System

Abstract

To provide a system according to an embodiment that identifies a true cause of an accident, and calls for attention, utilizing accident information on other sections.SOLUTION: A system according to an embodiment includes: a collection unit; an analysis unit; an identification unit; and an attention unit. The collection unit is configured to collect detail information on an accident. The analysis unit is configured to analyze the information collected by the collection unit. The identification unit is configured to identify a true cause of the accident on the basis of the information analyzed by the analysis unit. The attention unit is configured to analyze accident information on other section to call for attention.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately identify the true cause of an accident or effectively utilize accident information from other departments, leaving room for improvement.

[0005] The system according to the embodiment aims to identify the root cause of an accident and to call attention to the issue by utilizing accident information from other departments. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an identification unit, and a warning unit. The collection unit collects detailed information about the accident. The analysis unit analyzes the information collected by the collection unit. The identification unit identifies the true cause of the accident based on the information analyzed by the analysis unit. The warning unit analyzes accident information from other departments and issues a warning. Effect of the Invention

[0007] The system according to the embodiment can identify the root cause of an accident and call attention to the issue by utilizing accident information from other departments. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The accident recurrence prevention system according to the embodiment of the present invention is a system in which detailed information on an accident is collected, analyzed by a generation AI, the true cause is identified, and accident information from other departments is used to warn about the accident. In this system, when an accident occurs, detailed information on the accident is input to the generation AI, which analyzes the information and identifies the true cause of the accident. Furthermore, the generation AI also warns about accidents that occur in other departments at the time of release. For example, when an accident occurs, detailed information on the accident is input to the generation AI. For example, information such as the date and time of the accident, the location, the situation, the people involved, the equipment used, and the work content is input. This information is analyzed by the generation AI. Next, the generation AI analyzes the input information and identifies the true cause of the accident. The generation AI estimates the true cause of the accident based on past accident data and related information. For example, it is possible to identify specific causes such as machine failure and human error. Furthermore, the generation AI also warns about accidents that occur in other departments at the time of release. For example, the generation AI analyzes information on accidents that occur in other departments and warns about accidents that do not occur in the own department. At this time, the generating AI identifies points to watch out for based on past accident data and related information. This system accurately identifies the true cause of the accident and prevents recurrence by utilizing accident information from other departments. For example, if the cause was a machine failure, recurrence can be prevented by strengthening regular inspections and maintenance. Furthermore, if the cause was human error, recurrence can be prevented by strengthening education and training. In this way, the accident recurrence prevention system can accurately identify the true cause of the accident and prevent recurrence by utilizing accident information from other departments.

[0029] The accident recurrence prevention system according to the embodiment includes a collection unit, an analysis unit, an identification unit, and a warning unit. The collection unit collects detailed information about the accident. The detailed information about the accident includes, for example, the date and time of the accident, the location, the situation, the people involved, the equipment used, the work content, and the like, but is not limited to such examples. The collection unit can, for example, collect manually or collect using an automated sensor. The analysis unit uses a generation AI to analyze the information collected by the collection unit. The analysis is performed, for example, by a method such as data mining, statistical analysis, or machine learning algorithm, but is not limited to such examples. For example, the generation AI estimates the true cause of the accident based on past accident data and related information. The identification unit uses the generation AI to identify the true cause of the accident based on the information analyzed by the analysis unit. Identification can identify specific causes, such as machine failure and human error. For example, the generation AI identifies the type and cause of the machine failure. The identification unit can also identify specific causes of human error. The warning unit uses the generation AI to analyze accident information from other departments and urges caution so that similar accidents do not occur in the own department. Caution is given, for example, by displaying a warning message or implementing an educational program, but is not limited to such examples. For example, the generation AI analyzes information on accidents that have occurred in other departments and identifies points to be careful about. As a result, the accident recurrence prevention system according to the embodiment collects and analyzes detailed information on accidents, identifies the true cause, and uses accident information from other departments to urge caution, thereby making it possible to prevent accidents from recurring.

[0030] The collection unit collects detailed information about the accident. Detailed information about the accident includes, for example, the date and time of the accident, the location, the situation, the people involved, the equipment used, the work content, and the like, but is not limited to such examples. The collection unit can, for example, collect information manually or by an automated sensor. Specifically, manual collection is performed through interviews with the people involved and the creation of a report after the accident occurs. The people involved are witnesses to the accident, the parties involved, managers, etc., and detailed testimonies and reports from them are collected. On the other hand, collection by an automated sensor is performed through various sensors and cameras installed at the site. For example, temperature sensors, vibration sensors, pressure sensors, etc. are installed at the work site, and these sensors collect data in real time. In addition, the surveillance camera can record the moment of the accident as a video and use it for later analysis. Furthermore, the collection unit can also collect the operating status of the equipment and abnormality detection data by utilizing an IoT device. This allows the collection unit to collect comprehensive data from various information sources and grasp the detailed situation of the accident. The collected data is stored in a central database and managed so that the analysis unit and the identification unit can access it. This enables the collection unit to efficiently and accurately collect detailed information about an accident, improving the reliability of the entire system.

[0031] The analysis unit uses the generation AI to analyze the information collected by the collection unit. The analysis is performed by, for example, data mining, statistical analysis, machine learning algorithms, and other methods, but is not limited to these examples. Specifically, the generation AI preprocesses the collected data and removes noise and missing values. Next, data mining technology is used to extract useful patterns and trends from the data. For example, based on past accident data, it is possible to identify the frequency of accidents under specific conditions and common factors. In statistical analysis, the collected data is analyzed using statistical methods to evaluate the probability of accidents and the scope of their impact. Machine learning algorithms learn from past data and make predictions and classifications for new data. For example, the generation AI can learn from past accident data and estimate the true cause of an accident from current data. In addition, the generation AI can use natural language processing technology to analyze reports and interview contents of related parties and extract important information. This allows the analysis unit to analyze the collected data from multiple angles and quickly and accurately identify the true cause of an accident and risk factors. The analysis results are provided to the identification unit and the warning unit and are used for further analysis and countermeasure planning.

[0032] The identification unit uses the generation AI to identify the true cause of the accident based on the information analyzed by the analysis unit. Identification can identify specific causes, such as machine failure or human error. Specifically, the generation AI identifies the type and cause of the machine failure based on the data provided by the analysis unit. For example, it can analyze the sensor data of the machine and identify that abnormal vibrations or temperature increases are precursors to failure. The generation AI can also learn past failure data and identify failures with similar patterns. Furthermore, the identification unit can identify the specific cause of human error. For example, if the cause is an incorrect work procedure or non-compliance with safety rules, the generation AI analyzes the reports and interviews of the relevant parties to identify the specific content of the mistake and the circumstances under which it occurred. This allows the identification unit to identify the true cause of the accident in detail, such as machine failure or human error, and to plan specific measures to prevent recurrence. The identified true cause is provided to the attention unit and is used to prevent similar accidents in other departments.

[0033] The attention department uses the generation AI to analyze accident information from other departments and warn them not to cause similar accidents in their own departments. The warning is given, for example, by displaying a warning message or implementing an educational program, but is not limited to such examples. Specifically, the generation AI analyzes information on accidents that have occurred in other departments and identifies points to be careful about. For example, the generation AI evaluates similar risks in the own department based on information on machine failures and human errors that have occurred in other departments. The generation AI can learn past accident data and related information and identify risk factors under specific conditions. Next, the attention department displays a warning message based on the identified risk factors. For example, the generation AI notifies workers of points to be careful about and safety procedures when using a specific machine. It is also possible to implement educational programs and provide training to workers to increase their safety awareness. Furthermore, the attention department can conduct regular safety inspections and risk assessments to detect risk factors early and take measures. As a result, the attention department can utilize accident information from other departments to work to prevent accidents from recurring in its own department. The activities of the attention department contribute to fostering a company-wide safety culture and preventing accidents, and can improve the reliability and safety of the entire system.

[0034] The collection unit can collect information on the date and time of the accident, the location, the situation, the people involved, the equipment used, and the work content. For example, the collection unit collects the date and time of the accident in the form of a timestamp or a date and time. The collection unit can also collect the location of the accident in the form of GPS coordinates, an address, the name of the facility, and the like. Furthermore, the collection unit can collect the situation of the accident in the form of a detailed description, environmental conditions, and the like. For example, the collection unit records the date and time of the accident in the form of a timestamp, and identifies the location of the accident by GPS coordinates. The collection unit can also record the situation of the accident in detail and collect information on the people involved. For example, the collection unit collects information on people involved in the accident and witnesses. The collection unit can also collect information on the type of equipment used, the serial number, the usage status, and the like. For example, the collection unit collects detailed information on the equipment used at the time of the accident and records the work content. As a result, the collection unit can collect detailed information on the accident, enabling accurate analysis. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input detailed information about an accident into the generation AI, which can then analyze the information.

[0035] The analysis unit can estimate the true cause of the accident based on past accident data and related information. For example, the analysis unit acquires past accident data from a database and performs analysis. The past accident data includes, for example, past accident reports and databases, but is not limited to such examples. The analysis unit applies an algorithm for estimating the true cause of the accident based on the past accident data. For example, the analysis unit uses a data mining algorithm to extract patterns from the past accident data and estimate the true cause of the accident. The analysis unit can also use a machine learning algorithm to learn the past accident data and estimate the true cause of the accident. For example, the analysis unit uses the past accident data to identify specific causes such as machine failure and human error. Furthermore, the analysis unit can also estimate the true cause of the accident based on related information. The related information includes, for example, related laws, regulations, technical literature, etc., but is not limited to such examples. For example, the analysis unit refers to related technical literature to estimate the true cause of the accident. As a result, the analysis unit can accurately identify the cause by estimating the true cause based on the past accident data and related information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs past accident data to the generation AI, which can estimate the true cause.

[0036] The identification unit can identify the specific cause of a machine failure or a human error. The identification unit, for example, identifies the type and cause of the machine failure. Machine failures include, for example, wear and breakage of parts, system malfunctions, etc., but are not limited to such examples. The identification unit analyzes the operation data of the machine to identify the cause of the machine failure. For example, the identification unit analyzes the operation log of the machine to identify the cause of the failure. The identification unit can also identify the specific cause of the human error. Human errors include, for example, operation errors and judgment errors, but are not limited to such examples. The identification unit analyzes the work procedure and the work environment to identify the cause of the human error. For example, the identification unit analyzes the record of the work procedure to identify the cause of the error. The identification unit can also analyze the data of the work environment to identify the cause of the error. As a result, the identification unit can identify the specific cause and take appropriate measures. A part 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 machine operation data and records of work procedures into the generation AI, allowing the generation AI to identify the cause.

[0037] The warning unit can analyze information on accidents that have occurred in other departments and call attention so that similar accidents do not occur in the department. For example, the warning unit collects and analyzes information on accidents that have occurred in other departments. Accident information on other departments includes, for example, the date and time of the accident, the location, the situation, the people involved, the equipment used, the work content, and the like, but is not limited to such examples. The warning unit applies an algorithm for identifying points of attention based on the accident information of other departments. For example, the warning unit uses a data mining algorithm to extract patterns from the accident information of other departments and identify points of attention. The warning unit can also use a machine learning algorithm to learn the accident information of other departments and identify points of attention. For example, the warning unit can use the accident information of other departments to call attention so that similar accidents do not occur. Furthermore, the warning unit can call attention by methods such as displaying a warning message or implementing an educational program based on the accident information of other departments. For example, the warning unit analyzes the accident information of other departments, identifies points of attention, and displays a warning message. The warning unit can also implement an educational program based on the accident information of other departments to call attention. This allows the warning unit to prevent recurrence by utilizing accident information from other departments. Some or all of the above-mentioned processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input accident information from other departments to the generation AI, which can then identify points to be careful about.

[0038] The attention unit can specify points to be noted based on past accident data and related information. The attention unit, for example, acquires past accident data from a database and performs analysis. The past accident data includes, for example, past accident reports and databases, but is not limited to such examples. The attention unit applies an algorithm for specifying points to be noted based on past accident data. For example, the attention unit uses a data mining algorithm to extract patterns from past accident data and specify points to be noted. The attention unit can also use a machine learning algorithm to learn past accident data and specify points to be noted. For example, the attention unit uses past accident data to call attention so that a similar accident does not occur. Furthermore, the attention unit can specify points to be noted based on related information. The related information includes, for example, related laws, regulations, technical literature, etc., but is not limited to such examples. For example, the attention unit refers to related technical literature and specifies points to be noted. As a result, the attention unit can specify points to be noted based on past accident data and related information, thereby making it possible to call attention appropriately. A part or all of the above-mentioned processing in the attention unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input past accident data into the generation AI, which can then identify points to watch out for.

[0039] The identification unit can propose measures to strengthen regular inspections and maintenance. The identification unit, for example, proposes the contents and frequency of regular inspections. Regular inspections include, for example, inspection items and inspection intervals, but are not limited to such examples. The identification unit analyzes the operation data of the machine and past inspection records in order to propose the contents of regular inspections. For example, the identification unit analyzes the operation data of the machine and identifies items that require inspection. The identification unit can also analyze past inspection records and propose inspection intervals. Furthermore, the identification unit proposes the contents and methods of maintenance. Maintenance includes, for example, maintenance procedures and tools and materials to be used, but are not limited to such examples. The identification unit analyzes the operation data of the machine and past maintenance records in order to propose the contents of maintenance. For example, the identification unit analyzes the operation data of the machine and identifies items that require maintenance. The identification unit can also analyze past maintenance records and propose maintenance procedures. As a result, the identification unit can prevent recurrence by strengthening regular inspections and maintenance. A part 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 machine operation data and past inspection records into the generation AI, which can then suggest the contents of inspection and maintenance.

[0040] The identification unit can propose measures to strengthen education and training. The identification unit, for example, proposes the contents and implementation method of an education program. The education program includes, for example, the contents and implementation method of education, but is not limited to such an example. The identification unit analyzes past accident data and related information in order to propose the contents of the education program. For example, the identification unit analyzes past accident data and identifies items that require education. The identification unit can also propose the contents of the education program based on the related information. Furthermore, the identification unit proposes the contents and method of training. The training includes, for example, a training scenario and implementation method, but is not limited to such an example. The identification unit analyzes past accident data and related information in order to propose the contents of training. For example, the identification unit analyzes past accident data and identifies items that require training. The identification unit can also propose a training scenario based on the related information. As a result, the identification unit can prevent the recurrence of human error by strengthening education and training. A part 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 and related information into the generation AI, which can then suggest content for education and training.

[0041] When collecting accident information, the collection unit can analyze the user's past accident report history and select the optimal collection method. For example, if the user has made a detailed report in the past, the collection unit collects similar detailed information. In addition, if the user has made a brief report in the past, the collection unit can preferentially collect brief information. Furthermore, if specific information is lacking from the user's past report history, the collection unit can also focus on collecting that information. This allows the collection unit to select the optimal collection method by analyzing the past report history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past accident report history to the generation AI, and the generation AI can select the optimal collection method.

[0042] The collection unit can perform filtering based on the user's current work situation and field of interest when collecting accident information. For example, the collection unit preferentially collects related accident information according to the user's current work situation. The collection unit can also filter and collect specific accident information based on the user's field of interest. Furthermore, the collection unit can combine the user's current work situation and field of interest to collect optimal accident information. This allows the collection unit to collect highly relevant information by filtering based on the current work situation and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's current work situation and field of interest to the generation AI, and the generation AI can perform filtering.

[0043] When collecting accident information, the collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user. For example, the collection unit preferentially collects accident information that occurred in a place close to the user's current location. In addition, the collection unit can collect highly relevant accident information based on the user's past movement history. Furthermore, the collection unit can also preferentially collect accident information that occurred in a specific area based on the user's geographical location information. In this way, the collection unit can preferentially collect highly relevant information by considering the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's geographical location information to the generation AI, and the generation AI can preferentially collect highly relevant information.

[0044] The collection unit can analyze the user's social media activity and collect related information when collecting the accident information. The collection unit can collect related accident information based on, for example, information shared by the user on social media. The collection unit can also collect accident information related to a specific field of interest from the user's social media activity. Furthermore, the collection unit can analyze the user's social media activity and collect optimal accident information. In this way, the collection unit can collect related information by analyzing the social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI, and the generation AI can collect related information.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the accident during the analysis. For example, in the case of a serious accident, the analysis unit performs a detailed analysis to identify the specific cause. In addition, in the case of a minor accident, the analysis unit can perform a concise analysis to provide a result that covers the main points. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the accident to provide an optimal result. As a result, the analysis unit can provide an optimal analysis result by adjusting the level of detail of the analysis based on the importance of the accident. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the accident to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the accident category during analysis. For example, in the case of an accident caused by a machine failure, the analysis unit performs analysis based on the operation data of the machine. In addition, in the case of an accident caused by human error, the analysis unit can perform analysis based on the work procedure and the work environment. Furthermore, the analysis unit can apply an optimal analysis algorithm depending on the accident category to identify the specific cause. As a result, the analysis unit can identify the specific cause by applying an optimal analysis algorithm depending on the accident category. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input accident category data to the generation AI, and the generation AI can apply the optimal analysis algorithm.

[0047] The analysis unit can determine the priority of the analysis based on the time of occurrence of the accident during the analysis. For example, the analysis unit prioritizes the analysis of the accident that occurred recently and quickly identifies the cause. The analysis unit can also refer to the accidents that occurred in the past and analyze to prevent similar accidents from recurring. Furthermore, the analysis unit can also determine the optimal priority of the analysis based on the time of the accident. As a result, the analysis unit can quickly identify the cause by determining the priority of the analysis based on the time of the accident. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data on the time of the accident occurrence to the generation AI, and the generation AI can determine the priority of the analysis.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the accidents during analysis. For example, the analysis unit prioritizes the analysis of highly relevant accidents and quickly identifies the cause. The analysis unit can also postpone the analysis of less relevant accidents and prioritize the analysis of important accidents. Furthermore, the analysis unit can also adjust the optimal order of analysis based on the relevance of the accidents. As a result, the analysis unit can prioritize the analysis of important accidents by adjusting the order of analysis based on the relevance of the accidents. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input accident relevance data to the generation AI, and the generation AI can adjust the order of analysis.

[0049] At the time of identification, the identification unit can select an optimal identification method by analyzing the user's past accident reports. For example, if the user has made a detailed report in the past, the identification unit can provide a similar detailed identification method. In addition, if the user has made a brief report in the past, the identification unit can provide a brief identification method. Furthermore, if specific information is lacking from the user's past report history, the identification unit can also focus on identifying that information. This allows the identification unit to select an optimal identification method by analyzing past accident reports. A part 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 the user's past accident report data into the generation AI, and the generation AI can select an optimal identification method.

[0050] The identification unit can customize the specific means based on the user's current work situation at the time of identification. The identification unit provides a related specific means according to the user's current work situation, for example. The identification unit can also customize the specific means based on the user's current work situation. Furthermore, the identification unit can provide an optimal specific means by combining the user's current work situation and the user's field of interest. This allows the identification unit to provide a more appropriate specific means by customizing the specific means based on the current work situation. A part 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 the user's current work situation data to the generation AI, and the generation AI can customize the specific means.

[0051] The identification unit can select the optimal identification method in consideration of the geographical location information of the user at the time of identification. For example, the identification unit preferentially identifies accident information that occurred in a place close to the user's current location. In addition, the identification unit can identify highly relevant accident information based on the user's past movement history. Furthermore, the identification unit can also preferentially identify accident information that occurred in a specific area based on the user's geographical location information. Thereby, the identification unit can select the optimal identification method by considering the geographical location information. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the user's geographical location information to the generation AI, and the generation AI can select the optimal identification method.

[0052] At the time of identification, the identification unit can analyze the user's social media activity and suggest a specific means. The identification unit can, for example, suggest a related identification means based on information shared by the user on social media. The identification unit can also suggest an identification means related to a specific field of interest from the user's social media activity. Furthermore, the identification unit can analyze the user's social media activity and suggest an optimal identification means. In this way, the identification unit can suggest an optimal identification means by analyzing the social media activity. A part 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 the user's social media activity data to the generation AI, which can then suggest a specific means.

[0053] The attention unit can predict current points to be warned by referring to past accident data during warning. The attention unit can predict current points to be warned based on, for example, past similar accident data. The attention unit can also extract a specific pattern from past accident data and predict current points to be warned. Furthermore, the attention unit can also predict points to be warned according to the current situation by referring to past accident data. This allows the attention unit to predict current points to be warned by referring to past accident data. A part or all of the above-mentioned processing in the attention unit may be performed using, for example, AI, or may be performed without using AI. For example, the attention unit can input past accident data to a generation AI, which can predict current points to be warned.

[0054] The attention unit can apply different attention analysis methods for each accident category during attention. For example, in the case of an accident caused by a machine failure, the attention unit analyzes points to be careful about based on the operation data of the machine. In addition, in the case of an accident caused by a human error, the attention unit can analyze points to be careful about based on the work procedure and the work environment. Furthermore, the attention unit can also apply an optimal attention analysis method according to the accident category and identify specific points to be careful about. As a result, the attention unit can identify specific points to be careful about by applying an optimal attention analysis method according to the accident category. Part or all of the above-mentioned processing in the attention unit may be performed, for example, using AI or may be performed without using AI. For example, the attention unit can input accident category data to the generation AI, and the generation AI can apply the optimal attention analysis method.

[0055] The attention unit can analyze changes in attention based on the time of accident occurrence during attention. The attention unit can analyze current attention points based on, for example, recent accidents. The attention unit can also analyze attention points to prevent similar accidents from recurring by referring to accidents that have occurred in the past. Furthermore, the attention unit can also analyze optimal changes in attention points based on the time of accident occurrence. As a result, the attention unit can analyze changes in attention based on the time of accident occurrence, thereby making it possible to provide appropriate attention. Part or all of the above-mentioned processing in the attention unit may be performed using, for example, AI, or may be performed without using AI. For example, the attention unit can input accident occurrence time data to the generation AI, which can analyze changes in attention.

[0056] The warning unit can analyze the warning by referring to the relevant market data of the accident at the time of warning. The warning unit can analyze the current warning points based on the relevant market data, for example. The warning unit can also extract a specific pattern from the market data and analyze the current warning points. Furthermore, the warning unit can also analyze the warning points according to the current situation by referring to the market data. In this way, the warning unit can analyze the warning points according to the current situation by referring to the related market data. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the related market data to the generation AI, and the generation AI can analyze the warning points.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] When collecting detailed information on an accident, the collection unit can analyze the user's past behavioral patterns and select the optimal collection method. For example, if the user has made a detailed report in the past, similar detailed information can be collected. In addition, if the user has made a brief report in the past, the brief information can be preferentially collected. Furthermore, if specific information is lacking from the user's past report history, the information can be collected with a focus. This allows the collection unit to select the optimal collection method by analyzing past behavioral patterns. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral pattern data into the generation AI, which can select the optimal collection method.

[0059] When analyzing detailed information of an accident, the analysis unit can perform filtering based on the user's current work situation and field of interest. For example, the analysis unit preferentially analyzes related accident information according to the user's current work situation. In addition, specific accident information can be filtered and analyzed based on the user's field of interest. Furthermore, the analysis unit can analyze optimal accident information by combining the user's current work situation and field of interest. As a result, the analysis unit can analyze highly relevant information by filtering based on the current work situation and field of interest. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the user's current work situation and field of interest to the generation AI, and the generation AI can perform filtering.

[0060] When collecting accident information, the collection unit can preferentially collect highly relevant information taking into account the geographical location information of the user. For example, the collection unit preferentially collects accident information that occurred in a place close to the user's current location. In addition, highly relevant accident information can be collected based on the user's past movement history. Furthermore, based on the user's geographical location information, it is also possible to preferentially collect accident information that occurred in a specific area. In this way, the collection unit can preferentially collect highly relevant information by taking into account the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs the user's geographical location information to the generation AI, and the generation AI can preferentially collect highly relevant information.

[0061] When analyzing detailed information of an accident, the analysis unit can adjust the level of detail of the analysis based on the importance of the accident. For example, in the case of a serious accident, a detailed analysis is performed to identify the specific cause. In addition, in the case of a minor accident, a concise analysis can be performed to provide a result that covers the main points. Furthermore, the level of detail of the analysis can be adjusted according to the importance of the accident to provide an optimal result. As a result, the analysis unit can provide an optimal analysis result by adjusting the level of detail of the analysis based on the importance of the accident. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the accident to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0062] When identifying the true cause of an accident, the identification unit can select an optimal identification method by analyzing the user's past accident reports. For example, if the user has made a detailed report in the past, a similar detailed identification method can be provided. Also, if the user has made a concise report in the past, a concise identification method can be provided. Furthermore, if specific information is lacking from the user's past report history, the information can be identified with a focus. This allows the identification unit to select an optimal identification method by analyzing past accident reports. A part 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 the user's past accident report data into the generation AI, which can select an optimal identification method.

[0063] When analyzing information on accidents that have occurred in other departments, the attention section can apply different attention analysis methods for each accident category. For example, in the case of an accident caused by a machine failure, attention points can be analyzed based on the operation data of the machine. In addition, in the case of an accident caused by human error, attention points can be analyzed based on the work procedure and the work environment. Furthermore, it is also possible to apply an optimal attention analysis method according to the accident category and identify specific attention points. As a result, the attention section can identify specific attention points by applying an optimal attention analysis method according to the accident category. Part or all of the above-mentioned processing in the attention section may be performed, for example, using AI, or may be performed without using AI. For example, the attention section can input accident category data to the generation AI, and the generation AI can apply the optimal attention analysis method.

[0064] When collecting accident information, the collection unit can analyze the user's social media activity and collect related information. For example, related accident information can be collected based on information shared by the user on social media. In addition, accident information related to a specific field of interest can be collected from the user's social media activity. Furthermore, the user's social media activity can be analyzed to collect optimal accident information. In this way, the collection unit can collect related information by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI, which can collect related information.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The collection unit collects detailed information about the accident. Detailed information about the accident includes, for example, the date and time of the accident, the location, the situation, the people involved, the equipment used, and the work content. The collection unit can collect the information manually or by using an automated sensor. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. Step 3: The identification unit uses the generation AI to identify the true cause of the accident based on the information analyzed by the analysis unit. Identification can identify specific causes such as machine failure or human error. Step 4: The Caution Department uses the generation AI to analyze accident information from other departments and warn employees to prevent similar accidents from occurring in their own department. Caution is given by displaying warning messages, implementing educational programs, etc.

[0067] (Example 2) The accident recurrence prevention system according to the embodiment of the present invention is a system in which detailed information on an accident is collected, analyzed by a generation AI, the true cause is identified, and accident information from other departments is used to warn about the accident. In this system, when an accident occurs, detailed information on the accident is input to the generation AI, which analyzes the information and identifies the true cause of the accident. Furthermore, the generation AI also warns about accidents that occur in other departments at the time of release. For example, when an accident occurs, detailed information on the accident is input to the generation AI. For example, information such as the date and time of the accident, the location, the situation, the people involved, the equipment used, and the work content is input. This information is analyzed by the generation AI. Next, the generation AI analyzes the input information and identifies the true cause of the accident. The generation AI estimates the true cause of the accident based on past accident data and related information. For example, it is possible to identify specific causes such as machine failure and human error. Furthermore, the generation AI also warns about accidents that occur in other departments at the time of release. For example, the generation AI analyzes information on accidents that occur in other departments and warns about accidents that do not occur in the own department. At this time, the generating AI identifies points to watch out for based on past accident data and related information. This system accurately identifies the true cause of the accident and prevents recurrence by utilizing accident information from other departments. For example, if the cause was a machine failure, recurrence can be prevented by strengthening regular inspections and maintenance. Furthermore, if the cause was human error, recurrence can be prevented by strengthening education and training. In this way, the accident recurrence prevention system can accurately identify the true cause of the accident and prevent recurrence by utilizing accident information from other departments.

[0068] The accident recurrence prevention system according to the embodiment includes a collection unit, an analysis unit, an identification unit, and a warning unit. The collection unit collects detailed information about the accident. The detailed information about the accident includes, for example, the date and time of the accident, the location, the situation, the people involved, the equipment used, the work content, and the like, but is not limited to such examples. The collection unit can, for example, collect manually or collect using an automated sensor. The analysis unit uses a generation AI to analyze the information collected by the collection unit. The analysis is performed, for example, by a method such as data mining, statistical analysis, or machine learning algorithm, but is not limited to such examples. For example, the generation AI estimates the true cause of the accident based on past accident data and related information. The identification unit uses the generation AI to identify the true cause of the accident based on the information analyzed by the analysis unit. Identification can identify specific causes, such as machine failure and human error. For example, the generation AI identifies the type and cause of the machine failure. The identification unit can also identify specific causes of human error. The warning unit uses the generation AI to analyze accident information from other departments and urges caution so that similar accidents do not occur in the own department. Caution is given, for example, by displaying a warning message or implementing an educational program, but is not limited to such examples. For example, the generation AI analyzes information on accidents that have occurred in other departments and identifies points to be careful about. As a result, the accident recurrence prevention system according to the embodiment collects and analyzes detailed information on accidents, identifies the true cause, and uses accident information from other departments to urge caution, thereby making it possible to prevent accidents from recurring.

[0069] The collection unit collects detailed information about the accident. Detailed information about the accident includes, for example, the date and time of the accident, the location, the situation, the people involved, the equipment used, the work content, and the like, but is not limited to such examples. The collection unit can, for example, collect information manually or by an automated sensor. Specifically, manual collection is performed through interviews with the people involved and the creation of a report after the accident occurs. The people involved are witnesses to the accident, the parties involved, managers, etc., and detailed testimonies and reports from them are collected. On the other hand, collection by an automated sensor is performed through various sensors and cameras installed at the site. For example, temperature sensors, vibration sensors, pressure sensors, etc. are installed at the work site, and these sensors collect data in real time. In addition, the surveillance camera can record the moment of the accident as a video and use it for later analysis. Furthermore, the collection unit can also collect the operating status of the equipment and abnormality detection data by utilizing an IoT device. This allows the collection unit to collect comprehensive data from various information sources and grasp the detailed situation of the accident. The collected data is stored in a central database and managed so that the analysis unit and the identification unit can access it. This enables the collection unit to efficiently and accurately collect detailed information about an accident, improving the reliability of the entire system.

[0070] The analysis unit uses the generation AI to analyze the information collected by the collection unit. The analysis is performed by, for example, data mining, statistical analysis, machine learning algorithms, and other methods, but is not limited to these examples. Specifically, the generation AI preprocesses the collected data and removes noise and missing values. Next, data mining technology is used to extract useful patterns and trends from the data. For example, based on past accident data, it is possible to identify the frequency of accidents under specific conditions and common factors. In statistical analysis, the collected data is analyzed using statistical methods to evaluate the probability of accidents and the scope of their impact. Machine learning algorithms learn from past data and make predictions and classifications for new data. For example, the generation AI can learn from past accident data and estimate the true cause of an accident from current data. In addition, the generation AI can use natural language processing technology to analyze reports and interview contents of related parties and extract important information. This allows the analysis unit to analyze the collected data from multiple angles and quickly and accurately identify the true cause of an accident and risk factors. The analysis results are provided to the identification unit and the warning unit and are used for further analysis and countermeasure planning.

[0071] The identification unit uses the generation AI to identify the true cause of the accident based on the information analyzed by the analysis unit. Identification can identify specific causes, such as machine failure or human error. Specifically, the generation AI identifies the type and cause of the machine failure based on the data provided by the analysis unit. For example, it can analyze the sensor data of the machine and identify that abnormal vibrations or temperature increases are precursors to failure. The generation AI can also learn past failure data and identify failures with similar patterns. Furthermore, the identification unit can identify the specific cause of human error. For example, if the cause is an incorrect work procedure or non-compliance with safety rules, the generation AI analyzes the reports and interviews of the relevant parties to identify the specific content of the mistake and the circumstances under which it occurred. This allows the identification unit to identify the true cause of the accident in detail, such as machine failure or human error, and to plan specific measures to prevent recurrence. The identified true cause is provided to the attention unit and is used to prevent similar accidents in other departments.

[0072] The attention department uses the generation AI to analyze accident information from other departments and warn them not to cause similar accidents in their own departments. The warning is given, for example, by displaying a warning message or implementing an educational program, but is not limited to such examples. Specifically, the generation AI analyzes information on accidents that have occurred in other departments and identifies points to be careful about. For example, the generation AI evaluates similar risks in the own department based on information on machine failures and human errors that have occurred in other departments. The generation AI can learn past accident data and related information and identify risk factors under specific conditions. Next, the attention department displays a warning message based on the identified risk factors. For example, the generation AI notifies workers of points to be careful about and safety procedures when using a specific machine. It is also possible to implement educational programs and provide training to workers to increase their safety awareness. Furthermore, the attention department can conduct regular safety inspections and risk assessments to detect risk factors early and take measures. As a result, the attention department can utilize accident information from other departments to work to prevent accidents from recurring in its own department. The activities of the attention department contribute to fostering a company-wide safety culture and preventing accidents, and can improve the reliability and safety of the entire system.

[0073] The collection unit can collect information on the date and time of the accident, the location, the situation, the people involved, the equipment used, and the work content. For example, the collection unit collects the date and time of the accident in the form of a timestamp or a date and time. The collection unit can also collect the location of the accident in the form of GPS coordinates, an address, the name of the facility, and the like. Furthermore, the collection unit can collect the situation of the accident in the form of a detailed description, environmental conditions, and the like. For example, the collection unit records the date and time of the accident in the form of a timestamp, and identifies the location of the accident by GPS coordinates. The collection unit can also record the situation of the accident in detail and collect information on the people involved. For example, the collection unit collects information on people involved in the accident and witnesses. The collection unit can also collect information on the type of equipment used, the serial number, the usage status, and the like. For example, the collection unit collects detailed information on the equipment used at the time of the accident and records the work content. As a result, the collection unit can collect detailed information on the accident, enabling accurate analysis. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input detailed information about an accident into the generation AI, which can then analyze the information.

[0074] The analysis unit can estimate the true cause of the accident based on past accident data and related information. For example, the analysis unit acquires past accident data from a database and performs analysis. The past accident data includes, for example, past accident reports and databases, but is not limited to such examples. The analysis unit applies an algorithm for estimating the true cause of the accident based on the past accident data. For example, the analysis unit uses a data mining algorithm to extract patterns from the past accident data and estimate the true cause of the accident. The analysis unit can also use a machine learning algorithm to learn the past accident data and estimate the true cause of the accident. For example, the analysis unit uses the past accident data to identify specific causes such as machine failure and human error. Furthermore, the analysis unit can also estimate the true cause of the accident based on related information. The related information includes, for example, related laws, regulations, technical literature, etc., but is not limited to such examples. For example, the analysis unit refers to related technical literature to estimate the true cause of the accident. As a result, the analysis unit can accurately identify the cause by estimating the true cause based on the past accident data and related information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs past accident data to the generation AI, which can estimate the true cause.

[0075] The identification unit can identify the specific cause of a machine failure or a human error. The identification unit, for example, identifies the type and cause of the machine failure. Machine failures include, for example, wear and breakage of parts, system malfunctions, etc., but are not limited to such examples. The identification unit analyzes the operation data of the machine to identify the cause of the machine failure. For example, the identification unit analyzes the operation log of the machine to identify the cause of the failure. The identification unit can also identify the specific cause of the human error. Human errors include, for example, operation errors and judgment errors, but are not limited to such examples. The identification unit analyzes the work procedure and the work environment to identify the cause of the human error. For example, the identification unit analyzes the record of the work procedure to identify the cause of the error. The identification unit can also analyze the data of the work environment to identify the cause of the error. As a result, the identification unit can identify the specific cause and take appropriate measures. A part 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 machine operation data and records of work procedures into the generation AI, allowing the generation AI to identify the cause.

[0076] The warning unit can analyze information on accidents that have occurred in other departments and call attention so that similar accidents do not occur in the department. For example, the warning unit collects and analyzes information on accidents that have occurred in other departments. Accident information on other departments includes, for example, the date and time of the accident, the location, the situation, the people involved, the equipment used, the work content, and the like, but is not limited to such examples. The warning unit applies an algorithm for identifying points of attention based on the accident information of other departments. For example, the warning unit uses a data mining algorithm to extract patterns from the accident information of other departments and identify points of attention. The warning unit can also use a machine learning algorithm to learn the accident information of other departments and identify points of attention. For example, the warning unit can use the accident information of other departments to call attention so that similar accidents do not occur. Furthermore, the warning unit can call attention by methods such as displaying a warning message or implementing an educational program based on the accident information of other departments. For example, the warning unit analyzes the accident information of other departments, identifies points of attention, and displays a warning message. The warning unit can also implement an educational program based on the accident information of other departments to call attention. This allows the warning unit to prevent recurrence by utilizing accident information from other departments. Some or all of the above-mentioned processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input accident information from other departments to the generation AI, which can then identify points to be careful about.

[0077] The attention unit can specify points to be noted based on past accident data and related information. The attention unit, for example, acquires past accident data from a database and performs analysis. The past accident data includes, for example, past accident reports and databases, but is not limited to such examples. The attention unit applies an algorithm for specifying points to be noted based on past accident data. For example, the attention unit uses a data mining algorithm to extract patterns from past accident data and specify points to be noted. The attention unit can also use a machine learning algorithm to learn past accident data and specify points to be noted. For example, the attention unit uses past accident data to call attention so that a similar accident does not occur. Furthermore, the attention unit can specify points to be noted based on related information. The related information includes, for example, related laws, regulations, technical literature, etc., but is not limited to such examples. For example, the attention unit refers to related technical literature and specifies points to be noted. As a result, the attention unit can specify points to be noted based on past accident data and related information, thereby making it possible to call attention appropriately. A part or all of the above-mentioned processing in the attention unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input past accident data into the generation AI, which can then identify points to watch out for.

[0078] The identification unit can propose measures to strengthen regular inspections and maintenance. The identification unit, for example, proposes the contents and frequency of regular inspections. Regular inspections include, for example, inspection items and inspection intervals, but are not limited to such examples. The identification unit analyzes the operation data of the machine and past inspection records in order to propose the contents of regular inspections. For example, the identification unit analyzes the operation data of the machine and identifies items that require inspection. The identification unit can also analyze past inspection records and propose inspection intervals. Furthermore, the identification unit proposes the contents and methods of maintenance. Maintenance includes, for example, maintenance procedures and tools and materials to be used, but are not limited to such examples. The identification unit analyzes the operation data of the machine and past maintenance records in order to propose the contents of maintenance. For example, the identification unit analyzes the operation data of the machine and identifies items that require maintenance. The identification unit can also analyze past maintenance records and propose maintenance procedures. As a result, the identification unit can prevent recurrence by strengthening regular inspections and maintenance. A part 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 machine operation data and past inspection records into the generation AI, which can then suggest the contents of inspection and maintenance.

[0079] The identification unit can propose measures to strengthen education and training. The identification unit, for example, proposes the contents and implementation method of an education program. The education program includes, for example, the contents and implementation method of education, but is not limited to such an example. The identification unit analyzes past accident data and related information in order to propose the contents of the education program. For example, the identification unit analyzes past accident data and identifies items that require education. The identification unit can also propose the contents of the education program based on the related information. Furthermore, the identification unit proposes the contents and method of training. The training includes, for example, a training scenario and implementation method, but is not limited to such an example. The identification unit analyzes past accident data and related information in order to propose the contents of training. For example, the identification unit analyzes past accident data and identifies items that require training. The identification unit can also propose a training scenario based on the related information. As a result, the identification unit can prevent the recurrence of human error by strengthening education and training. A part 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 and related information into the generation AI, which can then suggest content for education and training.

[0080] The collection unit can estimate the user's emotions and adjust the timing of collecting accident information based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit delays the collection timing so that the user can provide information in a relaxed state. In addition, when the user is relaxed, the collection unit can immediately collect accident information and obtain detailed information. Furthermore, when the user is in a hurry, the collection unit can preferentially collect concise information and complement the details later. This allows the collection unit to adjust the collection timing according to the user's emotions, thereby enabling more appropriate information collection. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI, and the generation AI can adjust the collection timing.

[0081] When collecting accident information, the collection unit can analyze the user's past accident report history and select the optimal collection method. For example, if the user has made a detailed report in the past, the collection unit collects similar detailed information. In addition, if the user has made a brief report in the past, the collection unit can preferentially collect brief information. Furthermore, if specific information is lacking from the user's past report history, the collection unit can also focus on collecting that information. This allows the collection unit to select the optimal collection method by analyzing the past report history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past accident report history to the generation AI, and the generation AI can select the optimal collection method.

[0082] The collection unit can perform filtering based on the user's current work situation and field of interest when collecting accident information. For example, the collection unit preferentially collects related accident information according to the user's current work situation. The collection unit can also filter and collect specific accident information based on the user's field of interest. Furthermore, the collection unit can combine the user's current work situation and field of interest to collect optimal accident information. This allows the collection unit to collect highly relevant information by filtering based on the current work situation and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's current work situation and field of interest to the generation AI, and the generation AI can perform filtering.

[0083] The collection unit can estimate the user's emotion and determine the priority of the accident information to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit can collect important information with priority and collect detailed information later. Also, when the user is relaxed, the collection unit can collect detailed information with priority. Furthermore, when the user is in a hurry, the collection unit can also collect brief information with priority and complement the details later. In this way, the collection unit can collect important information with priority by determining the priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority.

[0084] When collecting accident information, the collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user. For example, the collection unit preferentially collects accident information that occurred in a place close to the user's current location. In addition, the collection unit can collect highly relevant accident information based on the user's past movement history. Furthermore, the collection unit can also preferentially collect accident information that occurred in a specific area based on the user's geographical location information. In this way, the collection unit can preferentially collect highly relevant information by considering the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's geographical location information to the generation AI, and the generation AI can preferentially collect highly relevant information.

[0085] The collection unit can analyze the user's social media activity and collect related information when collecting the accident information. The collection unit can collect related accident information based on, for example, information shared by the user on social media. The collection unit can also collect accident information related to a specific field of interest from the user's social media activity. Furthermore, the collection unit can analyze the user's social media activity and collect optimal accident information. In this way, the collection unit can collect related information by analyzing the social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI, and the generation AI can collect related information.

[0086] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. In addition, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can also provide an analysis result that covers the main points. In this way, the analysis unit can provide a more appropriate analysis result by adjusting the expression method of the analysis according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit inputs the user's emotion data into the generation AI, and the generation AI can adjust the expression method of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the accident during the analysis. For example, in the case of a serious accident, the analysis unit performs a detailed analysis to identify the specific cause. In addition, in the case of a minor accident, the analysis unit can perform a concise analysis to provide a result that covers the main points. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance of the accident to provide an optimal result. As a result, the analysis unit can provide an optimal analysis result by adjusting the level of detail of the analysis based on the importance of the accident. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the accident to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the accident category during analysis. For example, in the case of an accident caused by a machine failure, the analysis unit performs analysis based on the operation data of the machine. In addition, in the case of an accident caused by human error, the analysis unit can perform analysis based on the work procedure and the work environment. Furthermore, the analysis unit can apply an optimal analysis algorithm depending on the accident category to identify the specific cause. As a result, the analysis unit can identify the specific cause by applying an optimal analysis algorithm depending on the accident category. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input accident category data to the generation AI, and the generation AI can apply the optimal analysis algorithm.

[0089] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. In addition, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is excited, the analysis unit can also provide an analysis result with a visually stimulating effect. In this way, the analysis unit can provide a more appropriate analysis result by adjusting the length of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI, and the generation AI can adjust the length of the analysis.

[0090] The analysis unit can determine the priority of the analysis based on the time of occurrence of the accident during the analysis. For example, the analysis unit prioritizes the analysis of the accident that occurred recently and quickly identifies the cause. The analysis unit can also refer to the accidents that occurred in the past and analyze to prevent similar accidents from recurring. Furthermore, the analysis unit can also determine the optimal priority of the analysis based on the time of the accident. As a result, the analysis unit can quickly identify the cause by determining the priority of the analysis based on the time of the accident. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data on the time of the accident occurrence to the generation AI, and the generation AI can determine the priority of the analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the accidents during analysis. For example, the analysis unit prioritizes the analysis of highly relevant accidents and quickly identifies the cause. The analysis unit can also postpone the analysis of less relevant accidents and prioritize the analysis of important accidents. Furthermore, the analysis unit can also adjust the optimal order of analysis based on the relevance of the accidents. As a result, the analysis unit can prioritize the analysis of important accidents by adjusting the order of analysis based on the relevance of the accidents. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input accident relevance data to the generation AI, and the generation AI can adjust the order of analysis.

[0092] The identification unit can estimate the user's emotion and adjust the identification method based on the estimated user's emotion. For example, when the user is nervous, the identification unit provides a simple and highly visible identification method. Furthermore, when the user is relaxed, the identification unit can provide a detailed identification method. Furthermore, when the user is in a hurry, the identification unit can also provide a key point identification method. In this way, the identification unit can provide a more appropriate identification method by adjusting the identification method according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the user's emotion data to the generation AI, and the generation AI can adjust the identification method.

[0093] At the time of identification, the identification unit can select an optimal identification method by analyzing the user's past accident reports. For example, if the user has made a detailed report in the past, the identification unit can provide a similar detailed identification method. In addition, if the user has made a brief report in the past, the identification unit can provide a brief identification method. Furthermore, if specific information is lacking from the user's past report history, the identification unit can also focus on identifying that information. This allows the identification unit to select an optimal identification method by analyzing past accident reports. A part 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 the user's past accident report data into the generation AI, and the generation AI can select an optimal identification method.

[0094] The identification unit can customize the specific means based on the user's current work situation at the time of identification. The identification unit provides a related specific means according to the user's current work situation, for example. The identification unit can also customize the specific means based on the user's current work situation. Furthermore, the identification unit can provide an optimal specific means by combining the user's current work situation and the user's field of interest. This allows the identification unit to provide a more appropriate specific means by customizing the specific means based on the current work situation. A part 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 the user's current work situation data to the generation AI, and the generation AI can customize the specific means.

[0095] The identification unit can estimate the user's emotion and determine a specific priority order based on the estimated user's emotion. For example, when the user is stressed, the identification unit can prioritize identifying important information and later identify detailed information. Also, when the user is relaxed, the identification unit can prioritize identifying detailed information. Furthermore, when the user is in a hurry, the identification unit can prioritize identifying concise information and later complete the details. In this way, the identification unit can prioritize identifying important information by determining a specific priority order according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the user's emotion data to the generation AI, and the generation AI can determine a specific priority order.

[0096] The identification unit can select the optimal identification method in consideration of the geographical location information of the user at the time of identification. For example, the identification unit preferentially identifies accident information that occurred in a place close to the user's current location. In addition, the identification unit can identify highly relevant accident information based on the user's past movement history. Furthermore, the identification unit can also preferentially identify accident information that occurred in a specific area based on the user's geographical location information. Thereby, the identification unit can select the optimal identification method by considering the geographical location information. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the user's geographical location information to the generation AI, and the generation AI can select the optimal identification method.

[0097] At the time of identification, the identification unit can analyze the user's social media activity and suggest a specific means. The identification unit can, for example, suggest a related identification means based on information shared by the user on social media. The identification unit can also suggest an identification means related to a specific field of interest from the user's social media activity. Furthermore, the identification unit can analyze the user's social media activity and suggest an optimal identification means. In this way, the identification unit can suggest an optimal identification means by analyzing the social media activity. A part 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 the user's social media activity data to the generation AI, which can then suggest a specific means.

[0098] The attention unit can estimate the user's emotion and adjust the display method of the attention based on the estimated user's emotion. For example, when the user is nervous, the attention unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the attention unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the attention unit can also provide a display method that focuses on the main points. This allows the attention unit to adjust the display method of the attention according to the user's emotion, thereby enabling more appropriate attention. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the attention unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the attention unit can input the user's emotion data to the generation AI, and the generation AI can adjust the display method of the attention.

[0099] The attention unit can predict current points to be warned by referring to past accident data during warning. The attention unit can predict current points to be warned based on, for example, past similar accident data. The attention unit can also extract a specific pattern from past accident data and predict current points to be warned. Furthermore, the attention unit can also predict points to be warned according to the current situation by referring to past accident data. This allows the attention unit to predict current points to be warned by referring to past accident data. A part or all of the above-mentioned processing in the attention unit may be performed using, for example, AI, or may be performed without using AI. For example, the attention unit can input past accident data to a generation AI, which can predict current points to be warned.

[0100] The attention unit can apply different attention analysis methods for each accident category during attention. For example, in the case of an accident caused by a machine failure, the attention unit analyzes points to be careful about based on the operation data of the machine. In addition, in the case of an accident caused by a human error, the attention unit can analyze points to be careful about based on the work procedure and the work environment. Furthermore, the attention unit can also apply an optimal attention analysis method according to the accident category and identify specific points to be careful about. As a result, the attention unit can identify specific points to be careful about by applying an optimal attention analysis method according to the accident category. Part or all of the above-mentioned processing in the attention unit may be performed, for example, using AI or may be performed without using AI. For example, the attention unit can input accident category data to the generation AI, and the generation AI can apply the optimal attention analysis method.

[0101] The attention unit can estimate the user's emotion and adjust the importance of attention based on the estimated user's emotion. For example, when the user is nervous, the attention unit can display important points of attention with priority, and provide detailed information later. In addition, when the user is relaxed, the attention unit can display detailed points of attention with priority. Furthermore, when the user is in a hurry, the attention unit can also display brief points of attention with priority, and complete the details later. Thereby, the attention unit can adjust the importance of attention according to the user's emotion, thereby displaying important points of attention with priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the attention unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the attention unit can input the user's emotion data to the generation AI, and the generation AI can adjust the importance of attention.

[0102] The attention unit can analyze changes in attention based on the time of accident occurrence during attention. The attention unit can analyze current attention points based on, for example, recent accidents. The attention unit can also analyze attention points to prevent similar accidents from recurring by referring to accidents that have occurred in the past. Furthermore, the attention unit can also analyze optimal changes in attention points based on the time of accident occurrence. As a result, the attention unit can analyze changes in attention based on the time of accident occurrence, thereby making it possible to provide appropriate attention. Part or all of the above-mentioned processing in the attention unit may be performed using, for example, AI, or may be performed without using AI. For example, the attention unit can input accident occurrence time data to the generation AI, which can analyze changes in attention.

[0103] The warning unit can analyze the warning by referring to the relevant market data of the accident at the time of warning. The warning unit can analyze the current warning points based on the relevant market data, for example. The warning unit can also extract a specific pattern from the market data and analyze the current warning points. Furthermore, the warning unit can also analyze the warning points according to the current situation by referring to the market data. In this way, the warning unit can analyze the warning points according to the current situation by referring to the related market data. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the related market data to the generation AI, and the generation AI can analyze the warning points.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] When collecting detailed information on an accident, the collection unit can analyze the user's past behavioral patterns and select the optimal collection method. For example, if the user has made a detailed report in the past, similar detailed information can be collected. In addition, if the user has made a brief report in the past, the brief information can be preferentially collected. Furthermore, if specific information is lacking from the user's past report history, the information can be collected with a focus. This allows the collection unit to select the optimal collection method by analyzing past behavioral patterns. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral pattern data into the generation AI, which can select the optimal collection method.

[0106] When analyzing detailed information of an accident, the analysis unit can perform filtering based on the user's current work situation and field of interest. For example, the analysis unit preferentially analyzes related accident information according to the user's current work situation. In addition, specific accident information can be filtered and analyzed based on the user's field of interest. Furthermore, the analysis unit can analyze optimal accident information by combining the user's current work situation and field of interest. As a result, the analysis unit can analyze highly relevant information by filtering based on the current work situation and field of interest. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the user's current work situation and field of interest to the generation AI, and the generation AI can perform filtering.

[0107] When identifying the true cause of the accident, the identification unit can estimate the user's emotion and adjust the identification method based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible identification method can be provided. Also, when the user is relaxed, a detailed identification method can be provided. Furthermore, when the user is in a hurry, a method that focuses on the main points can be provided. In this way, the identification unit can provide a more appropriate identification method by adjusting the identification method according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the identification unit can input the user's emotion data to the generation AI, and the generation AI can adjust the identification method.

[0108] When analyzing information on an accident that occurred in another department, the attention unit can estimate the user's emotions and adjust the display method of the attention based on the estimated user's emotions. For example, when the user is nervous, a simple and highly visible display method can be provided. Also, when the user is relaxed, a display method including detailed information can be provided. Furthermore, when the user is in a hurry, a display method that focuses on the main points can be provided. This allows the attention unit to adjust the display method of the attention according to the user's emotions, thereby enabling more appropriate attention. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the attention unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the attention unit can input the user's emotion data to the generation AI, and the generation AI can adjust the display method of the attention.

[0109] When collecting accident information, the collection unit can preferentially collect highly relevant information taking into account the geographical location information of the user. For example, the collection unit preferentially collects accident information that occurred in a place close to the user's current location. In addition, highly relevant accident information can be collected based on the user's past movement history. Furthermore, based on the user's geographical location information, it is also possible to preferentially collect accident information that occurred in a specific area. In this way, the collection unit can preferentially collect highly relevant information by taking into account the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs the user's geographical location information to the generation AI, and the generation AI can preferentially collect highly relevant information.

[0110] When analyzing detailed information of an accident, the analysis unit can adjust the level of detail of the analysis based on the importance of the accident. For example, in the case of a serious accident, a detailed analysis is performed to identify the specific cause. In addition, in the case of a minor accident, a concise analysis can be performed to provide a result that covers the main points. Furthermore, the level of detail of the analysis can be adjusted according to the importance of the accident to provide an optimal result. As a result, the analysis unit can provide an optimal analysis result by adjusting the level of detail of the analysis based on the importance of the accident. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the accident to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0111] When identifying the true cause of an accident, the identification unit can select an optimal identification method by analyzing the user's past accident reports. For example, if the user has made a detailed report in the past, a similar detailed identification method can be provided. Also, if the user has made a concise report in the past, a concise identification method can be provided. Furthermore, if specific information is lacking from the user's past report history, the information can be identified with a focus. This allows the identification unit to select an optimal identification method by analyzing past accident reports. A part 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 the user's past accident report data into the generation AI, which can select an optimal identification method.

[0112] When analyzing information on accidents that have occurred in other departments, the attention section can apply different attention analysis methods for each accident category. For example, in the case of an accident caused by a machine failure, attention points can be analyzed based on the operation data of the machine. In addition, in the case of an accident caused by human error, attention points can be analyzed based on the work procedure and the work environment. Furthermore, it is also possible to apply an optimal attention analysis method according to the accident category and identify specific attention points. As a result, the attention section can identify specific attention points by applying an optimal attention analysis method according to the accident category. Part or all of the above-mentioned processing in the attention section may be performed, for example, using AI, or may be performed without using AI. For example, the attention section can input accident category data to the generation AI, and the generation AI can apply the optimal attention analysis method.

[0113] When collecting accident information, the collection unit can analyze the user's social media activity and collect related information. For example, related accident information can be collected based on information shared by the user on social media. In addition, accident information related to a specific field of interest can be collected from the user's social media activity. Furthermore, the user's social media activity can be analyzed to collect optimal accident information. In this way, the collection unit can collect related information by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI, which can collect related information.

[0114] When analyzing detailed information of an accident, the analysis unit can estimate the user's emotions and adjust the way of expressing the analysis based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible analysis result can be provided. Also, if the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, an analysis result that covers the main points can be provided. As a result, the analysis unit can provide a more appropriate analysis result by adjusting the way of expressing the analysis according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI, and the generation AI can adjust the way of expressing the analysis.

[0115] The process flow of the second embodiment will be briefly described below.

[0116] Step 1: The collection unit collects detailed information about the accident. The detailed information about the accident includes, for example, the date and time of the accident, the location, the situation, the people involved, the equipment used, the work content, etc. The collection unit can collect the information manually or by using an automated sensor. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. Step 3: The identification unit uses the generation AI to identify the true cause of the accident based on the information analyzed by the analysis unit. Identification can identify specific causes such as machine failure or human error. Step 4: The Caution Department uses the generation AI to analyze accident information from other departments and warn employees to prevent similar accidents from occurring in their own department. Caution is given by displaying warning messages, implementing educational programs, etc.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0119] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements including the above-mentioned collection unit, analysis unit, identification unit, and warning unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects detailed information on the accident using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the true cause of the accident based on the analyzed information. The warning unit is realized, for example, by the control unit 46A of the smart device 14, and analyzes accident information of other departments and calls for attention. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0128] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements including the above-mentioned collection unit, analysis unit, identification unit, and warning unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects detailed information of the accident using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the true cause of the accident based on the analyzed information. The warning unit is realized, for example, by the control unit 46A of the smart glasses 214, and analyzes accident information of other departments and calls for attention. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0147] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements including the above-mentioned collection unit, analysis unit, identification unit, and warning unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects detailed information on the accident using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the true cause of the accident based on the analyzed information. The warning unit is realized, for example, by the control unit 46A of the headset type terminal 314, and analyzes accident information of other departments and calls for attention. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0164] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] Each of the multiple elements including the above-mentioned collection unit, analysis unit, identification unit, and warning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects detailed information on the accident using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the true cause of the accident based on the analyzed information. The warning unit is realized, for example, by the control unit 46A of the robot 414, and analyzes accident information from other departments and calls for attention. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0171] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0175] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0177] 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.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0180] 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.

[0181] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0182] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0186] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0188] (Appendix 1) A collection department that collects detailed information on the accident; an analysis unit that analyzes the information collected by the collection unit; an identification unit that identifies a true cause of the accident based on the information analyzed by the analysis unit; An alert department that analyzes accident information from other departments and issues alerts A system characterized by: (Appendix 2) The collecting unit includes: Collect information on the date and time of the accident, location, circumstances, people involved, equipment used, and work details. 2. The system of claim 1, (Appendix 3) The analysis unit is Estimate the true cause of the accident based on past accident data and related information 2. The system of claim 1, (Appendix 4) The identification unit is Identify specific causes of machine failure or human error 2. The system of claim 1, (Appendix 5) The warning section includes: Analyze information about accidents that have occurred in other departments and raise awareness to prevent similar accidents from occurring in your own department 2. The system of claim 1, (Appendix 6) The warning section includes: Identify points of concern based on past accident data and related information 2. The system of claim 1, (Appendix 7) The identification unit is Propose measures to strengthen regular inspections and maintenance 2. The system of claim 1, (Appendix 8) The identification unit is Propose measures to strengthen education and training 2. The system of claim 1, (Appendix 9) The collecting unit includes: To estimate a user's emotion and adjust the timing of collecting accident information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting accident information, analyze the user's past accident report history and select the optimal collection method. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting accident information, it is filtered based on the user's current work situation and areas of interest. 2. The system of claim 1, (Appendix 12) The collecting unit includes: Estimate the user's emotions and determine the priority of the accident information to be collected based on the estimated user's emotions. 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting accident information, the system prioritizes collecting information that is more relevant by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 14) The collecting unit includes: When collecting accident information, analyze users' social media activity and collect related information. 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 16) The analysis unit is During the analysis, adjust the level of detail based on the severity of the incident 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, different analysis algorithms are applied depending on the accident category. 2. The system of claim 1, (Appendix 18) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, prioritize analysis based on when the accident occurred 2. The system of claim 1, (Appendix 20) The analysis unit is During analysis, adjust the order of analysis based on accident relevance 2. The system of claim 1, (Appendix 21) The identification unit is Inferring a user's emotion and adjusting a particular method based on the inferred user emotion 2. The system of claim 1, (Appendix 22) The identification unit is During identification, the system analyzes the user's past accident reports to select the optimal identification method. 2. The system of claim 1, (Appendix 23) The identification unit is At a given time, customize a specific method based on the user's current work context 2. The system of claim 1, (Appendix 24) The identification unit is Inferring a user's emotions and determining a specific priority based on the estimated user's emotions 2. The system of claim 1, (Appendix 25) The identification unit is When identifying a user, the optimal identification method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 26) The identification unit is Upon identification, analyze the user's social media activity to suggest specific measures 2. The system of claim 1, (Appendix 27) The warning section includes: Inferring user emotions and adjusting attention display based on the estimated user emotions 2. The system of claim 1, (Appendix 28) The warning section includes: When a warning is given, past accident data is referenced to predict the current warning points. 2. The system of claim 1, (Appendix 29) The warning section includes: During attention, different attention analysis methods are applied to different accident categories. 2. The system of claim 1, (Appendix 30) The warning section includes: Infer user emotions and adjust attention importance based on the estimated user emotions 2. The system of claim 1, (Appendix 31) The warning section includes: During attention, analyze changes in attention based on when accidents occur 2. The system of claim 1, (Appendix 32) The warning section includes: When caution is required, refer to the relevant market data of the accident to analyze caution. 2. The system of claim 1, [Explanation of symbols]

[0189] 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 collection department that collects detailed information on the accident; an analysis unit that analyzes the information collected by the collection unit; an identification unit that identifies a true cause of the accident based on the information analyzed by the analysis unit; An alert department that analyzes accident information from other departments and issues alerts A system characterized by:

2. The collecting unit includes: Collect information on the date and time of the accident, location, circumstances, people involved, equipment used, and work details.

2. The system of claim 1 .

3. The analysis unit is Estimate the true cause of the accident based on past accident data and related information 2. The system of claim 1 .

4. The identification unit is Identify specific causes of machine failure or human error 2. The system of claim 1 .

5. The warning section includes: Analyze information about accidents that have occurred in other departments and raise awareness to prevent similar accidents from occurring in your own department 2. The system of claim 1 .

6. The warning section includes: Identify points of concern based on past accident data and related information 2. The system of claim 1 .

7. The identification unit is Propose measures to strengthen regular inspections and maintenance 2. The system of claim 1 .

8. The identification unit is Propose measures to strengthen education and training 2. The system of claim 1 .

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