system

Abstract

The system according to the embodiment aims to prevent industrial accidents from occurring at construction sites. [Solution] A system according to an embodiment includes an identification unit, a proposal unit, a monitoring unit, and an evaluation unit. The identification unit identifies dangerous work at the site. The proposal unit proposes preventive measures for the dangerous work identified by the identification unit. The monitoring unit monitors the implementation status of the preventive measures proposed by the proposal unit. The evaluation unit evaluates the effectiveness based on the implementation status obtained by the monitoring unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, measures to prevent industrial accidents at construction sites rely on tacit knowledge, which can lead to a decline in safety awareness due to staleness.

[0005] The system according to the embodiment aims to prevent industrial accidents from occurring at construction sites. [Means for solving the problem]

[0006] The system according to the embodiment includes an identification unit, a proposal unit, a monitoring unit, and an evaluation unit. The identification unit identifies dangerous work at the site. The proposal unit proposes preventive measures for the dangerous work identified by the identification unit. The monitoring unit monitors the implementation status of the preventive measures proposed by the proposal unit. The evaluation unit evaluates the effectiveness based on the implementation status obtained by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can prevent industrial accidents from occurring at construction sites. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is designed to prevent industrial accidents at construction sites. This system uses MECE to identify dangerous worksite tasks, comprehensively identifies dangerous worksite tasks by referencing past examples from similar sites, proposes effective preventive measures for the identified dangerous worksites, monitors the implementation status of the proposed preventive measures, and evaluates their effectiveness. This allows the system to thoroughly identify dangerous worksite tasks, propose specific preventive measures, constantly monitor the implementation status of the preventive measures, and evaluate their effectiveness. For example, the system lists dangerous worksite tasks, such as working at heights, operating heavy machinery, and electrical work, and comprehensively identifies them by referencing past examples. Next, the system proposes specific preventive measures, such as the use of safety harnesses and the installation of scaffolding. Furthermore, the system monitors the implementation status of preventive measures, issuing warnings if they have not been implemented and sending reminders if they have not been implemented. This system can improve safety awareness at construction sites and contribute to preventing industrial accidents.

[0029] The industrial accident prevention system according to the embodiment includes an identification unit, a proposal unit, a monitoring unit, and an evaluation unit. The identification unit identifies dangerous work at a work site. For example, the identification unit can comprehensively identify dangerous work by referring to past examples from similar work sites. The identification unit can also identify dangerous work through the use of checklists, on-site observations, and interviews with workers. The proposal unit proposes preventive measures for the dangerous work identified by the identification unit. For example, the proposal unit can propose preventive measures based on a database of past examples and preventive measures. The proposal unit can also propose specific measures such as installing safety devices, reviewing work procedures, and conducting education and training. The monitoring unit monitors the implementation status of the preventive measures proposed by the proposal unit. For example, the monitoring unit can periodically check the use status of safety harnesses and issue a warning if they are not being used. The monitoring unit can also record the implementation status of regular inspections and send reminders if they have not been carried out. The evaluation unit evaluates the effectiveness based on the implementation status obtained by the monitoring unit. For example, the evaluation unit can constantly monitor the implementation status of the preventive measures and evaluate their effectiveness. The evaluation unit can also evaluate the effectiveness based on changes in accident occurrence rates, worker feedback, cost reduction effects, etc. As a result, the industrial accident prevention system according to the embodiment can prevent industrial accidents by identifying dangerous work on-site, proposing preventive measures, monitoring the implementation status, and evaluating the effectiveness.

[0030] The identification unit can comprehensively identify dangerous work by referring to past examples from similar work sites. Examples of past examples from similar work sites include, but are not limited to, accident reports, work records, and near-miss incidents. The identification unit can, for example, refer to past accident reports to identify similar dangerous work. The identification unit can also analyze work records to identify patterns of dangerous work. The identification unit can also refer to near-miss incidents to identify potentially dangerous work. In this way, by referring to past examples, dangerous work specific to each work site can be identified without being overlooked. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input past accident reports and work records into the generation AI and have the generation AI identify dangerous work.

[0031] The suggestion unit can propose preventive measures based on a database of past examples and preventive measures. The database of preventive measures includes, for example, the type of measure, an evaluation of its effectiveness, and application conditions, but is not limited to these examples. For example, the suggestion unit can refer to past examples and propose specific measures, such as using safety harnesses or installing scaffolding when working at heights. The suggestion unit can also propose measures, such as checking operating procedures for heavy machinery and conducting regular inspections. The suggestion unit can also propose measures, such as using insulated gloves and checking power sources when working with electricity. In this way, effective preventive measures can be proposed based on past examples and the database. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the database of past examples and preventive measures into the generation AI and have the generation AI propose optimal preventive measures.

[0032] The monitoring unit can periodically check the usage status of the safety belt and issue a warning if the safety belt is not being used. Methods for checking the usage status of the safety belt include, but are not limited to, installing a sensor, having workers report, and regular inspections. For example, the monitoring unit can install a sensor on the safety belt and monitor its usage status in real time. The monitoring unit can also have workers report their usage status. The monitoring unit can also periodically inspect the work site and check the usage status of the safety belt. This checks the usage status of the safety belt and issues a warning if the safety belt is not being used, thereby promoting the implementation of safety measures. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input data from a sensor installed on the safety belt into the generation AI and have the generation AI issue a warning if the safety belt is not being used.

[0033] The monitoring unit can record the status of periodic inspections and send reminders if they have not been performed. The content of periodic inspections includes, but is not limited to, inspection items, inspection intervals, and inspection methods. For example, the monitoring unit can list inspection items and record the status of inspections. The monitoring unit can also set inspection intervals and perform inspections periodically. The monitoring unit can also create a manual for inspection methods and instruct workers to perform inspections. This allows the implementation of periodic inspections to be recorded and reminders sent if they have not been performed, thereby promoting the implementation of inspections. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input the status of inspections into the generation AI and have the generation AI send reminders if the inspections have not been performed.

[0034] The evaluation unit can constantly grasp the implementation status of the preventive measures and evaluate their effectiveness. Methods for grasping the implementation status of the preventive measures include, but are not limited to, real-time monitoring, periodic reporting, and database updates. For example, the evaluation unit can monitor the implementation status of the preventive measures in real time and record it in a database. The evaluation unit can also periodically request reports from workers to grasp the implementation status of the preventive measures. The evaluation unit can also record the implementation status of the preventive measures in a database and constantly grasp the latest information. This enables continuous safety measures by constantly grasping the implementation status of the preventive measures and evaluating their effectiveness. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or without AI. For example, the evaluation unit can input the implementation status of the preventive measures into a generation AI and have the generation AI evaluate their effectiveness.

[0035] The identification unit can analyze past accident data and predict dangerous work under specific conditions. Past accident data includes, but is not limited to, accident reports, statistical data, and near-miss cases. For example, the identification unit can analyze past accident reports and predict dangerous work under specific weather conditions. The identification unit can also predict dangerous work that is likely to occur during specific time periods based on statistical data. The identification unit can also predict dangerous work in specific work environments by referring to near-miss cases. In this way, by analyzing past accident data, dangerous work under specific conditions can be predicted and countermeasures can be taken in advance. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input past accident data into the generation AI and cause the generation AI to predict dangerous work under specific conditions.

[0036] The identification unit can identify dangerous tasks by taking into account the site's environmental data. The site's environmental data includes, but is not limited to, weather data, time zone data, temperature and humidity data, for example. For example, the identification unit can identify dangerous tasks during rainy weather based on the site's weather data. The identification unit can also identify dangerous tasks during nighttime work based on the site's time zone data. The identification unit can also identify dangerous tasks under extreme temperature conditions based on the site's temperature data. This allows for more accurate identification of dangerous tasks by taking into account the site's environmental data. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input the site's environmental data into the generation AI and cause the generation AI to identify dangerous tasks.

[0037] The identification unit can evaluate the skill levels of on-site workers and identify dangerous tasks corresponding to their skills. Methods for evaluating a worker's skill level include, but are not limited to, the presence or absence of qualifications, years of experience, and results of skill tests. For example, the identification unit can evaluate a worker's skill level and identify dangerous tasks for beginners. The identification unit can also evaluate a worker's skill level and identify dangerous tasks for experienced workers. The identification unit can also identify dangerous tasks for workers with specific qualifications. This allows for more appropriate countermeasures to be taken by identifying dangerous tasks corresponding to the worker's skill level. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input worker skill data into a generation AI and have the generation AI identify dangerous tasks corresponding to the worker's skills.

[0038] The identification unit can identify dangerous tasks in a specific area by taking into account geographical information of the site. Geographical information of the site includes, but is not limited to, map data, GPS data, and risk information specific to the area. For example, the identification unit can identify dangerous tasks in mountainous areas based on the geographical information of the site. The identification unit can also identify dangerous tasks in urban areas based on the geographical information of the site. The identification unit can also identify dangerous tasks in coastal areas based on the geographical information of the site. In this way, by taking geographical information into consideration, dangerous tasks specific to the area can be identified. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input geographical information of the site to the generation AI and cause the generation AI to identify dangerous tasks.

[0039] The identification unit can analyze the work schedule at the site and identify dangerous work that is likely to occur during certain time periods. The work schedule includes, but is not limited to, the start and end times of work, the order of work, and the frequency of work. For example, the identification unit can identify dangerous work that occurs early in the morning based on the work schedule. The identification unit can also identify dangerous work that occurs after lunch breaks based on the work schedule. The identification unit can also identify dangerous work that occurs at night based on the work schedule. In this way, by analyzing the work schedule, dangerous work that is likely to occur during certain time periods can be identified and countermeasures can be taken in advance. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or without AI. For example, the identification unit can input work schedule data into a generation AI and have the generation AI identify dangerous work that is likely to occur during certain time periods.

[0040] The identification unit can identify dangerous operations for each piece of equipment used at the work site based on the type of equipment. The type of equipment used includes, but is not limited to, the name of the equipment, the purpose of the equipment, and the risk level of the equipment. For example, the identification unit can identify dangerous operations during crane operation based on the type of equipment used. The identification unit can also identify dangerous operations during power tool use based on the type of equipment used. The identification unit can also identify dangerous operations during heavy machinery operation based on the type of equipment used. By identifying dangerous operations based on the type of equipment used, appropriate measures can be taken for each piece of equipment. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data on the equipment used into the generation AI and have the generation AI identify dangerous operations for each piece of equipment.

[0041] When making a proposal, the suggestion unit can select the optimal measure by referring to past effectiveness data of preventive measures. Examples of the effectiveness data of past preventive measures include, but are not limited to, changes in accident rates, worker feedback, and cost reduction effects. For example, the suggestion unit can select the optimal measure for work at heights based on past effectiveness data. The suggestion unit can also select the optimal measure for heavy machinery operation based on past effectiveness data. The suggestion unit can also select the optimal measure for electrical work based on past effectiveness data. This allows optimal preventive measures to be selected by referring to past effectiveness data. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input past effectiveness data into a generation AI and cause the generation AI to select the optimal preventive measure.

[0042] When proposing a countermeasure, the suggestion unit can customize the countermeasure by taking into account the work environment of the site. The work environment includes, but is not limited to, weather, time of day, temperature, humidity, etc. For example, the suggestion unit can suggest preventive measures for rainy weather based on weather data of the site. The suggestion unit can also suggest preventive measures for night work based on time of day data of the site. The suggestion unit can also suggest preventive measures under extreme temperature conditions based on temperature data of the site. This makes it possible to propose more appropriate preventive measures by taking the work environment into consideration. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input work environment data of the site into the generation AI and cause the generation AI to customize the countermeasure.

[0043] When making a proposal, the suggestion unit can propose countermeasures according to the skill level of the worker. Examples of the worker's skill level include, but are not limited to, the presence or absence of qualifications, years of experience, and results of skill tests. For example, the suggestion unit can propose preventive measures for beginners based on the worker's skill level. The suggestion unit can also propose preventive measures for experienced workers based on the worker's skill level. The suggestion unit can also propose preventive measures for workers with specific qualifications based on the worker's skill level. By proposing countermeasures according to the worker's skill level, more effective preventive measures can be taken. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the worker's skill data into the generation AI and cause the generation AI to propose countermeasures according to the worker's skills.

[0044] When making a proposal, the proposal unit can propose region-specific measures by taking into account geographical information of the site. Geographical information includes, but is not limited to, map data, GPS data, and region-specific risk information. For example, the proposal unit can propose prevention measures for mountainous areas based on the geographical information of the site. The proposal unit can also propose prevention measures for urban areas based on the geographical information of the site. The proposal unit can also propose prevention measures for coastal areas based on the geographical information of the site. In this way, region-specific prevention measures can be proposed by taking geographical information into consideration. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the geographical information of the site to the generation AI and cause the generation AI to propose region-specific measures.

[0045] When making a proposal, the suggestion unit can analyze the work schedule and propose countermeasures suitable for a specific time period. The work schedule may include, but is not limited to, the start and end times of work, the order of work, and the frequency of work. For example, the suggestion unit can propose early morning prevention countermeasures based on the work schedule. The suggestion unit can also propose prevention countermeasures after lunch breaks based on the work schedule. The suggestion unit can also propose nighttime prevention countermeasures based on the work schedule. In this way, by analyzing the work schedule, it is possible to propose prevention countermeasures suitable for a specific time period. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input work schedule data into a generation AI and cause the generation AI to propose countermeasures suitable for a specific time period.

[0046] When making a proposal, the suggestion unit can propose countermeasures for each piece of equipment based on the type of equipment used. The type of equipment used includes, for example, the name of the equipment, the purpose of the equipment, and the risk level of the equipment, but is not limited to these examples. The suggestion unit can, for example, propose preventive measures for crane operation based on the type of equipment used. The suggestion unit can also propose preventive measures for power tool use based on the type of equipment used. The suggestion unit can also propose preventive measures for heavy machinery operation based on the type of equipment used. In this way, by proposing countermeasures based on the type of equipment used, appropriate preventive measures can be taken for each piece of equipment. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the equipment used to the generation AI and cause the generation AI to propose countermeasures for each piece of equipment.

[0047] During monitoring, the monitoring unit can improve the accuracy of monitoring by referring to past implementation status data. Past implementation status data includes, but is not limited to, inspection records, work reports, sensor data, etc. For example, the monitoring unit can improve the monitoring accuracy of work at height based on the past implementation status data. The monitoring unit can also improve the monitoring accuracy of heavy equipment operation based on the past implementation status data. The monitoring unit can also improve the monitoring accuracy of electrical work based on the past implementation status data. In this way, the accuracy of monitoring can be improved by referring to the past implementation status data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past implementation status data into the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0048] The monitoring unit can perform monitoring while taking into account on-site environmental data. On-site environmental data includes, but is not limited to, weather data, time zone data, temperature and humidity data, for example. For example, the monitoring unit can perform monitoring during rainy weather based on on-site weather data. The monitoring unit can also perform monitoring during night work based on on-site time zone data. The monitoring unit can also perform monitoring under extreme temperature conditions based on on-site temperature data. This enables more accurate monitoring by taking the on-site environmental data into consideration. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input on-site environmental data into a generation AI and have the generation AI perform monitoring.

[0049] During monitoring, the monitoring unit can evaluate the skill level of the worker and perform monitoring according to the skill. The skill level of the worker includes, but is not limited to, for example, whether or not the worker has a qualification, years of experience, and results of a skill test. For example, the monitoring unit can evaluate the skill level of the worker and perform monitoring for beginners. The monitoring unit can also evaluate the skill level of the worker and perform monitoring for experienced workers. The monitoring unit can also evaluate the skill level of the worker and perform monitoring for workers with specific qualifications. This enables more effective monitoring by performing monitoring according to the worker's skill level. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the worker's skill data into the generation AI and have the generation AI perform monitoring according to the skill.

[0050] During monitoring, the monitoring unit can perform region-specific monitoring by taking into account geographical information of the site. Geographical information includes, but is not limited to, map data, GPS data, and region-specific risk information. For example, the monitoring unit can perform monitoring in mountainous areas based on the geographical information of the site. The monitoring unit can also perform monitoring in urban areas based on the geographical information of the site. The monitoring unit can also perform monitoring in coastal areas based on the geographical information of the site. This enables region-specific monitoring by taking geographical information into account. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input geographical information of the site to the generation AI and have the generation AI perform region-specific monitoring.

[0051] During monitoring, the monitoring unit can analyze the work schedule and perform monitoring appropriate for a specific time period. The work schedule includes, for example, but is not limited to, the start and end times of work, the order of work, and the frequency of work. For example, the monitoring unit can perform early morning monitoring based on the work schedule. The monitoring unit can also perform monitoring after a lunch break based on the work schedule. The monitoring unit can also perform nighttime monitoring based on the work schedule. In this way, by analyzing the work schedule, monitoring appropriate for a specific time period becomes possible. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input work schedule data into a generation AI and have the generation AI perform monitoring appropriate for a specific time period.

[0052] During monitoring, the monitoring unit can monitor each piece of equipment based on the type of equipment used. The type of equipment used includes, for example, the name of the equipment, the purpose of the equipment, and the risk level of the equipment, but is not limited to these examples. For example, the monitoring unit can monitor the operation of a crane based on the type of equipment used. The monitoring unit can also monitor the use of power tools based on the type of equipment used. The monitoring unit can also monitor the operation of heavy machinery based on the type of equipment used. In this way, monitoring based on the type of equipment used enables appropriate monitoring of each piece of equipment. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the equipment used to the generation AI and have the generation AI perform monitoring for each piece of equipment.

[0053] The evaluation unit can improve the accuracy of the evaluation by referring to past evaluation data during the evaluation. Past evaluation data includes, but is not limited to, changes in accident rates, worker feedback, and cost reduction effects. For example, the evaluation unit can improve the evaluation accuracy of work at height based on past evaluation data. The evaluation unit can also improve the evaluation accuracy of heavy equipment operation based on past evaluation data. The evaluation unit can also improve the evaluation accuracy of electrical work based on past evaluation data. In this way, the accuracy of the evaluation can be improved by referring to past evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input past evaluation data into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0054] The evaluation unit can perform the evaluation by taking into account the site environmental data. The site environmental data includes, but is not limited to, weather data, time zone data, temperature and humidity data, for example. The evaluation unit can perform the evaluation during rainy weather, for example, based on the site weather data. The evaluation unit can also perform the evaluation during night work based on the site time zone data. The evaluation unit can also perform the evaluation under extreme temperature conditions based on the site temperature data. This allows for more accurate evaluation by taking the site environmental data into consideration. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input the site environmental data into the generation AI and have the generation AI perform the evaluation.

[0055] During the evaluation, the evaluation unit can evaluate the skill level of the worker and perform an evaluation according to the skill. The skill level of the worker includes, for example, whether or not the worker has a qualification, years of experience, and results of a skill test, but is not limited to these examples. For example, the evaluation unit can evaluate the skill level of the worker and perform an evaluation for beginners. The evaluation unit can also evaluate the skill level of the worker and perform an evaluation for experienced workers. The evaluation unit can also evaluate the skill level of the worker and perform an evaluation for workers with specific qualifications. This enables more appropriate evaluation by performing an evaluation according to the worker's skill level. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the worker's skill data into the generation AI and have the generation AI perform an evaluation according to the skill.

[0056] During the evaluation, the evaluation unit can perform a region-specific evaluation by taking into account geographical information of the site. Geographical information includes, but is not limited to, map data, GPS data, and region-specific risk information. For example, the evaluation unit can perform an evaluation in a mountainous area based on the geographical information of the site. The evaluation unit can also perform an evaluation in an urban area based on the geographical information of the site. The evaluation unit can also perform an evaluation in a coastal area based on the geographical information of the site. This enables a region-specific evaluation by taking geographical information into account. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the geographical information of the site to the generation AI and have the generation AI perform a region-specific evaluation.

[0057] During evaluation, the evaluation unit can analyze the work schedule and perform an evaluation appropriate for a specific time period. The work schedule includes, for example, the start and end times of work, the order of work, and the frequency of work, but is not limited to these examples. For example, the evaluation unit can perform an early morning evaluation based on the work schedule. The evaluation unit can also perform an evaluation after a lunch break based on the work schedule. The evaluation unit can also perform an evening evaluation based on the work schedule. In this way, by analyzing the work schedule, an evaluation appropriate for a specific time period can be performed. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input work schedule data into a generation AI and have the generation AI perform an evaluation appropriate for a specific time period.

[0058] During evaluation, the evaluation unit can perform an evaluation for each piece of equipment based on the type of equipment used. The type of equipment used includes, for example, the name of the equipment, the purpose of the equipment, and the risk level of the equipment, but is not limited to these examples. For example, the evaluation unit can perform an evaluation for crane operation based on the type of equipment used. The evaluation unit can also perform an evaluation for power tool use based on the type of equipment used. The evaluation unit can also perform an evaluation for heavy machinery operation based on the type of equipment used. In this way, by performing an evaluation based on the type of equipment used, an appropriate evaluation for each piece of equipment is possible. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the equipment used into the generation AI and have the generation AI perform an evaluation for each piece of equipment.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The identification unit can monitor the health status of on-site workers and identify dangerous tasks based on their health status. For example, it can monitor a worker's heart rate and blood pressure, and if abnormalities are detected, it can identify dangerous tasks for that worker. It can also evaluate the worker's fatigue level and suggest a break if fatigue is accumulating. Furthermore, it can assign specific tasks to other workers based on the worker's health status. This makes it possible to provide a safer work environment by taking the worker's health status into consideration.

[0061] The monitoring unit can monitor noise levels at the work site and evaluate the safety of the work environment based on the noise level. For example, if the noise level exceeds a certain standard, it can recommend that workers use earplugs. It can also suggest that workers stop working if the noise level is high. Furthermore, it can adjust the work schedule based on the noise level and perform work during times when noise levels are lower. In this way, the safety of the work environment can be improved by taking noise levels into consideration.

[0062] The identification unit can identify dangerous work by taking into account the lighting conditions at the work site. For example, if the lighting is insufficient, visibility will be reduced, so specific work can be identified as dangerous. Also, if the lighting is too strong, it can identify dangerous work by taking into account visual impairment caused by glare. Furthermore, if the color temperature of the lighting is inappropriate, it can identify dangerous work by taking into account the reduced concentration of workers. In this way, by taking lighting conditions into account, dangerous work can be identified more accurately.

[0063] The monitoring unit can monitor the temperature and humidity data at the site and evaluate the safety of the work environment based on the temperature and humidity. For example, if the temperature is too high, it can recommend that workers hydrate. If the humidity is too low, it can suggest appropriate moisturizing measures for workers. Furthermore, if the temperature and humidity are not appropriate, it can suggest that work be temporarily suspended. In this way, the safety of the work environment can be improved by taking temperature and humidity data into consideration.

[0064] The identification unit monitors vibration data at the work site and can identify dangerous work based on the vibration. For example, if vibration is strong, it can identify specific machine operations as dangerous work. Also, if vibration continues for a long period of time, it can identify dangerous work by taking into account worker fatigue. Furthermore, if vibration is irregular, it can suggest that work be temporarily suspended. In this way, by taking vibration data into consideration, dangerous work can be identified more accurately.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The Identification Department identifies dangerous work at the site. For example, it can comprehensively identify dangerous work by referring to past examples from similar sites. It can also identify dangerous work by using checklists, observing the site, and interviewing workers. Step 2: The Proposal Department proposes preventive measures for the dangerous work identified by the Identification Department. For example, they can propose preventive measures based on a database of past cases and preventive measures. They can also propose specific measures such as installing safety devices, reviewing work procedures, and implementing education and training. Step 3: The monitoring unit monitors the implementation status of the preventive measures proposed by the proposal unit. For example, it can periodically check the use status of safety harnesses and issue a warning if they are not being used. It can also record the implementation status of regular inspections and send reminders if they are not carried out. Step 4: The evaluation department evaluates the effectiveness based on the implementation status obtained by the monitoring department. For example, the effectiveness of preventive measures can be evaluated by constantly understanding their implementation status. Effectiveness can also be evaluated based on changes in accident rates, worker feedback, cost reduction effects, etc.

[0067] (Example 2) A system according to an embodiment of the present invention is designed to prevent industrial accidents at construction sites. This system uses MECE to identify dangerous worksite tasks, comprehensively identifies dangerous worksite tasks by referencing past examples from similar sites, proposes effective preventive measures for the identified dangerous worksites, monitors the implementation status of the proposed preventive measures, and evaluates their effectiveness. This allows the system to thoroughly identify dangerous worksite tasks, propose specific preventive measures, constantly monitor the implementation status of the preventive measures, and evaluate their effectiveness. For example, the system lists dangerous worksite tasks, such as working at heights, operating heavy machinery, and electrical work, and comprehensively identifies them by referencing past examples. Next, the system proposes specific preventive measures, such as the use of safety harnesses and the installation of scaffolding. Furthermore, the system monitors the implementation status of preventive measures, issuing warnings if they have not been implemented and sending reminders if they have not been implemented. This system can improve safety awareness at construction sites and contribute to preventing industrial accidents.

[0068] The industrial accident prevention system according to the embodiment includes an identification unit, a proposal unit, a monitoring unit, and an evaluation unit. The identification unit identifies dangerous work at a work site. For example, the identification unit can comprehensively identify dangerous work by referring to past examples from similar work sites. The identification unit can also identify dangerous work through the use of checklists, on-site observations, and interviews with workers. The proposal unit proposes preventive measures for the dangerous work identified by the identification unit. For example, the proposal unit can propose preventive measures based on a database of past examples and preventive measures. The proposal unit can also propose specific measures such as installing safety devices, reviewing work procedures, and conducting education and training. The monitoring unit monitors the implementation status of the preventive measures proposed by the proposal unit. For example, the monitoring unit can periodically check the use status of safety harnesses and issue a warning if they are not being used. The monitoring unit can also record the implementation status of regular inspections and send reminders if they have not been carried out. The evaluation unit evaluates the effectiveness based on the implementation status obtained by the monitoring unit. For example, the evaluation unit can constantly monitor the implementation status of the preventive measures and evaluate their effectiveness. The evaluation unit can also evaluate the effectiveness based on changes in accident occurrence rates, worker feedback, cost reduction effects, etc. As a result, the industrial accident prevention system according to the embodiment can prevent industrial accidents by identifying dangerous work on-site, proposing preventive measures, monitoring the implementation status, and evaluating the effectiveness.

[0069] The identification unit can comprehensively identify dangerous work by referring to past examples from similar work sites. Examples of past examples from similar work sites include, but are not limited to, accident reports, work records, and near-miss incidents. The identification unit can, for example, refer to past accident reports to identify similar dangerous work. The identification unit can also analyze work records to identify patterns of dangerous work. The identification unit can also refer to near-miss incidents to identify potentially dangerous work. In this way, by referring to past examples, dangerous work specific to each work site can be identified without being overlooked. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input past accident reports and work records into the generation AI and have the generation AI identify dangerous work.

[0070] The suggestion unit can propose preventive measures based on a database of past examples and preventive measures. The database of preventive measures includes, for example, the type of measure, an evaluation of its effectiveness, and application conditions, but is not limited to these examples. For example, the suggestion unit can refer to past examples and propose specific measures, such as using safety harnesses or installing scaffolding when working at heights. The suggestion unit can also propose measures, such as checking operating procedures for heavy machinery and conducting regular inspections. The suggestion unit can also propose measures, such as using insulated gloves and checking power sources when working with electricity. In this way, effective preventive measures can be proposed based on past examples and the database. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the database of past examples and preventive measures into the generation AI and have the generation AI propose optimal preventive measures.

[0071] The monitoring unit can periodically check the usage status of the safety belt and issue a warning if the safety belt is not being used. Methods for checking the usage status of the safety belt include, but are not limited to, installing a sensor, having workers report, and regular inspections. For example, the monitoring unit can install a sensor on the safety belt and monitor its usage status in real time. The monitoring unit can also have workers report their usage status. The monitoring unit can also periodically inspect the work site and check the usage status of the safety belt. This checks the usage status of the safety belt and issues a warning if the safety belt is not being used, thereby promoting the implementation of safety measures. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input data from a sensor installed on the safety belt into the generation AI and have the generation AI issue a warning if the safety belt is not being used.

[0072] The monitoring unit can record the status of periodic inspections and send reminders if they have not been performed. The content of periodic inspections includes, but is not limited to, inspection items, inspection intervals, and inspection methods. For example, the monitoring unit can list inspection items and record the status of inspections. The monitoring unit can also set inspection intervals and perform inspections periodically. The monitoring unit can also create a manual for inspection methods and instruct workers to perform inspections. This allows the implementation of periodic inspections to be recorded and reminders sent if they have not been performed, thereby promoting the implementation of inspections. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input the status of inspections into the generation AI and have the generation AI send reminders if the inspections have not been performed.

[0073] The evaluation unit can constantly grasp the implementation status of the preventive measures and evaluate their effectiveness. Methods for grasping the implementation status of the preventive measures include, but are not limited to, real-time monitoring, periodic reporting, and database updates. For example, the evaluation unit can monitor the implementation status of the preventive measures in real time and record it in a database. The evaluation unit can also periodically request reports from workers to grasp the implementation status of the preventive measures. The evaluation unit can also record the implementation status of the preventive measures in a database and constantly grasp the latest information. This enables continuous safety measures by constantly grasping the implementation status of the preventive measures and evaluating their effectiveness. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or without AI. For example, the evaluation unit can input the implementation status of the preventive measures into a generation AI and have the generation AI evaluate their effectiveness.

[0074] The identification unit can estimate the user's emotions and adjust the method for identifying dangerous tasks based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, questionnaire surveys, facial expression recognition, and voice analysis. For example, when the user is nervous, the identification unit can provide a simple, visually easy-to-understand interface to facilitate the identification of dangerous tasks. Furthermore, when the user is relaxed, the identification unit can provide an interface with detailed explanations to thoroughly identify dangerous tasks. Furthermore, when the user is in a hurry, the identification unit can prioritize voice input and quickly identify dangerous tasks. This allows for more appropriate identification of dangerous tasks by adjusting the identification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using, for example, an AI, or without an AI. For example, the identification unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0075] The identification unit can analyze past accident data and predict dangerous work under specific conditions. Past accident data includes, but is not limited to, accident reports, statistical data, and near-miss cases. For example, the identification unit can analyze past accident reports and predict dangerous work under specific weather conditions. The identification unit can also predict dangerous work that is likely to occur during specific time periods based on statistical data. The identification unit can also predict dangerous work in specific work environments by referring to near-miss cases. In this way, by analyzing past accident data, dangerous work under specific conditions can be predicted and countermeasures can be taken in advance. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input past accident data into the generation AI and cause the generation AI to predict dangerous work under specific conditions.

[0076] The identification unit can identify dangerous tasks by taking into account the site's environmental data. The site's environmental data includes, but is not limited to, weather data, time zone data, temperature and humidity data, for example. For example, the identification unit can identify dangerous tasks during rainy weather based on the site's weather data. The identification unit can also identify dangerous tasks during nighttime work based on the site's time zone data. The identification unit can also identify dangerous tasks under extreme temperature conditions based on the site's temperature data. This allows for more accurate identification of dangerous tasks by taking into account the site's environmental data. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input the site's environmental data into the generation AI and cause the generation AI to identify dangerous tasks.

[0077] The identification unit can evaluate the skill levels of on-site workers and identify dangerous tasks corresponding to their skills. Methods for evaluating a worker's skill level include, but are not limited to, the presence or absence of qualifications, years of experience, and results of skill tests. For example, the identification unit can evaluate a worker's skill level and identify dangerous tasks for beginners. The identification unit can also evaluate a worker's skill level and identify dangerous tasks for experienced workers. The identification unit can also identify dangerous tasks for workers with specific qualifications. This allows for more appropriate countermeasures to be taken by identifying dangerous tasks corresponding to the worker's skill level. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input worker skill data into a generation AI and have the generation AI identify dangerous tasks corresponding to the worker's skills.

[0078] The identification unit can estimate the user's emotions and prioritize dangerous tasks based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, questionnaire surveys, facial expression recognition, and voice analysis. For example, if the user is nervous, the identification unit can prioritize the most dangerous tasks. If the user is relaxed, the identification unit can display a priority list including detailed explanations. If the user is in a hurry, the identification unit can prioritize dangerous tasks that require a quick response. This enables more effective management of dangerous tasks by determining priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0079] The identification unit can identify dangerous tasks in a specific area by taking into account geographical information of the site. Geographical information of the site includes, but is not limited to, map data, GPS data, and risk information specific to the area. For example, the identification unit can identify dangerous tasks in mountainous areas based on the geographical information of the site. The identification unit can also identify dangerous tasks in urban areas based on the geographical information of the site. The identification unit can also identify dangerous tasks in coastal areas based on the geographical information of the site. In this way, by taking geographical information into consideration, dangerous tasks specific to the area can be identified. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input geographical information of the site to the generation AI and cause the generation AI to identify dangerous tasks.

[0080] The identification unit can analyze the work schedule at the site and identify dangerous work that is likely to occur during certain time periods. The work schedule includes, but is not limited to, the start and end times of work, the order of work, and the frequency of work. For example, the identification unit can identify dangerous work that occurs early in the morning based on the work schedule. The identification unit can also identify dangerous work that occurs after lunch breaks based on the work schedule. The identification unit can also identify dangerous work that occurs at night based on the work schedule. In this way, by analyzing the work schedule, dangerous work that is likely to occur during certain time periods can be identified and countermeasures can be taken in advance. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or without AI. For example, the identification unit can input work schedule data into a generation AI and have the generation AI identify dangerous work that is likely to occur during certain time periods.

[0081] The identification unit can identify dangerous operations for each piece of equipment used at the work site based on the type of equipment. The type of equipment used includes, but is not limited to, the name of the equipment, the purpose of the equipment, and the risk level of the equipment. For example, the identification unit can identify dangerous operations during crane operation based on the type of equipment used. The identification unit can also identify dangerous operations during power tool use based on the type of equipment used. The identification unit can also identify dangerous operations during heavy machinery operation based on the type of equipment used. By identifying dangerous operations based on the type of equipment used, appropriate measures can be taken for each piece of equipment. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data on the equipment used into the generation AI and have the generation AI identify dangerous operations for each piece of equipment.

[0082] The suggestion unit can estimate the user's emotions and adjust the method of proposing preventive measures based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, questionnaire surveys, facial expression recognition, and voice analysis. For example, if the user is nervous, the suggestion unit can suggest simple and visually easy-to-understand preventive measures. Furthermore, if the user is relaxed, the suggestion unit can suggest preventive measures with detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can suggest preventive measures that can be implemented quickly. This allows more effective preventive measures to be proposed by adjusting the suggestion method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0083] When making a proposal, the suggestion unit can select the optimal measure by referring to past effectiveness data of preventive measures. Examples of the effectiveness data of past preventive measures include, but are not limited to, changes in accident rates, worker feedback, and cost reduction effects. For example, the suggestion unit can select the optimal measure for work at heights based on past effectiveness data. The suggestion unit can also select the optimal measure for heavy machinery operation based on past effectiveness data. The suggestion unit can also select the optimal measure for electrical work based on past effectiveness data. This allows optimal preventive measures to be selected by referring to past effectiveness data. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input past effectiveness data into a generation AI and cause the generation AI to select the optimal preventive measure.

[0084] When proposing a countermeasure, the suggestion unit can customize the countermeasure by taking into account the work environment of the site. The work environment includes, but is not limited to, weather, time of day, temperature, humidity, etc. For example, the suggestion unit can suggest preventive measures for rainy weather based on weather data of the site. The suggestion unit can also suggest preventive measures for night work based on time of day data of the site. The suggestion unit can also suggest preventive measures under extreme temperature conditions based on temperature data of the site. This makes it possible to propose more appropriate preventive measures by taking the work environment into consideration. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input work environment data of the site into the generation AI and cause the generation AI to customize the countermeasure.

[0085] When making a proposal, the suggestion unit can propose countermeasures according to the skill level of the worker. Examples of the worker's skill level include, but are not limited to, the presence or absence of qualifications, years of experience, and results of skill tests. For example, the suggestion unit can propose preventive measures for beginners based on the worker's skill level. The suggestion unit can also propose preventive measures for experienced workers based on the worker's skill level. The suggestion unit can also propose preventive measures for workers with specific qualifications based on the worker's skill level. By proposing countermeasures according to the worker's skill level, more effective preventive measures can be taken. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the worker's skill data into the generation AI and cause the generation AI to propose countermeasures according to the worker's skills.

[0086] The suggestion unit can estimate the user's emotions and prioritize preventive measures based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, questionnaire surveys, facial expression recognition, and voice analysis. For example, if the user is nervous, the suggestion unit can prioritize the most important preventive measures. If the user is relaxed, the suggestion unit can also suggest priorities including detailed explanations. If the user is in a hurry, the suggestion unit can also prioritize preventive measures that can be implemented quickly. This allows more effective preventive measures to be proposed by determining priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0087] When making a proposal, the proposal unit can propose region-specific measures by taking into account geographical information of the site. Geographical information includes, but is not limited to, map data, GPS data, and region-specific risk information. For example, the proposal unit can propose prevention measures for mountainous areas based on the geographical information of the site. The proposal unit can also propose prevention measures for urban areas based on the geographical information of the site. The proposal unit can also propose prevention measures for coastal areas based on the geographical information of the site. In this way, region-specific prevention measures can be proposed by taking geographical information into consideration. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the geographical information of the site to the generation AI and cause the generation AI to propose region-specific measures.

[0088] When making a proposal, the suggestion unit can analyze the work schedule and propose countermeasures suitable for a specific time period. The work schedule may include, but is not limited to, the start and end times of work, the order of work, and the frequency of work. For example, the suggestion unit can propose early morning prevention countermeasures based on the work schedule. The suggestion unit can also propose prevention countermeasures after lunch breaks based on the work schedule. The suggestion unit can also propose nighttime prevention countermeasures based on the work schedule. In this way, by analyzing the work schedule, it is possible to propose prevention countermeasures suitable for a specific time period. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input work schedule data into a generation AI and cause the generation AI to propose countermeasures suitable for a specific time period.

[0089] When making a proposal, the suggestion unit can propose countermeasures for each piece of equipment based on the type of equipment used. The type of equipment used includes, for example, the name of the equipment, the purpose of the equipment, and the risk level of the equipment, but is not limited to these examples. The suggestion unit can, for example, propose preventive measures for crane operation based on the type of equipment used. The suggestion unit can also propose preventive measures for power tool use based on the type of equipment used. The suggestion unit can also propose preventive measures for heavy machinery operation based on the type of equipment used. In this way, by proposing countermeasures based on the type of equipment used, appropriate preventive measures can be taken for each piece of equipment. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the equipment used to the generation AI and cause the generation AI to propose countermeasures for each piece of equipment.

[0090] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, questionnaire surveys, facial expression recognition, and voice analysis. For example, if the user is nervous, the monitoring unit can monitor frequently to provide a sense of security. If the user is relaxed, the monitoring unit can monitor at an appropriate frequency. If the user is in a hurry, the monitoring unit can monitor only important points. This enables more effective monitoring by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or without AI. For example, the monitoring unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0091] During monitoring, the monitoring unit can improve the accuracy of monitoring by referring to past implementation status data. Past implementation status data includes, but is not limited to, inspection records, work reports, sensor data, etc. For example, the monitoring unit can improve the monitoring accuracy of work at height based on the past implementation status data. The monitoring unit can also improve the monitoring accuracy of heavy equipment operation based on the past implementation status data. The monitoring unit can also improve the monitoring accuracy of electrical work based on the past implementation status data. In this way, the accuracy of monitoring can be improved by referring to the past implementation status data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past implementation status data into the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0092] The monitoring unit can perform monitoring while taking into account on-site environmental data. On-site environmental data includes, but is not limited to, weather data, time zone data, temperature and humidity data, for example. For example, the monitoring unit can perform monitoring during rainy weather based on on-site weather data. The monitoring unit can also perform monitoring during night work based on on-site time zone data. The monitoring unit can also perform monitoring under extreme temperature conditions based on on-site temperature data. This enables more accurate monitoring by taking the on-site environmental data into consideration. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input on-site environmental data into a generation AI and have the generation AI perform monitoring.

[0093] During monitoring, the monitoring unit can evaluate the skill level of the worker and perform monitoring according to the skill. The skill level of the worker includes, but is not limited to, for example, whether or not the worker has a qualification, years of experience, and results of a skill test. For example, the monitoring unit can evaluate the skill level of the worker and perform monitoring for beginners. The monitoring unit can also evaluate the skill level of the worker and perform monitoring for experienced workers. The monitoring unit can also evaluate the skill level of the worker and perform monitoring for workers with specific qualifications. This enables more effective monitoring by performing monitoring according to the worker's skill level. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the worker's skill data into the generation AI and have the generation AI perform monitoring according to the skill.

[0094] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, questionnaire surveys, facial expression recognition, and voice analysis. For example, if the user is nervous, the monitoring unit can prioritize monitoring of the most important tasks. Furthermore, if the user is relaxed, the monitoring unit can suggest priorities including detailed explanations. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring of tasks that require a quick response. This enables more effective monitoring by determining priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the monitoring unit may be performed using AI, or without AI. For example, the monitoring unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0095] During monitoring, the monitoring unit can perform region-specific monitoring by taking into account geographical information of the site. Geographical information includes, but is not limited to, map data, GPS data, and region-specific risk information. For example, the monitoring unit can perform monitoring in mountainous areas based on the geographical information of the site. The monitoring unit can also perform monitoring in urban areas based on the geographical information of the site. The monitoring unit can also perform monitoring in coastal areas based on the geographical information of the site. This enables region-specific monitoring by taking geographical information into account. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input geographical information of the site to the generation AI and have the generation AI perform region-specific monitoring.

[0096] During monitoring, the monitoring unit can analyze the work schedule and perform monitoring appropriate for a specific time period. The work schedule includes, for example, but is not limited to, the start and end times of work, the order of work, and the frequency of work. For example, the monitoring unit can perform early morning monitoring based on the work schedule. The monitoring unit can also perform monitoring after a lunch break based on the work schedule. The monitoring unit can also perform nighttime monitoring based on the work schedule. In this way, by analyzing the work schedule, monitoring appropriate for a specific time period becomes possible. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input work schedule data into a generation AI and have the generation AI perform monitoring appropriate for a specific time period.

[0097] During monitoring, the monitoring unit can monitor each piece of equipment based on the type of equipment used. The type of equipment used includes, for example, the name of the equipment, the purpose of the equipment, and the risk level of the equipment, but is not limited to these examples. For example, the monitoring unit can monitor the operation of a crane based on the type of equipment used. The monitoring unit can also monitor the use of power tools based on the type of equipment used. The monitoring unit can also monitor the operation of heavy machinery based on the type of equipment used. In this way, monitoring based on the type of equipment used enables appropriate monitoring of each piece of equipment. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the equipment used to the generation AI and have the generation AI perform monitoring for each piece of equipment.

[0098] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, questionnaire surveys, facial expression recognition, and voice analysis. For example, if the user is nervous, the evaluation unit can provide simple, visually easy-to-understand evaluation criteria. If the user is relaxed, the evaluation unit can provide evaluation criteria with detailed explanations. If the user is in a hurry, the evaluation unit can provide criteria that allow for quick evaluation. This enables more appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, or without AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0099] The evaluation unit can improve the accuracy of the evaluation by referring to past evaluation data during the evaluation. Past evaluation data includes, but is not limited to, changes in accident rates, worker feedback, and cost reduction effects. For example, the evaluation unit can improve the evaluation accuracy of work at height based on past evaluation data. The evaluation unit can also improve the evaluation accuracy of heavy equipment operation based on past evaluation data. The evaluation unit can also improve the evaluation accuracy of electrical work based on past evaluation data. In this way, the accuracy of the evaluation can be improved by referring to past evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input past evaluation data into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0100] The evaluation unit can perform the evaluation by taking into account the site environmental data. The site environmental data includes, but is not limited to, weather data, time zone data, temperature and humidity data, for example. The evaluation unit can perform the evaluation during rainy weather, for example, based on the site weather data. The evaluation unit can also perform the evaluation during night work based on the site time zone data. The evaluation unit can also perform the evaluation under extreme temperature conditions based on the site temperature data. This allows for more accurate evaluation by taking the site environmental data into consideration. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input the site environmental data into the generation AI and have the generation AI perform the evaluation.

[0101] During the evaluation, the evaluation unit can evaluate the skill level of the worker and perform an evaluation according to the skill. The skill level of the worker includes, for example, whether or not the worker has a qualification, years of experience, and results of a skill test, but is not limited to these examples. For example, the evaluation unit can evaluate the skill level of the worker and perform an evaluation for beginners. The evaluation unit can also evaluate the skill level of the worker and perform an evaluation for experienced workers. The evaluation unit can also evaluate the skill level of the worker and perform an evaluation for workers with specific qualifications. This enables more appropriate evaluation by performing an evaluation according to the worker's skill level. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the worker's skill data into the generation AI and have the generation AI perform an evaluation according to the skill.

[0102] The evaluation unit can estimate the user's emotions and determine the priority of evaluation based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, questionnaire surveys, facial expression recognition, and voice analysis. For example, if the user is nervous, the evaluation unit can prioritize the evaluation of the most important tasks. Furthermore, if the user is relaxed, the evaluation unit can propose priorities including detailed explanations. Furthermore, if the user is in a hurry, the evaluation unit can prioritize the evaluation of tasks that require a quick response. This enables more effective evaluation by determining priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotions.

[0103] During the evaluation, the evaluation unit can perform a region-specific evaluation by taking into account geographical information of the site. Geographical information includes, but is not limited to, map data, GPS data, and region-specific risk information. For example, the evaluation unit can perform an evaluation in a mountainous area based on the geographical information of the site. The evaluation unit can also perform an evaluation in an urban area based on the geographical information of the site. The evaluation unit can also perform an evaluation in a coastal area based on the geographical information of the site. This enables a region-specific evaluation by taking geographical information into account. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the geographical information of the site to the generation AI and have the generation AI perform a region-specific evaluation.

[0104] During evaluation, the evaluation unit can analyze the work schedule and perform an evaluation appropriate for a specific time period. The work schedule includes, for example, the start and end times of work, the order of work, and the frequency of work, but is not limited to these examples. For example, the evaluation unit can perform an early morning evaluation based on the work schedule. The evaluation unit can also perform an evaluation after a lunch break based on the work schedule. The evaluation unit can also perform an evening evaluation based on the work schedule. In this way, by analyzing the work schedule, an evaluation appropriate for a specific time period can be performed. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input work schedule data into a generation AI and have the generation AI perform an evaluation appropriate for a specific time period.

[0105] During evaluation, the evaluation unit can perform an evaluation for each piece of equipment based on the type of equipment used. The type of equipment used includes, for example, the name of the equipment, the purpose of the equipment, and the risk level of the equipment, but is not limited to these examples. For example, the evaluation unit can perform an evaluation for crane operation based on the type of equipment used. The evaluation unit can also perform an evaluation for power tool use based on the type of equipment used. The evaluation unit can also perform an evaluation for heavy machinery operation based on the type of equipment used. In this way, by performing an evaluation based on the type of equipment used, an appropriate evaluation for each piece of equipment is possible. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the equipment used into the generation AI and have the generation AI perform an evaluation for each piece of equipment. === Hard Collateral 1-1 === Each of the multiple elements, including the identification unit, proposal unit, monitoring unit, and evaluation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the identification unit observes dangerous work at the work site using the camera 42 and microphone 38B of the smart device 14, and comprehensively identifies dangerous work by referring to past examples of similar work sites using the control unit 46A. The proposal unit, realized, for example, by the identification processing unit 290 of the data processing device 12, proposes specific preventive measures based on a database of past examples and preventive measures. The monitoring unit, realized, for example, by the control unit 46A of the smart device 14, periodically checks the use status of safety harnesses and issues a warning if they are not being used. The evaluation unit, realized, for example, by the identification processing unit 290 of the data processing device 12, constantly monitors the implementation status of preventive measures and evaluates their effectiveness. === Hard Collateral 1-2 === Each of the multiple elements, including the identification unit, proposal unit, monitoring unit, and evaluation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the identification unit observes dangerous work at the site using the camera 42 and microphone 238 of the smart glasses 214, and comprehensively identifies dangerous work by referring to past examples of similar work sites using the control unit 46A. The proposal unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and proposes specific preventive measures based on a database of past examples and preventive measures. The monitoring unit is realized, for example, by the control unit 46A of the smart glasses 214, and periodically checks the use status of the safety harness and issues a warning if it is not being used. The evaluation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and constantly monitors the implementation status of preventive measures and evaluates their effectiveness. === Hard Collateral 1-3 === Each of the multiple elements, including the identification unit, proposal unit, monitoring unit, and evaluation unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the identification unit observes dangerous work at the site using the camera 42 and microphone 238 of the headset terminal 314, and comprehensively identifies dangerous work by referring to past examples of similar work sites using the control unit 46A. The proposal unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and proposes specific preventive measures based on a database of past examples and preventive measures. The monitoring unit is realized, for example, by the control unit 46A of the headset terminal 314, and periodically checks the use status of safety harnesses and issues a warning if they are not being used. The evaluation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and constantly monitors the implementation status of preventive measures and evaluates their effectiveness. === Hard Collateral 1-4 === Each of the multiple elements, including the identification unit, proposal unit, monitoring unit, and evaluation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the identification unit observes dangerous work at the site using the camera 42 and microphone 238 of the robot 414, and comprehensively identifies dangerous work by referring to past examples of similar work sites using the control unit 46A. The proposal unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and proposes specific preventive measures based on a database of past examples and preventive measures. The monitoring unit is realized, for example, by the control unit 46A of the robot 414, and periodically checks the use status of the safety harness and issues a warning if it is not being used. The evaluation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and constantly monitors the implementation status of preventive measures and evaluates their effectiveness.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The identification unit can monitor the health status of on-site workers and identify dangerous tasks based on their health status. For example, it can monitor a worker's heart rate and blood pressure, and if abnormalities are detected, it can identify dangerous tasks for that worker. It can also evaluate the worker's fatigue level and suggest a break if fatigue is accumulating. Furthermore, it can assign specific tasks to other workers based on the worker's health status. This makes it possible to provide a safer work environment by taking the worker's health status into consideration.

[0108] The suggestion unit can estimate the user's emotions and adjust the proposed content of preventive measures based on the estimated user emotions. For example, if the user feels anxious, the suggestion unit can suggest preventive measures with more detailed explanations. If the user feels confident, the suggestion unit can suggest concise preventive measures. Furthermore, if the user feels anxious, the suggestion unit can prioritize the suggestion of preventive measures that can be implemented quickly. In this way, by adjusting the proposed content according to the user's emotions, more effective preventive measures can be provided.

[0109] The monitoring unit can monitor noise levels at the work site and evaluate the safety of the work environment based on the noise level. For example, if the noise level exceeds a certain standard, it can recommend that workers use earplugs. It can also suggest that workers stop working if the noise level is high. Furthermore, it can adjust the work schedule based on the noise level and perform work during times when noise levels are lower. In this way, the safety of the work environment can be improved by taking noise levels into consideration.

[0110] The evaluation unit can estimate the user's emotions and adjust the evaluation feedback method based on the estimated user's emotions. For example, if the user is nervous, positive feedback can be given preferentially. If the user is relaxed, detailed feedback can be given. Furthermore, if the user is in a hurry, brief feedback can be given. This allows for more effective evaluation by adjusting the feedback method according to the user's emotions.

[0111] The identification unit can identify dangerous work by taking into account the lighting conditions at the work site. For example, if the lighting is insufficient, visibility will be reduced, so specific work can be identified as dangerous. Also, if the lighting is too strong, it can identify dangerous work by taking into account visual impairment caused by glare. Furthermore, if the color temperature of the lighting is inappropriate, it can identify dangerous work by taking into account the reduced concentration of workers. In this way, by taking lighting conditions into account, dangerous work can be identified more accurately.

[0112] The suggestion unit can estimate the user's emotions and adjust the implementation method of preventive measures based on the estimated user emotions. For example, if the user feels anxious, it can suggest preventive measures that can be implemented in stages. Also, if the user feels confident, it can suggest implementing multiple preventive measures at once. Furthermore, if the user feels anxious, it can prioritize and suggest preventive measures that can be implemented quickly. In this way, by adjusting the implementation method according to the user's emotions, it is possible to provide more effective preventive measures.

[0113] The monitoring unit can monitor the temperature and humidity data at the site and evaluate the safety of the work environment based on the temperature and humidity. For example, if the temperature is too high, it can recommend that workers hydrate. If the humidity is too low, it can suggest appropriate moisturizing measures for workers. Furthermore, if the temperature and humidity are not appropriate, it can suggest that work be temporarily suspended. In this way, the safety of the work environment can be improved by taking temperature and humidity data into consideration.

[0114] The evaluation unit can estimate the user's emotions and adjust the timing of the evaluation based on the estimated user's emotions. For example, if the user is nervous, the timing of the evaluation can be delayed. Also, if the user is relaxed, the timing of the evaluation can be advanced. Furthermore, if the user is in a hurry, the evaluation can be made quickly. In this way, by adjusting the timing of the evaluation according to the user's emotions, more appropriate evaluation can be made.

[0115] The identification unit monitors vibration data at the work site and can identify dangerous work based on the vibration. For example, if vibration is strong, it can identify specific machine operations as dangerous work. Also, if vibration continues for a long period of time, it can identify dangerous work by taking into account worker fatigue. Furthermore, if vibration is irregular, it can suggest that work be temporarily suspended. In this way, by taking vibration data into consideration, dangerous work can be identified more accurately.

[0116] The suggestion unit can estimate the user's emotions and adjust the priority of prevention measures based on the estimated user emotions. For example, if the user feels anxious, the most important prevention measures can be suggested with priority. If the user feels confident, prevention measures including detailed explanations can be suggested. Furthermore, if the user feels anxious, prevention measures that can be implemented quickly can be suggested with priority. In this way, by adjusting the priority according to the user's emotions, more effective prevention measures can be provided.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The Identification Department identifies dangerous work at the site. For example, it can comprehensively identify dangerous work by referring to past examples from similar sites. It can also identify dangerous work by using checklists, observing the site, and interviewing workers. Step 2: The Proposal Department proposes preventive measures for the dangerous work identified by the Identification Department. For example, they can propose preventive measures based on a database of past cases and preventive measures. They can also propose specific measures such as installing safety devices, reviewing work procedures, and implementing education and training. Step 3: The monitoring unit monitors the implementation status of the preventive measures proposed by the proposal unit. For example, it can periodically check the use status of safety harnesses and issue a warning if they are not being used. It can also record the implementation status of regular inspections and send reminders if they are not carried out. Step 4: The evaluation department evaluates the effectiveness based on the implementation status obtained by the monitoring department. For example, the effectiveness of preventive measures can be evaluated by constantly understanding their implementation status. Effectiveness can also be evaluated based on changes in accident rates, worker feedback, cost reduction effects, etc.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A team that identifies dangerous work on-site, a proposal unit that proposes preventive measures for the dangerous work identified by the identification unit; a monitoring unit that monitors the implementation status of the prevention measures proposed by the proposal unit; an evaluation unit that evaluates the effectiveness based on the implementation status obtained by the monitoring unit; Equipped with A system characterized by:

2. The washing-out section includes: Comprehensively identify dangerous work by looking at past examples from similar work sites 2. The system of claim 1.

3. The proposal unit Propose prevention measures based on a database of past cases and prevention measures 2. The system of claim 1.

4. The monitoring unit Regularly check the use of safety belts and issue a warning if they are not being used.

2. The system of claim 1.

5. The monitoring unit Record the status of regular inspections and send reminders if they are missed 2. The system of claim 1.

6. The evaluation unit Always monitor the implementation status of prevention measures and evaluate their effectiveness 2. The system of claim 1.

7. The washing-out section includes: Estimate the user's emotions and adjust the method for identifying dangerous tasks based on the estimated user emotions.

2. The system of claim 1.

8. The washing-out section includes: Analyzing past accident data to predict dangerous work under specific conditions 2. The system of claim 1.

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