detail of improvement measures based on the risk level of occupational accidents. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input data on the risk level of occupational accidents into AI, and the AI ​​can adjust the level of detail of the improvement measures.

[0051] The proposal department can apply different improvement measures depending on the characteristics of the workplace when making proposals. For example, in a manufacturing workplace, the proposal department can provide improvement measures related to machine operation. In a construction workplace, for example, the proposal department can provide improvement measures related to working at heights. Furthermore, in a service industry workplace, the proposal department can provide improvement measures related to customer service. This allows the proposal department to implement more effective safety measures by applying different improvement measures according to the characteristics of the workplace. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on the characteristics of the workplace into the AI, and the AI ​​can generate different improvement measures.

[0052] The suggestion unit can provide improvement measures when making suggestions, taking into account the workers' working hours and shift patterns. For example, the suggestion unit can suggest appropriate break times based on the workers' working hours. For example, the suggestion unit can provide points to note during shift changes based on the workers' shift patterns. The suggestion unit can also provide improvement measures considering both the workers' working hours and shift patterns. This allows the suggestion unit to provide more appropriate improvement measures by considering both the workers' working hours and shift patterns. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on workers' working hours and shift patterns into AI, which can then generate improvement measures.

[0053] The proposal unit can customize improvement measures when making proposals, taking into account the worker's past accident history and health condition. For example, the proposal unit can provide improvement measures for specific risks based on the worker's past accident history. For example, the proposal unit can provide improvement measures related to maintaining health based on the worker's health condition. Furthermore, the proposal unit can customize improvement measures by taking into account both the worker's past accident history and health condition. This allows the proposal unit to provide more effective improvement measures by customizing them to take into account the worker's past accident history and health condition. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input data on the worker's past accident history and health condition into AI, which can then customize the improvement measures.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The AI ​​coaching system can also be equipped with a real-time feedback unit. This unit can provide real-time safety guidance to workers during their work. For example, if a worker is performing work at height and is improperly using a safety harness, the real-time feedback unit can immediately issue a warning. Similarly, if safety devices are not being used properly during machine operation, the real-time feedback unit can provide immediate guidance. Furthermore, it can suggest appropriate countermeasures in real time in response to changes in the work environment. This allows the real-time feedback unit to help workers take immediate safety measures, further reducing the risk of workplace accidents.

[0056] The AI ​​coaching system can also include a communication department. This department can facilitate communication among workers and improve safety awareness. For example, it can provide a platform where workers can share questions and concerns about safety. It can also create a forum where workers can share past workplace accident cases and learn from their experiences. Furthermore, by encouraging workers to propose safety measures to each other, it can raise safety awareness throughout the workplace. In this way, the communication department can strengthen collaboration among workers and contribute to creating a safer work environment.

[0057] The AI ​​coaching system can also be equipped with a predictive unit. This unit can predict the risk of future workplace accidents based on historical data and current conditions. For example, it can predict the risk of workplace accidents under specific seasons or weather conditions and take preventative measures. It can also identify high-risk tasks based on the frequency of use of specific work processes or equipment. Furthermore, it can predict high-risk timings by considering workers' fatigue levels and stress levels. This allows the predictive unit to provide effective measures to prevent workplace accidents.

[0058] The AI ​​coaching system can also include a training department. This department can provide regular safety training to workers. For example, it can use virtual reality (VR) technology to recreate actual work environments and conduct practical training on safety measures. It can also teach workers how to deal with dangerous situations through simulations. Furthermore, the training department can manage workers' training history and provide additional training as needed. This allows the training department to improve workers' safety awareness and skills, and reduce the risk of workplace accidents.

[0059] The AI ​​coaching system can also include a rewards section. This section can reward workers who perform safe work practices. For example, it could offer bonuses or special leave to workers who have worked accident-free for a certain period. It could also offer recognition or promotion opportunities to workers who proactively propose safety measures. Furthermore, it could award points to workers who participate in safety awareness training programs, rewarding them based on their accumulated points. In this way, the rewards section can contribute to raising workers' safety awareness and creating a safer work environment.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The learning unit learns from past industrial accident data. For example, the learning unit obtains detailed reports and causes of past industrial accidents from a database and learns from them using machine learning algorithms. The learning unit can learn data such as the type of accident, the date and time of occurrence, the cause, and the extent of the damage. Step 2: The provisioning unit provides preventive measures based on the data learned by the learning unit. For example, the provisioning unit provides specific preventive measures for occupational accidents tailored to the specific circumstances of each workplace, based on the learned data. The provisioning unit can provide preventive measures such as safety training, equipment improvements, and revisions to work procedures. Step 3: The analysis unit analyzes the video recording of the workplace. For example, the analysis unit acquires video recordings of the workplace and analyzes them using an image analysis algorithm. The analysis unit can then identify dangerous behaviors or situations. Step 4: The proposal department proposes improvements based on the results of the analysis conducted by the analysis department. For example, the proposal department will present specific improvement measures based on the analysis results. The proposal department may propose improvement measures such as changes to work procedures, equipment improvements, or enhanced training.

[0062] (Example of form 2) The AI ​​coaching system according to an embodiment of the present invention is a system that learns detailed reports and causes of various occupational accidents to prevent dangerous situations and actions and provides guidance to realize a safe workplace environment. The AI ​​coaching system learns detailed reports and causes of past occupational accidents. In this process, it analyzes occupational accident occurrence cases in each workplace and trends for each type of equipment used. Next, based on the learned data, the AI ​​coaching system provides specific occupational accident prevention measures tailored to the situation of each workplace. Furthermore, the AI ​​coaching system analyzes videos recorded of the workplace and proposes areas for improvement and points to note from the perspective of occupational accident prevention. This system provides concrete and practical occupational accident prevention measures and improves the safety environment of the workplace. In addition, even if there is a shortage of personnel who can provide guidance, the AI ​​coaching system can provide guidance on their behalf, minimizing the risk of occupational accidents. For example, the AI ​​coaching system provides specific guidance such as: As a safety measure when working at height, it provides guidance on the proper use of safety harnesses and how to inspect scaffolding. As a point of caution when operating machinery, it provides guidance on checking safety devices on machinery and adhering to operating procedures. It analyzes videos recorded of the workplace and proposes areas for improvement regarding worker movements and the work environment. In this way, the AI ​​coaching system provides concrete and practical preventive measures against workplace accidents based on past accident data, thereby creating a safe work environment. Thus, the AI ​​coaching system can provide concrete and practical preventive measures against workplace accidents and create a safe work environment.

[0063] The AI ​​coaching system according to this embodiment comprises a learning unit, a provision unit, an analysis unit, and a proposal unit. The learning unit learns data on past industrial accidents. For example, the learning unit obtains detailed reports and causes of past industrial accidents from a database and learns using a machine learning algorithm. For example, the learning unit can learn data such as the type of accident, the date and time of occurrence, the cause, and the extent of the damage. The provision unit provides preventive measures based on the data learned by the learning unit. For example, the provision unit provides specific industrial accident preventive measures tailored to the situation of each workplace based on the learned data. For example, the provision unit can provide preventive measures such as safety education, equipment improvements, and revisions to work procedures. The analysis unit analyzes videos recorded of the workplace. For example, the analysis unit obtains videos recorded of the workplace and analyzes them using an image analysis algorithm. For example, the analysis unit can identify dangerous behaviors and situations. The proposal unit proposes improvements based on the results analyzed by the analysis unit. For example, the proposal unit presents specific improvement measures based on the analysis results. The proposal department can suggest improvement measures such as changes to work procedures, equipment improvements, and enhanced training. This allows the AI ​​coaching system, according to the embodiment, to learn from past industrial accident data, provide preventative measures, analyze workplace conditions, and propose improvements, thereby creating a safer work environment.

[0064] The learning unit learns from data on past workplace accidents. For example, the learning unit obtains detailed reports and causes of past workplace accidents from a database and learns from them using machine learning algorithms. Specifically, the learning unit collects data such as the type of accident, the date and time of occurrence, the cause, and the extent of the damage, and preprocesses this data before inputting it into the machine learning model. Preprocessing includes data cleaning, normalization, and feature extraction. For example, important keywords are extracted from text data and converted into numerical data to enable the machine learning model to learn efficiently. The learning unit combines different learning methods such as supervised learning, unsupervised learning, and reinforcement learning to identify patterns and causes of workplace accidents with high accuracy. Furthermore, the learning unit regularly incorporates new data and updates the model, enabling predictions and analyses to always be based on the latest information. As a result, the learning unit can effectively learn from past workplace accident data and provide insights that can help prevent future accidents.

[0065] The service provider provides preventive measures based on data learned by the learning provider. For example, the service provider provides specific workplace accident prevention measures tailored to the specific circumstances of each workplace, based on the learned data. Specifically, the service provider conducts risk assessments for each workplace based on the knowledge gained from the learning provider and proposes appropriate preventive measures. For example, it identifies the causes of accidents that frequently occur in a particular work environment and presents specific measures to eliminate those causes. The service provider can provide a wide range of preventive measures, such as designing safety education programs, proposing equipment improvements, and reviewing work procedures. Furthermore, the service provider also has the function of monitoring the implementation status of preventive measures and evaluating their effectiveness. For example, it conducts another risk assessment after the implementation of preventive measures and proposes additional measures as needed. In this way, the service provider can reduce the risk of workplace accidents and contribute to the realization of a safe workplace environment.

[0066] The analysis unit analyzes videos recorded of the workplace. For example, the analysis unit acquires videos of the workplace and analyzes them using image analysis algorithms. Specifically, the analysis unit divides the recorded video data into frames and applies image recognition technology to each frame. This allows for the identification of dangerous behaviors and situations. For example, it can detect cases where workers are not using safety equipment or are entering dangerous areas. The analysis unit utilizes object detection algorithms and behavior recognition algorithms using deep learning to identify risk factors in the workplace with high accuracy. Furthermore, the analysis unit can provide analysis results in real time and can be linked with systems that immediately warn of dangers. This allows the analysis unit to continuously monitor workplace safety and support rapid response.

[0067] The proposal department proposes improvements based on the results analyzed by the analysis department. For example, the proposal department presents specific improvement measures based on the analysis results. Specifically, the proposal department analyzes the data provided by the analysis department in detail and proposes optimal improvement measures to reduce the risk of occupational accidents. For example, it can propose improvement measures such as changing work procedures, improving equipment, and strengthening training. The proposal department also has the function of formulating implementation plans for improvement measures and monitoring their progress. Furthermore, the proposal department evaluates the effectiveness of the improvement measures and proposes additional measures as needed. For example, it conducts another risk assessment after the implementation of the improvement measures and takes further measures if the effect is insufficient. In this way, the proposal department can provide concrete action plans for continuously improving workplace safety and contribute to the realization of a safe working environment.

[0068] The learning unit can analyze workplace accident cases and trends for each piece of equipment used in each workplace. For example, the learning unit can obtain workplace accident cases from a database and analyze them using machine learning algorithms. For example, the learning unit can analyze data such as the type of accident, the date and time of occurrence, the cause, and the extent of the damage. The learning unit can also analyze trends for each piece of equipment used. For example, the learning unit can analyze data such as the type of equipment, the frequency of use, and the failure rate. As a result, by analyzing workplace accident cases and trends for each piece of equipment used in each workplace, the learning unit can provide more specific preventive measures. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on workplace accident cases into AI, and the AI ​​can analyze the data and extract trends.

[0069] The service provider can provide customized preventative measures for each job type. For example, based on data learned by the learning unit, the service provider can provide specific occupational accident prevention measures tailored to each job type. For example, in a manufacturing workplace, the service provider can provide preventative measures related to machine operation. In a construction workplace, the service provider can also provide preventative measures related to working at heights. Furthermore, in a service workplace, the service provider can provide preventative measures related to customer service. This enables more effective safety measures by providing customized preventative measures for each job type. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data learned by the learning unit into AI, and the AI ​​can generate preventative measures tailored to each job type.

[0070] The analysis unit can analyze videos recorded in the workplace to identify dangerous behaviors and situations. For example, the analysis unit can acquire videos recorded in the workplace and analyze them using an image analysis algorithm. For example, the analysis unit can analyze the movements of workers and the work environment to identify dangerous behaviors and situations. For example, the analysis unit can identify things like ignoring safety devices, inappropriate work procedures, and dangerous environmental conditions. As a result, the analysis unit can prevent occupational accidents by analyzing videos recorded in the workplace and identifying dangerous behaviors and situations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input recorded video data into AI, which can analyze the video to identify dangerous behaviors and situations.

[0071] The proposal department can propose specific improvement measures based on the analysis results. For example, the proposal department will propose specific improvement measures based on the results analyzed by the analysis department. The proposal department can propose improvement measures such as changing work procedures, improving equipment, and strengthening training. For example, as a change in work procedures, the proposal department can propose procedures to ensure the thorough use of safety devices. The proposal department can also propose inspection and repair of safety devices on machinery as an improvement to equipment. Furthermore, the proposal department can propose the implementation of safety training as a way to strengthen training. In this way, the proposal department can improve workplace safety by proposing specific improvement measures based on the analysis results. Some or all of the above processes in the proposal department may be performed using AI, for example, or without using AI. For example, the proposal department can input the analysis results into AI, and the AI ​​can generate specific improvement measures.

[0072] The learning unit can estimate the emotions of workers and select training data based on the estimated emotions. For example, the learning unit can capture a worker's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the learning unit can calculate an emotion score based on changes in facial expression. The learning unit can also record a worker's voice and estimate the emotion using voice analysis technology. For example, the learning unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the learning unit can collect biometric data from workers (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. For example, the learning unit can calculate an emotion score based on fluctuations in heart rate. This allows the learning unit to select training data based on the emotions of workers, enabling more effective learning. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input worker emotion data into the AI, which can then select the learning data.

[0073] The learning unit can optimize its learning algorithm by referring to past occupational accident data during the learning process. For example, the learning unit can retrieve past occupational accident data from a database and optimize the learning algorithm. For example, the learning unit can optimize an algorithm to identify a specific accident pattern. The learning unit can also adjust the weighting of the learning algorithm based on the frequency of occupational accidents. Furthermore, the learning unit can set priorities for the learning algorithm based on the impact of occupational accidents. This improves the accuracy of learning by optimizing the learning algorithm by referring to past occupational accident data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past occupational accident data into AI, and the AI ​​can optimize the learning algorithm.

[0074] The learning unit can determine the learning priority based on the frequency and impact of occupational accidents during the learning process. For example, the learning unit may prioritize learning cases with a high frequency of occupational accidents. For example, the learning unit may prioritize learning cases with a high impact of occupational accidents. The learning unit can also determine the learning priority by considering both the frequency and impact of occupational accidents. This allows the learning unit to prioritize learning important data by determining the learning priority based on the frequency and impact of occupational accidents. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the frequency and impact of occupational accidents into the AI, which can then determine the learning priority.

[0075] The learning unit can estimate the worker's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can capture the worker's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the learning unit can calculate an emotion score based on changes in facial expressions. The learning unit can also record the worker's voice and estimate their emotions using voice analysis technology. For example, the learning unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the learning unit can collect the worker's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the learning unit can calculate an emotion score based on fluctuations in heart rate. This allows the learning unit to adjust the learning frequency based on the worker's emotions, enabling effective learning while reducing the burden on the worker. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input worker emotion data into the AI, which can then adjust the frequency of learning.

[0076] The learning unit can select training data while considering the geographical and climatic conditions of each workplace. For example, the learning unit can learn data on occupational accidents that are likely to occur in a particular region. For example, the learning unit can learn data on occupational accidents that are likely to occur in a particular season. The learning unit can also select training data while considering both geographical and climatic conditions. This allows the learning unit to provide more appropriate preventive measures by selecting training data while considering the geographical and climatic conditions of each workplace. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input geographical and climatic data into the AI, and the AI ​​can select the training data.

[0077] The learning unit can customize the learning data based on the worker's age and years of experience during the learning process. For example, the learning unit can learn occupational accident data related to a specific age group. For example, the learning unit can learn occupational accident data related to less experienced workers. The learning unit can also customize the learning data by considering both the worker's age and years of experience. This allows the learning unit to learn more effectively by customizing the learning data based on the worker's age and years of experience. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the worker's age and years of experience into the AI, which can then customize the learning data.

[0078] The service provider can estimate the worker's emotions and adjust the way preventive measures are presented based on the estimated emotions. For example, the service provider can capture the worker's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on changes in facial expressions. The service provider can also record the worker's voice and estimate their emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the service provider can collect the worker's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on fluctuations in heart rate. This allows the service provider to provide more effective preventive measures by adjusting the way preventive measures are presented based on the worker's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input worker emotional data into the AI, which can then adjust how the preventive measures are expressed.

[0079] The service provider can adjust the level of detail of preventive measures based on the risk level of occupational accidents at the time of provision. For example, if the risk level of occupational accidents is high, the service provider can provide detailed preventive measures. For example, if the risk level of occupational accidents is moderate, the service provider can provide general preventive measures. Furthermore, if the risk level of occupational accidents is low, the service provider can provide concise preventive measures. In this way, the service provider can provide appropriate preventive measures by adjusting the level of detail of preventive measures based on the risk level of occupational accidents. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the risk level of occupational accidents into AI, and the AI ​​can adjust the level of detail of the preventive measures.

[0080] The service provider can apply different preventive measures depending on the characteristics of the workplace at the time of provision. For example, in a manufacturing workplace, the service provider can provide preventive measures related to machine operation. In a construction workplace, for example, the service provider can provide preventive measures related to working at heights. In a service workplace, the service provider can also provide preventive measures related to customer service. This allows the service provider to implement more effective safety measures by applying different preventive measures according to the characteristics of the workplace. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the characteristics of the workplace into the AI, and the AI ​​can generate different preventive measures.

[0081] The service provider can estimate a worker's emotions and prioritize preventive measures based on the estimated emotions. For example, the service provider can capture a worker's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on changes in facial expression. The service provider can also record a worker's voice and estimate their emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the service provider can collect a worker's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on fluctuations in heart rate. This allows the service provider to provide more effective preventive measures by prioritizing preventive measures based on the worker's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input worker emotional data into AI, which can then determine the priority of preventive measures.

[0082] The service provider can provide preventive measures when providing them, taking into account the worker's working hours and shift patterns. For example, the service provider can suggest appropriate break times based on the worker's working hours. For example, the service provider can provide points to note during shift changes based on the worker's shift patterns. The service provider can also provide preventive measures by taking into account both the worker's working hours and shift patterns. This allows the service provider to provide more appropriate preventive measures by taking into account the worker's working hours and shift patterns. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the worker's working hours and shift patterns into AI, and the AI ​​can generate preventive measures.

[0083] The service provider can customize preventive measures at the time of provision, taking into account the worker's past accident history and health condition. For example, the service provider can provide preventive measures for specific risks based on the worker's past accident history. For example, the service provider can provide preventive measures related to maintaining health based on the worker's health condition. The service provider can also customize preventive measures by taking into account both the worker's past accident history and health condition. This allows the service provider to provide more effective preventive measures by customizing them to take into account the worker's past accident history and health condition. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the worker's past accident history and health condition into AI, which can then customize the preventive measures.

[0084] The analysis unit can estimate the worker's emotions and adjust the analysis criteria based on the estimated emotions. For example, the analysis unit can capture the worker's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions. The analysis unit can also record the worker's voice and estimate their emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the analysis unit can collect the worker's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. This allows the analysis unit to perform more appropriate analysis by adjusting the analysis criteria based on the worker's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input worker emotion data into the AI, which can then adjust the analysis criteria.

[0085] The analysis unit can identify dangerous behaviors by analyzing the worker's movement patterns and work environment in detail during the analysis. For example, the analysis unit can analyze the worker's movement patterns and identify dangerous behaviors. For example, the analysis unit can analyze the work environment and identify dangerous situations. The analysis unit can also analyze both the worker's movement patterns and the work environment to identify dangerous behaviors. As a result, the analysis unit can identify dangerous behaviors by analyzing the worker's movement patterns and work environment in detail, thereby preventing occupational accidents. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the worker's movement patterns and work environment into AI, which can then identify dangerous behaviors.

[0086] The analysis unit can perform analysis while considering the condition of the equipment and tools used by the workers. For example, the analysis unit can analyze the condition of the equipment used by the workers and identify malfunctions or defects. For example, the analysis unit can analyze the condition of the tools used by the workers and confirm whether proper maintenance is being performed. The analysis unit can also provide analysis results while considering the condition of the equipment and tools used by the workers. This allows the analysis unit to perform analysis while considering the condition of the equipment and tools used by the workers, enabling proper maintenance and fault identification. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the equipment and tools used by the workers into the AI, and the AI ​​can perform the analysis.

[0087] The analysis unit can estimate the worker's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can capture the worker's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions. The analysis unit can also record the worker's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the analysis unit can collect the worker's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. This allows the analysis unit to adjust the display method of the analysis results based on the worker's emotions, enabling a more easily understandable display. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input worker emotion data into the AI, which can then adjust how the analysis results are displayed.

[0088] The analysis unit can perform analyses while considering the lighting and sound environment of the workplace. For example, the analysis unit can analyze the lighting conditions of the workplace to confirm whether adequate lighting is ensured. For example, the analysis unit can analyze the sound environment of the workplace to confirm whether the noise level is appropriate. Furthermore, the analysis unit can provide analysis results that consider both the lighting and sound environment of the workplace. This allows the analysis unit to maintain an appropriate working environment by performing analyses while considering the lighting and sound environment of the workplace. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the lighting and sound environment of the workplace into AI, and the AI ​​can perform the analysis.

[0089] The analysis unit can perform analysis while considering the worker's work speed and rest patterns. For example, the analysis unit can analyze the worker's work speed to confirm whether an appropriate work pace is maintained. For example, the analysis unit can analyze the worker's rest patterns to confirm whether appropriate rests are being taken. The analysis unit can also provide analysis results considering both the worker's work speed and rest patterns. This allows the analysis unit to ensure an appropriate work pace and rest by performing analysis while considering the worker's work speed and rest patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the worker's work speed and rest patterns into AI, which can then perform the analysis.

[0090] The proposal unit can estimate the worker's emotions and adjust the way improvement measures are presented based on the estimated emotions. For example, the proposal unit can capture the worker's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the proposal unit can calculate an emotion score based on changes in facial expressions. The proposal unit can also record the worker's voice and estimate their emotions using voice analysis technology. For example, the proposal unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the proposal unit can collect the worker's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the proposal unit can calculate an emotion score based on fluctuations in heart rate. As a result, the proposal unit can provide more effective improvement measures by adjusting the way improvement measures are presented based on the worker's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input worker emotional data into the AI, which can then adjust how the improvement measures are presented.

[0091] The proposal unit can adjust the level of detail of improvement measures based on the risk level of occupational accidents when making a proposal. For example, if the risk level of occupational accidents is high, the proposal unit can provide detailed improvement measures. For example, if the risk level of occupational accidents is moderate, the proposal unit can provide general improvement measures. Furthermore, if the risk level of occupational accidents is low, the proposal unit can provide concise improvement measures. In this way, the proposal unit can provide appropriate improvement measures by adjusting the level of detail of improvement measures based on the risk level of occupational accidents. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input data on the risk level of occupational accidents into AI, and the AI ​​can adjust the level of detail of the improvement measures.

[0092] The proposal department can apply different improvement measures depending on the characteristics of the workplace when making proposals. For example, in a manufacturing workplace, the proposal department can provide improvement measures related to machine operation. In a construction workplace, for example, the proposal department can provide improvement measures related to working at heights. Furthermore, in a service industry workplace, the proposal department can provide improvement measures related to customer service. This allows the proposal department to implement more effective safety measures by applying different improvement measures according to the characteristics of the workplace. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on the characteristics of the workplace into the AI, and the AI ​​can generate different improvement measures.

[0093] The proposal unit can estimate the emotions of workers and prioritize improvement measures based on the estimated emotions. For example, the proposal unit can capture a worker's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the proposal unit can calculate an emotion score based on changes in facial expression. The proposal unit can also record a worker's voice and estimate their emotions using voice analysis technology. For example, the proposal unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the proposal unit can collect biometric data from workers (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the proposal unit can calculate an emotion score based on fluctuations in heart rate. As a result, the proposal unit can provide more effective improvement measures by prioritizing improvement measures based on the emotions of workers. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input workers' emotional data into AI, which can then determine the priority of improvement measures.

[0094] The suggestion unit can provide improvement measures when making suggestions, taking into account the workers' working hours and shift patterns. For example, the suggestion unit can suggest appropriate break times based on the workers' working hours. For example, the suggestion unit can provide points to note during shift changes based on the workers' shift patterns. The suggestion unit can also provide improvement measures considering both the workers' working hours and shift patterns. This allows the suggestion unit to provide more appropriate improvement measures by considering both the workers' working hours and shift patterns. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on workers' working hours and shift patterns into AI, which can then generate improvement measures.

[0095] The proposal unit can customize improvement measures when making proposals, taking into account the worker's past accident history and health condition. For example, the proposal unit can provide improvement measures for specific risks based on the worker's past accident history. For example, the proposal unit can provide improvement measures related to maintaining health based on the worker's health condition. Furthermore, the proposal unit can customize improvement measures by taking into account both the worker's past accident history and health condition. This allows the proposal unit to provide more effective improvement measures by customizing them to take into account the worker's past accident history and health condition. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input data on the worker's past accident history and health condition into AI, which can then customize the improvement measures.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The AI ​​coaching system can also be equipped with a real-time feedback unit. This unit can provide real-time safety guidance to workers during their work. For example, if a worker is performing work at height and is improperly using a safety harness, the real-time feedback unit can immediately issue a warning. Similarly, if safety devices are not being used properly during machine operation, the real-time feedback unit can provide immediate guidance. Furthermore, it can suggest appropriate countermeasures in real time in response to changes in the work environment. This allows the real-time feedback unit to help workers take immediate safety measures, further reducing the risk of workplace accidents.

[0098] The AI ​​coaching system can also include a communication department. This department can facilitate communication among workers and improve safety awareness. For example, it can provide a platform where workers can share questions and concerns about safety. It can also create a forum where workers can share past workplace accident cases and learn from their experiences. Furthermore, by encouraging workers to propose safety measures to each other, it can raise safety awareness throughout the workplace. In this way, the communication department can strengthen collaboration among workers and contribute to creating a safer work environment.

[0099] The AI ​​coaching system can also be equipped with a predictive unit. This unit can predict the risk of future workplace accidents based on historical data and current conditions. For example, it can predict the risk of workplace accidents under specific seasons or weather conditions and take preventative measures. It can also identify high-risk tasks based on the frequency of use of specific work processes or equipment. Furthermore, it can predict high-risk timings by considering workers' fatigue levels and stress levels. This allows the predictive unit to provide effective measures to prevent workplace accidents.

[0100] The AI ​​coaching system can also include a training department. This department can provide regular safety training to workers. For example, it can use virtual reality (VR) technology to recreate actual work environments and conduct practical training on safety measures. It can also teach workers how to deal with dangerous situations through simulations. Furthermore, the training department can manage workers' training history and provide additional training as needed. This allows the training department to improve workers' safety awareness and skills, and reduce the risk of workplace accidents.

[0101] The AI ​​coaching system can also include a rewards section. This section can reward workers who perform safe work practices. For example, it could offer bonuses or special leave to workers who have worked accident-free for a certain period. It could also offer recognition or promotion opportunities to workers who proactively propose safety measures. Furthermore, it could award points to workers who participate in safety awareness training programs, rewarding them based on their accumulated points. In this way, the rewards section can contribute to raising workers' safety awareness and creating a safer work environment.

[0102] The AI ​​coaching system can also be equipped with an emotion monitoring unit. This unit can monitor the worker's emotional state in real time and detect signs of stress and fatigue. For example, it can analyze the worker's facial expressions and voice to calculate an emotional score. It can also collect the worker's biometric data (heart rate and skin electrical activity) to estimate their emotional state. Furthermore, the emotion monitoring unit can suggest appropriate breaks and refreshments based on the worker's emotional state. In this way, the emotion monitoring unit can support the worker's mental health and reduce the risk of workplace accidents.

[0103] The AI ​​coaching system can also be equipped with an emotional feedback unit. This unit can adjust the content and method of feedback based on the worker's emotional state. For example, if the worker is stressed, it can provide feedback in a gentle tone. Conversely, if the worker is relaxed, it can provide detailed explanations of specific areas for improvement. Furthermore, the emotional feedback unit can adjust the frequency and timing of feedback according to the worker's emotional state. This allows the emotional feedback unit to provide feedback in a way that is easily accepted by the worker, enabling effective guidance.

[0104] The AI ​​coaching system can also include an emotional support unit. This unit can provide mental health support based on the worker's emotional state. For example, if a worker is stressed, it can suggest relaxation techniques and stress management methods. If a worker is fatigued, it can suggest appropriate breaks and ways to refresh themselves. Furthermore, the emotional support unit can provide emotional diaries and self-monitoring tools to enable workers to self-manage their emotional state. This allows the emotional support unit to support the worker's mental health and reduce the risk of workplace accidents.

[0105] The AI ​​coaching system can also be equipped with an emotional alert unit. This unit can proactively warn of dangerous situations based on the worker's emotional state. For example, if a worker is experiencing extreme stress, it can warn them to temporarily stop working. It can also suggest a break if a worker is feeling fatigued. Furthermore, the emotional alert unit can notify managers of appropriate actions based on the worker's emotional state. This allows the emotional alert unit to monitor workers' emotional states in real time and reduce the risk of workplace accidents.

[0106] The AI ​​coaching system can also be equipped with an emotional reporting unit. This unit can regularly report on the emotional state of workers and provide this information to managers. For example, it can report on workers' emotional scores weekly or monthly to understand trends in stress and fatigue. The emotional reporting unit can also suggest appropriate measures based on the worker's emotional state. Furthermore, the emotional reporting unit can monitor workers' emotional state over the long term and evaluate improvements in their mental health. This allows the emotional reporting unit to continuously support workers' mental health and reduce the risk of workplace accidents.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The learning unit learns from past industrial accident data. For example, the learning unit obtains detailed reports and causes of past industrial accidents from a database and learns from them using machine learning algorithms. The learning unit can learn data such as the type of accident, the date and time of occurrence, the cause, and the extent of the damage. Step 2: The provisioning unit provides preventive measures based on the data learned by the learning unit. For example, the provisioning unit provides specific preventive measures for occupational accidents tailored to the specific circumstances of each workplace, based on the learned data. The provisioning unit can provide preventive measures such as safety training, equipment improvements, and revisions to work procedures. Step 3: The analysis unit analyzes the video recording of the workplace. For example, the analysis unit acquires video recordings of the workplace and analyzes them using an image analysis algorithm. The analysis unit can then identify dangerous behaviors or situations. Step 4: The proposal department proposes improvements based on the results of the analysis conducted by the analysis department. For example, the proposal department will present specific improvement measures based on the analysis results. The proposal department may propose improvement measures such as changes to work procedures, equipment improvements, or enhanced training.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the learning unit, provision unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which acquires data on past work accidents from the database 24 and learns using a machine learning algorithm. The provision unit is implemented by the control unit 46A of the smart device 14, which provides specific work accident prevention measures tailored to the situation of each workplace based on the learned data. The analysis unit records the workplace using the camera 42 of the smart device 14 and analyzes it using an image analysis algorithm by the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which presents specific improvement measures based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the learning unit, provision unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which acquires data on past work accidents from the database 24 and learns using a machine learning algorithm. The provision unit is implemented by the control unit 46A of the smart glasses 214, which provides specific work accident prevention measures tailored to the situation of each workplace based on the learned data. The analysis unit records the workplace using the camera 42 of the smart glasses 214 and analyzes it using an image analysis algorithm by the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which presents specific improvement measures based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the learning unit, provision unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which acquires data on past industrial accidents from the database 24 and learns using a machine learning algorithm. The provision unit is implemented by the control unit 46A of the headset terminal 314, which provides specific industrial accident prevention measures tailored to the situation of each workplace based on the learned data. The analysis unit records the workplace using the camera 42 of the headset terminal 314 and analyzes it using an image analysis algorithm by the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which presents specific improvement measures based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the learning unit, provision unit, analysis unit, and proposal unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which acquires data on past industrial accidents from the database 24 and learns using a machine learning algorithm. The provision unit is implemented by the control unit 46A of the robot 414, which provides specific industrial accident prevention measures tailored to the conditions of each workplace based on the learned data. The analysis unit records the workplace using the camera 42 of the robot 414 and analyzes it using an image analysis algorithm by the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which presents specific improvement measures based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A learning unit that learns from past industrial accident data, A provisioning unit that provides preventive measures based on data learned by the aforementioned learning unit, The analysis department analyzes videos recorded of the workplace, The system includes a proposal unit that suggests improvements based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned learning unit, Analyze workplace accident cases and trends based on the equipment used in each workplace. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We provide customized preventative measures for each job type. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyzing video recordings of the workplace will help identify dangerous behaviors and situations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the analysis results, we will present specific improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, The system estimates the emotions of workers and selects training data based on the estimated emotions of the workers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past industrial accident data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, When learning, prioritize your studies based on the frequency and impact of workplace accidents. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, It estimates the workers' emotions and adjusts the frequency of learning based on the estimated workers' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, During training, training data is selected considering the geographical and climatic conditions of each workplace. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, During training, the training data is customized based on the worker's age and years of experience. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, Estimate workers' sentiments and adjust the way preventive measures are expressed based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, When providing the information, adjust the level of detail in preventive measures based on the risk level of occupational accidents. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, When providing the service, apply different precautions depending on the characteristics of the workplace. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, Estimate workers' sentiments and prioritize preventative measures based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing services, preventative measures should be offered while considering the workers' working hours and shift patterns. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing the service, the preventative measures are customized to take into account the worker's past accident history and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, We estimate the workers' emotions and adjust the analysis criteria based on the estimated workers' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the worker's movement patterns and work environment are analyzed in detail to identify dangerous behaviors. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During the analysis, the condition of the equipment and tools used by the workers will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, The system estimates the workers' emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During the analysis, the lighting and sound environment of the workplace will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During the analysis, the worker's work speed and rest patterns are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, We estimate the workers' feelings and adjust the way improvement measures are presented based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the improvement measures based on the risk level of workplace accidents. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, apply different improvement measures depending on the characteristics of the workplace. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, Estimate workers' feelings and prioritize improvement measures based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, we will provide improvement measures that take into account the workers' working hours and shift patterns. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, customize the improvement plan by taking into account the worker's past accident history and health condition. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A learning unit that learns from past industrial accident data, A provisioning unit that provides preventive measures based on data learned by the aforementioned learning unit, The analysis department analyzes videos recorded of the workplace, The system includes a proposal unit that suggests improvements based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned learning unit, Analyze workplace accident cases and trends based on the equipment used in each workplace. The system according to feature 1.

3. The aforementioned supply unit is, We provide customized preventative measures for each job type. The system according to feature 1.

4. The aforementioned analysis unit, Analyzing video recordings of the workplace will help identify dangerous behaviors and situations. The system according to feature 1.

5. The aforementioned proposal section is, Based on the analysis results, we will present specific improvement measures. The system according to feature 1.

6. The aforementioned learning unit, The system estimates the emotions of workers and selects training data based on the estimated emotions of the workers. The system according to feature 1.

7. The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past industrial accident data. The system according to feature 1.

8. The aforementioned learning unit, When learning, prioritize your studies based on the frequency and impact of workplace accidents. The system according to feature 1.

9. The aforementioned learning unit, It estimates the workers' emotions and adjusts the frequency of learning based on the estimated workers' emotions. The system according to feature 1.

10. The aforementioned learning unit, During training, training data is selected considering the geographical and climatic conditions of each workplace. The system according to feature 1.

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