System

Abstract

To provide a system according to an embodiment, capable of accumulating and analyzing incident information, and improving a prediction accuracy.SOLUTION: A system according to an embodiment comprises a collection unit, an analysis unit, a notice unit, a retrieval unit and an evaluation unit. The collection unit collects incident information. The analysis unit analyzes the incident information collected by the collection unit. The notice unit notifies a user of a prediction result obtained by the analysis unit. The retrieval unit retrieves and analyzes the same type of the incident information. The evaluation unit evaluates whether or not the generation of an event to be predicted can be suppressed.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 accumulate and analyze incident information, leaving room for improvement in the accuracy of prediction and prevention.

[0005] The system according to the embodiment aims to accumulate and analyze incident information and improve prediction accuracy. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a notification unit, a search unit, and an evaluation unit. The collection unit collects incident information. The analysis unit analyzes the incident information collected by the collection unit. The notification unit notifies the prediction result obtained by the analysis unit. The search unit searches and analyzes similar incident information. The evaluation unit evaluates whether the occurrence was prevented according to the prediction. Effect of the Invention

[0007] The system according to the embodiment can accumulate and analyze incident information to improve prediction accuracy. [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 incident prediction system according to the embodiment of the present invention is a system that accumulates, searches, and analyzes incident information between group companies, predicts which group company will have what kind of accident, when, and notifies the company. This system further enables the system to instantly derive the best response to the occurrence status and solution method of the same type of incident, the derived impact, the recurrence prevention measures, the reporting status to the relevant authorities, etc. In addition, information on whether the occurrence was prevented according to the prediction is also accumulated, and is used as a regular event to compete AI and human intelligence. For example, incident information is collected from each group company. The incident information includes the date and time of occurrence, the location of occurrence, the type of accident, the scope of the impact, the solution method, the recurrence prevention measures, the reporting status to the relevant authorities, etc. This information is analyzed by AI to predict which group company will have what kind of accident, when, and not. Next, the prediction result is notified to each group company. The announcement includes the predicted type of accident, the time of occurrence, the location of occurrence, the scope of impact, the response method, etc. This allows each group company to take measures in advance. In addition, the system instantly searches and analyzes the occurrence status and solution methods of similar incidents, the derived effects, measures to prevent recurrence, and the status of reporting to the relevant authorities, and derives the best response. For example, if a similar accident has occurred in the past, the solution methods and measures to prevent recurrence can be used as reference. In addition, information on whether the occurrence was prevented according to the prediction is also accumulated. This allows the prediction accuracy of AI to be evaluated and improved. Furthermore, it will be used as a regular event to compete between AI and human intelligence, and the prediction accuracy will be improved. This system makes it possible to share and predict incident information between group companies, and to prevent accidents from occurring. In addition, by quickly deriving the best response, the impact of accidents can be minimized. This allows the incident prediction system to share and predict incident information between group companies, and to prevent accidents from occurring. In addition, by quickly deriving the best response, the impact of accidents can be minimized.

[0029] The incident prediction system according to the embodiment includes a collection unit, an analysis unit, a notice unit, a search unit, and an evaluation unit. The collection unit collects incident information. The incident information includes, for example, the date and time of occurrence, the location of occurrence, the type of accident, the scope of impact, the solution method, measures to prevent recurrence, and the status of reporting to the relevant authorities, but is not limited to such examples. The collection unit accepts incident information from, for example, each group company. The collection unit can use a manual collection method or an automatic collection method. For example, the collection unit automatically acquires incident information from the system of each group company. The collection unit can also manually input the incident information. The analysis unit analyzes the incident information collected by the collection unit using AI. The analysis unit predicts, for example, which group company will have an accident, when, and what kind of accident will occur, using a machine learning algorithm or deep learning technology. For example, the analysis unit learns past incident data and builds a model to predict future incident occurrences. The notice unit notifies each group company of the prediction results obtained by the analysis unit. The notification unit notifies, for example, the type of predicted accident, the time of occurrence, the location of occurrence, the scope of impact, and the method of response. The notification unit can communicate the prediction result to each group company using email or a notification system. The search unit searches and analyzes similar incident information. The search unit instantly searches and analyzes, for example, the occurrence status and solution method of the same type of incident, the derived impact, the recurrence prevention measures, and the report status to the relevant authorities based on past incident information. The search unit can derive the best response using AI. The evaluation unit evaluates whether the occurrence was suppressed according to the prediction. The evaluation unit evaluates, for example, whether the predicted incident actually occurred and accumulates the result. The evaluation unit can be used as a regular event that competes between AI and human intelligence. Thereby, the incident prediction system according to the embodiment can collect, analyze, notify, search, and evaluate incident information. A part or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without using AI. For example, the evaluation unit compares the predicted results with the actual occurrence and provides feedback to improve the accuracy of the evaluation.

[0030] The collection unit collects incident information. The incident information includes, for example, the date and time of occurrence, the location of occurrence, the type of accident, the scope of the impact, the solution method, measures to prevent recurrence, and the status of reporting to the relevant authorities, but is not limited to such examples. The collection unit accepts incident information from, for example, each group company. The collection unit can use a manual collection method or an automatic collection method. For example, the collection unit can automatically acquire incident information from the system of each group company. The collection unit can also manually input the incident information. Specifically, the collection unit can connect to the system of each group company and automatically acquire the incident information through an API. This allows the collection of incident information to be performed quickly and accurately. In the case of manual collection, the person in charge can input the incident information using a dedicated input form. This input form covers each item of incident information and also has a check function to prevent input errors. In addition, the collection unit can flexibly set the frequency and method of collecting incident information. For example, in the case of automatic collection in real time, it can be set to acquire data at regular time intervals. In the case of manual collection, a reminder function is provided to encourage regular reporting, supporting the person in charge to remember to input information. This enables the collection unit to collect incident information efficiently and effectively, thereby improving the reliability of the entire system.

[0031] The analysis unit uses AI to analyze the incident information collected by the collection unit. For example, the analysis unit uses machine learning algorithms and deep learning technology to predict which group company will have what kind of accident, when, and at what time. For example, the analysis unit learns past incident data and builds a model to predict future incident occurrences. Specifically, the analysis unit uses past incident data to analyze accident occurrence patterns and trends. This allows the risk of incident occurrence under specific conditions to be evaluated and a prediction model to be built. For example, a machine learning algorithm is used to extract features from past data, and a prediction model is trained based on these features. When deep learning technology is used, a neural network is built, and the incident data is used as input to output a prediction result. At this time, the accuracy of the model is evaluated using past data, and the model parameters are adjusted as necessary. Furthermore, the analysis unit can continuously update the prediction model using new incident information collected in real time. This enables highly accurate predictions that reflect the latest information. In addition, the analysis unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. This allows the analysis unit to improve the accuracy of incident predictions and increase the reliability of the entire system.

[0032] The Notification Department notifies each group company of the prediction results obtained by the Analysis Department. The Notification Department notifies, for example, the type of predicted accident, the time of occurrence, the location of occurrence, the scope of impact, and the method of response. The Notification Department can communicate the prediction results to each group company using email or a notification system. Specifically, the Notification Department creates a detailed report based on the prediction results and sends it to the person in charge at each group company. This report includes detailed information on the predicted incident and recommended countermeasures, and supports the person in charge to respond quickly. In addition, the Notification Department has a real-time notification function and can issue an immediate warning in the event of an emergency. For example, if the predicted incident is likely to have a significant impact, the Notification Department sends a push notification to the person in charge's smartphone or computer to encourage a quick response. Furthermore, the Notification Department can manage the history of the prediction results and compare past prediction results with the actual occurrence situation. This makes it possible to evaluate the accuracy and effectiveness of the prediction and use it to improve the system. The Notification Department can strengthen cooperation with each group company and maximize the effectiveness of the incident prediction system.

[0033] The search unit searches and analyzes similar incident information. For example, based on past incident information, the search unit instantly searches and analyzes the occurrence status and solution method of the same type of incident, the derived impact, recurrence prevention measures, and the report status to the relevant authorities. The search unit can use AI to derive the best response. Specifically, the search unit accesses a past incident database and quickly searches for incident information that matches specific conditions. For example, a search is performed by specifying conditions such as a specific type of accident, the location of occurrence, and the impact range, and related incident information is extracted. Furthermore, the search unit can propose optimal countermeasures based on the extracted incident information. For example, the solution methods and recurrence prevention measures when a similar incident occurred in the past are referred to to derive countermeasures appropriate for the current situation. In addition, the search unit has a function to visually display the analysis results of the incident information, supporting the person in charge to easily understand. For example, the frequency of occurrence and the impact range of the incident can be displayed in graphs and charts, allowing visual understanding. This allows the search unit to quickly and accurately search and analyze incident information and provide optimal countermeasures.

[0034] The evaluation department evaluates whether the occurrence was prevented according to the prediction. For example, the evaluation department evaluates whether the predicted incident actually occurred and accumulates the results. The evaluation department can be used as a regular event where AI and human intelligence compete. Specifically, the evaluation department compares the prediction result with the actual occurrence situation and evaluates the accuracy and effectiveness of the prediction. For example, if a predicted incident actually occurs, the evaluation department analyzes in detail the cause of the occurrence, the scope of the impact, and the effectiveness of the countermeasures, and accumulates the evaluation results. This makes it possible to provide feedback to improve the accuracy of the prediction model. In addition, if the prediction result does not match the actual occurrence situation, the evaluation department analyzes the cause and uses it to improve the prediction model. Furthermore, the evaluation department regularly reports the evaluation results and contributes to the improvement of the entire system. For example, by creating a report based on the evaluation results and providing it to the person in charge of each group company, it promotes the improvement of the accuracy of the prediction model and the improvement of the countermeasures. The evaluation department can hold a regular event where AI and human intelligence compete to improve the prediction accuracy. This allows the evaluation department to continuously evaluate the accuracy and effectiveness of the prediction and improve the reliability of the entire system.

[0035] The collection unit can receive incident information from each group company. The collection unit, for example, receives incident information from each group company. The collection unit can use a manual collection method or an automatic collection method. For example, the collection unit automatically acquires incident information from the system of each group company. The collection unit can also manually input incident information. This makes it possible to accept incident information from each group company. 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 use an AI model for automatically acquiring incident information from the system of each group company.

[0036] The analysis unit can analyze the collected incident information using AI and predict what kind of accident will occur at which group company and at a specific time. The analysis unit can analyze the collected incident information using AI and predict what kind of accident will occur at which group company and at a specific time. The analysis unit analyzes the incident information using, for example, a machine learning algorithm or deep learning technology. For example, the analysis unit learns past incident data and builds a model that predicts future incident occurrences. The analysis unit can predict a range such as a date, week, or month as a specific time. For example, the analysis unit predicts incidents that are likely to occur in the next month based on past data. This makes it possible to analyze the incident information and predict accidents. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected incident information to the generation AI and cause the generation AI to execute the prediction of accidents.

[0037] The notification unit can notify each group company of the type of the predicted accident, the time of occurrence, the location of occurrence, the extent of the impact, and the method of response. The notification unit notifies each group company of the type of the predicted accident, the time of occurrence, the location of occurrence, the extent of the impact, and the method of response. The notification unit can communicate the prediction result to each group company, for example, by email or a notification system. The notification unit can notify, for example, a traffic accident, a work accident, etc. as the type of the predicted accident. The time of occurrence can be notified of a range such as a date, a week, or a month. The location of occurrence can be notified of a range such as a building, a region, or a country. The impact range can be notified of the number of people and the region affected. The response method can be notified of an emergency response procedure, a long-term response procedure, etc. Thereby, information on the predicted accident can be notified to each group company. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the prediction result into an AI model and cause the generation AI to execute the optimal notification method.

[0038] The search unit can instantly search and analyze the occurrence status and solution method of the same type of incident, derived effects, measures to prevent recurrence, and the report status to the relevant authorities, and derive the best response. The search unit instantly searches and analyzes the occurrence status and solution method of the same type of incident, derived effects, measures to prevent recurrence, and the report status to the relevant authorities, and derives the best response. For example, based on past incident information, the search unit searches and analyzes the occurrence status and solution method of the same type of incident, derived effects, measures to prevent recurrence, and the report status to the relevant authorities. The search unit can use AI to derive the best response. For example, if a similar accident occurs in the past, the search unit can refer to the solution method and measures to prevent recurrence. The search unit can search for the occurrence status of the incident information, such as the frequency of occurrence and the location of occurrence. As a solution method, it can search for technical solutions and management solutions. As derived effects, it can search for secondary damage and related problems. As measures to prevent recurrence, it can search for preventive measures and improvement measures. As the report status to the relevant authorities, it can search for the timing of the report and the report content. This makes it possible to search and analyze similar incident information and derive the best response. Some or all of the above-mentioned processing in the search unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the search unit may input similar incident information to the generation AI and cause the generation AI to perform search and analysis to derive the best response.

[0039] The evaluation unit accumulates information on whether the occurrence was prevented against the prediction, and can be used as a regular event to compare AI and human intelligence. The evaluation unit accumulates information on whether the occurrence was prevented against the prediction, and can be used as a regular event to compare AI and human intelligence. The evaluation unit, for example, evaluates whether the predicted incident actually occurred and accumulates the result. The evaluation unit can be used as a regular event to compete between AI and human intelligence. For example, the evaluation unit compares the prediction result of the AI ​​with the prediction result of a human and evaluates the accuracy. The evaluation unit can set the frequency and evaluation criteria of the regular event and perform evaluation periodically. Thereby, information on whether the occurrence was prevented against the prediction is accumulated, and can be used as a regular event to compete between AI and human intelligence. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit compares the prediction result with the actual occurrence situation and provides feedback to improve the accuracy of the evaluation.

[0040] The collection unit can analyze the past incident information of each group company and select the optimal collection method. The collection unit analyzes the past incident information of each group company and selects the optimal collection method. The collection unit, for example, identifies a pattern of frequently occurring accidents from the past incident information and selects a collection method based on the pattern. The collection unit can analyze the past response history of each group company and select the most effective collection method. The collection unit can evaluate the collection method of the past incident information and select the optimal method. This makes it possible to analyze the past incident information and select the optimal collection method. 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 past incident information to the generation AI and cause the generation AI to select the optimal collection method.

[0041] The collection unit can filter the incident information based on the current business status and risk level of each group company when collecting the incident information. The collection unit can filter the incident information based on the current business status and risk level of each group company when collecting the incident information. For example, the collection unit acquires the current business status of each group company in real time and preferentially collects incident information related to high-risk business. The collection unit can evaluate the risk level of each group company and filter incident information related to high-risk business. The collection unit can comprehensively judge the business status and risk level of each group company and collect optimal incident information. This makes it possible to filter the incident information based on the business status and risk level. A 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 business status data of each group company to the generation AI and cause the generation AI to execute filtering.

[0042] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of each group company when collecting incident information. The collection unit preferentially collects highly relevant information in consideration of the geographical location information of each group company when collecting incident information. For example, the collection unit preferentially collects incident information occurring in the vicinity based on the geographical location information of each group company. The collection unit can preferentially collect incident information in a highly geographically relevant area. The collection unit can preferentially collect incident information having a wide range of influence in consideration of the geographical location information of each group company. This makes it possible to preferentially collect highly relevant information in consideration of the geographical location information. 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 geographical location information of each group company to the generation AI and cause the generation AI to execute collection of highly relevant information.

[0043] The collection unit can analyze the social media activity of each group company when collecting incident information, and collect related information. The collection unit can analyze the social media activity of each group company when collecting incident information, and collect related information. The collection unit, for example, monitors the social media activity of each group company and collects posts related to the incident. The collection unit can analyze discussions about the incident on social media and collect related information. The collection unit can analyze the social media activity of each group company, grasp trends related to the incident, and collect information. This makes it possible to analyze social media activity and collect related information. 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 social media activity data to the generation AI and cause the generation AI to execute collection of related information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the incident information during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the incident information during the analysis. The analysis unit, for example, performs a detailed analysis on incident information with a high level of importance. The analysis unit can perform a simplified analysis on incident information with a low level of importance. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the incident information. This makes it possible to adjust the level of detail of the analysis based on the importance of the incident information. 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 importance data of the incident information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the incident during analysis. The analysis unit can apply different analysis algorithms depending on the category of the incident during analysis. The analysis unit can apply a specific analysis algorithm to a safety accident, for example. The analysis unit can apply another analysis algorithm to an environmental accident. The analysis unit can select and apply an optimal analysis algorithm depending on the category of the incident. This makes it possible to apply different analysis algorithms depending on the category of the incident. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input incident category data to the generation AI and cause the generation AI to select and apply an optimal analysis algorithm.

[0046] The analysis unit can determine the priority of the analysis based on the occurrence time of the incident information during the analysis. The analysis unit determines the priority of the analysis based on the occurrence time of the incident information during the analysis. For example, the analysis unit prioritizes the analysis of recently occurring incident information. The analysis unit can postpone the analysis of incident information that occurred earlier. The analysis unit can gradually adjust the priority of the analysis based on the occurrence time of the incident information. This makes it possible to determine the priority of the analysis based on the occurrence time of the incident information. 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 occurrence time data of the incident information to the generation AI and cause the generation AI to determine the priority of the analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the incident information during analysis. The analysis unit adjusts the order of analysis based on the relevance of the incident information during analysis. For example, the analysis unit preferentially analyzes highly relevant incident information. The analysis unit can analyze less relevant incident information later. The analysis unit can adjust the order of analysis stepwise based on the relevance of the incident information. This makes it possible to adjust the order of analysis based on the relevance of the incident information. 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 relevance data of the incident information to the generation AI and cause the generation AI to adjust the order of analysis.

[0048] The notification unit can adjust the level of detail of the notification based on the importance of the predicted accident at the time of notification. The notification unit adjusts the level of detail of the notification based on the importance of the predicted accident at the time of notification. For example, the notification unit issues a detailed notification for an accident with a high level of importance. The notification unit can issue a simplified notification for an accident with a low level of importance. The notification unit can gradually adjust the level of detail of the notification according to the importance of the predicted accident. This makes it possible to adjust the level of detail of the notification based on the importance of the predicted accident. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the importance data of the predicted accident to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0049] The notification unit can apply different notification algorithms depending on the category of the predicted accident at the time of notification. The notification unit applies different notification algorithms depending on the category of the predicted accident at the time of notification. The notification unit applies a specific notification algorithm to a safety accident, for example. The notification unit can apply another notification algorithm to an environmental accident. The notification unit can select and apply an optimal notification algorithm depending on the category of the predicted accident. This makes it possible to apply different notification algorithms depending on the category of the predicted accident. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input category data of the predicted accident to the generation AI and cause the generation AI to select and apply the optimal notification algorithm.

[0050] The notification unit can determine the priority of the notification based on the predicted time of occurrence of the accident at the time of notification. The notification unit determines the priority of the notification based on the predicted time of occurrence of the accident at the time of notification. For example, when the predicted time of occurrence of the accident is near, the notification unit prioritizes the notification. The notification unit can postpone the notification of the accident that is far away. The notification unit can gradually adjust the priority of the notification based on the predicted time of occurrence of the accident. This makes it possible to determine the priority of the notification based on the predicted time of occurrence of the accident. Part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the predicted time of occurrence data of the accident to the generation AI and cause the generation AI to execute the determination of the priority of the notification.

[0051] The notification unit can adjust the order of notification based on the predicted relevance of the accidents at the time of notification. The notification unit adjusts the order of notification based on the predicted relevance of the accidents at the time of notification. For example, the notification unit prioritizes the notification of highly relevant accidents. The notification unit can notify less relevant accidents at a later date. The notification unit can gradually adjust the order of notification based on the predicted relevance of the accidents. This makes it possible to adjust the order of notification based on the predicted relevance of the accidents. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the relevance data of the predicted accidents to the generation AI and cause the generation AI to adjust the order of notification.

[0052] The search unit can improve the accuracy of the search by considering the interrelationships of the incident information at the time of the search. The search unit can improve the accuracy of the search by considering the interrelationships of the incident information at the time of the search. For example, the search unit analyzes the interrelationships of the incident information and preferentially displays information with high relevance. The search unit can improve the accuracy of the search result by considering the interrelationships of the incident information. The search unit can provide an optimal search result based on the interrelationships of the incident information. This makes it possible to improve the accuracy of the search by considering the interrelationships of the incident information. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI or may be performed without using AI. For example, the search unit can input interrelationship data of the incident information to the generation AI and cause the generation AI to execute the improvement of the search accuracy.

[0053] The search unit can perform a search while taking into consideration the attribute information of the submitter of the incident information at the time of searching. The search unit performs a search while taking into consideration the attribute information of the submitter of the incident information at the time of searching. The search unit preferentially displays highly relevant information based on, for example, the attribute information of the submitter of the incident information. The search unit can improve the accuracy of the search result by taking into consideration the attribute information of the submitter. The search unit can provide optimal search results based on the attribute information of the submitter. This allows a search to be performed while taking into consideration the attribute information of the submitter. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input attribute information data of the submitter to the generation AI and cause the generation AI to execute a search.

[0054] The search unit can perform a search taking into consideration the geographical distribution of the incident information at the time of a search. The search unit performs a search taking into consideration the geographical distribution of the incident information at the time of a search. The search unit preferentially displays highly relevant information based on the geographical distribution of the incident information, for example. The search unit can improve the accuracy of the search results by taking into consideration the geographical distribution. The search unit can provide optimal search results based on the geographical distribution. This makes it possible to perform a search taking into consideration the geographical distribution. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input geographical distribution data of the incident information to the generation AI and cause the generation AI to execute a search.

[0055] The search unit can improve the accuracy of the search by referring to the literature related to the incident information at the time of the search. The search unit can improve the accuracy of the search by referring to the literature related to the incident information at the time of the search. For example, the search unit preferentially displays information with high relevance based on the literature related to the incident information. The search unit can improve the accuracy of the search result by referring to the related literature. The search unit can provide an optimal search result based on the related literature. This can improve the accuracy of the search by referring to the related literature. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the literature data related to the incident information to the generation AI and cause the generation AI to execute the search.

[0056] The evaluation unit can improve the accuracy of the evaluation by analyzing the actual occurrence situation with respect to the prediction at the time of evaluation. The evaluation unit can improve the accuracy of the evaluation by analyzing the actual occurrence situation with respect to the prediction at the time of evaluation. The evaluation unit, for example, compares the actual occurrence situation with the prediction and improves the accuracy of the evaluation. The evaluation unit can analyze the actual occurrence situation and evaluate the accuracy of the prediction. The evaluation unit can analyze the difference between the prediction and the actual occurrence situation and improve the accuracy of the evaluation. Thereby, the accuracy of the evaluation can be improved by analyzing the actual occurrence situation with respect to the prediction. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI. For example, the evaluation unit can input the prediction result and actual occurrence situation data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the evaluation.

[0057] The evaluation unit can perform the evaluation while taking into consideration the attribute information of each group company at the time of evaluation. The evaluation unit performs the evaluation while taking into consideration the attribute information of each group company at the time of evaluation. The evaluation unit improves the accuracy of the evaluation, for example, based on the attribute information of each group company. The evaluation unit can adjust the evaluation method while taking into consideration the attribute information. The evaluation unit can provide an optimal evaluation method based on the attribute information of each group company. This allows the evaluation to be performed while taking into consideration the attribute information of each group company. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input attribute information data of each group company to the generation AI and cause the generation AI to perform the evaluation.

[0058] The evaluation unit can perform the evaluation taking into consideration the geographical distribution of the incident information at the time of evaluation. The evaluation unit performs the evaluation taking into consideration the geographical distribution of the incident information at the time of evaluation. The evaluation unit improves the accuracy of the evaluation, for example, based on the geographical distribution of the incident information. The evaluation unit can adjust the evaluation method taking into consideration the geographical distribution. The evaluation unit can provide an optimal evaluation method based on the geographical distribution. This makes it possible to perform the evaluation taking into consideration the geographical distribution. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input geographical distribution data of the incident information to the generation AI and cause the generation AI to perform the evaluation.

[0059] The evaluation unit can improve the accuracy of the evaluation by referring to the related literature of the incident information at the time of evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to the related literature of the incident information at the time of evaluation. The evaluation unit can improve the accuracy of the evaluation, for example, based on the related literature of the incident information. The evaluation unit can adjust the evaluation method by referring to the related literature. The evaluation unit can provide an optimal evaluation method based on the related literature. This can improve the accuracy of the evaluation by referring to the related literature. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the related literature data of the incident information to the generation AI and cause the generation AI to execute the evaluation.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The incident prediction system can further evaluate the skill level of employees of each group company and optimize the division of roles for responding to incidents based on the skill level. For example, the collection unit collects skill data of each employee, and the analysis unit proposes optimal division of roles based on the skill data. The notification unit notifies each employee of the results of the division of roles, and the search unit can propose a training program for improving skills based on past incident response history. The evaluation unit can evaluate the effectiveness of the division of roles and provide feedback to promote improvement of skill levels.

[0062] The collection unit can monitor the health status of employees of each group company and adjust the timing of collecting incident information based on the health status. For example, if an employee is tired, the collection timing can be delayed to reduce the burden on the employee. If the employee is healthy, incident information can be collected immediately to encourage a quick response. This makes it possible to adjust the timing of collecting incident information based on the employee's health status.

[0063] When analyzing incident information, the analysis department can adjust the analysis algorithm taking into account the culture and business processes of each group company. For example, a specific analysis algorithm can be applied to group companies with different cultural backgrounds. If the business processes are different, a different analysis algorithm can be applied. This makes it possible to provide optimal analysis results according to culture and business processes.

[0064] The notification department can determine the priority of notifications based on the predicted scope of impact of the accident. For example, priority is given to notifications of accidents with a wide scope of impact. Accidents with a narrow scope of impact can be notified later. This makes it possible to adjust the priority of notifications based on the scope of impact.

[0065] When searching for incident information, the search unit can adjust the search results taking into account the workload of each group company. For example, important information can be displayed preferentially for group companies with a high workload. When the workload is low, detailed information can be provided. This makes it possible to adjust the search results based on the workload.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The collection department collects incident information. Incident information includes the date and time of occurrence, the location of occurrence, the type of accident, the scope of impact, the solution, measures to prevent recurrence, and the status of reporting to the relevant authorities. The collection department accepts incident information from each group company. The collection department can use manual or automatic collection methods. For example, incident information is automatically acquired from the system of each group company. Incident information can also be entered manually. Step 2: The analysis department uses AI to analyze the incident information collected by the collection department. Using machine learning algorithms and deep learning technology, the analysis department predicts which group company will experience an accident, when, and what type of accident will occur. It learns from past incident data and builds a model to predict future incidents. Step 3: The Notification Department announces the prediction results obtained by the Analysis Department to each group company. The Notification Department announces the predicted type of accident, the time of occurrence, the location of occurrence, the scope of impact, how to respond, etc. The prediction results can be communicated to each group company using email or a notification system. Step 4: The search unit searches and analyzes similar incident information. Based on past incident information, the search unit instantly searches and analyzes the occurrence status and resolution methods of similar incidents, their derived effects, measures to prevent recurrence, and the status of reporting to relevant authorities. Using AI, it is possible to derive the best response. Step 5: The evaluation unit evaluates whether the predicted incidents were prevented from occurring. The evaluation unit evaluates whether the predicted incidents actually occurred and accumulates the results. The evaluation unit compares the prediction results with the actual occurrences and provides feedback to improve the accuracy of the evaluation.

[0068] (Example 2) The incident prediction system according to the embodiment of the present invention is a system that accumulates, searches, and analyzes incident information between group companies, predicts which group company will have what kind of accident, when, and notifies the company. This system further enables the system to instantly derive the best response to the occurrence status and solution method of the same type of incident, the derived impact, the recurrence prevention measures, the reporting status to the relevant authorities, etc. In addition, information on whether the occurrence was prevented according to the prediction is also accumulated, and is used as a regular event to compete AI and human intelligence. For example, incident information is collected from each group company. The incident information includes the date and time of occurrence, the location of occurrence, the type of accident, the scope of the impact, the solution method, the recurrence prevention measures, the reporting status to the relevant authorities, etc. This information is analyzed by AI to predict which group company will have what kind of accident, when, and not. Next, the prediction result is notified to each group company. The announcement includes the predicted type of accident, the time of occurrence, the location of occurrence, the scope of impact, the response method, etc. This allows each group company to take measures in advance. In addition, the system instantly searches and analyzes the occurrence status and solution methods of similar incidents, the derived effects, measures to prevent recurrence, and the status of reporting to the relevant authorities, and derives the best response. For example, if a similar accident has occurred in the past, the solution methods and measures to prevent recurrence can be used as reference. In addition, information on whether the occurrence was prevented according to the prediction is also accumulated. This allows the prediction accuracy of AI to be evaluated and improved. Furthermore, it will be used as a regular event to compete between AI and human intelligence, and the prediction accuracy will be improved. This system makes it possible to share and predict incident information between group companies, and to prevent accidents from occurring. In addition, by quickly deriving the best response, the impact of accidents can be minimized. This allows the incident prediction system to share and predict incident information between group companies, and to prevent accidents from occurring. In addition, by quickly deriving the best response, the impact of accidents can be minimized.

[0069] The incident prediction system according to the embodiment includes a collection unit, an analysis unit, a notice unit, a search unit, and an evaluation unit. The collection unit collects incident information. The incident information includes, for example, the date and time of occurrence, the location of occurrence, the type of accident, the scope of impact, the solution method, measures to prevent recurrence, and the status of reporting to the relevant authorities, but is not limited to such examples. The collection unit accepts incident information from, for example, each group company. The collection unit can use a manual collection method or an automatic collection method. For example, the collection unit automatically acquires incident information from the system of each group company. The collection unit can also manually input the incident information. The analysis unit analyzes the incident information collected by the collection unit using AI. The analysis unit predicts, for example, which group company will have an accident, when, and what kind of accident will occur, using a machine learning algorithm or deep learning technology. For example, the analysis unit learns past incident data and builds a model to predict future incident occurrences. The notice unit notifies each group company of the prediction results obtained by the analysis unit. The notification unit notifies, for example, the type of predicted accident, the time of occurrence, the location of occurrence, the scope of impact, and the method of response. The notification unit can communicate the prediction result to each group company using email or a notification system. The search unit searches and analyzes similar incident information. The search unit instantly searches and analyzes, for example, the occurrence status and solution method of the same type of incident, the derived impact, the recurrence prevention measures, and the report status to the relevant authorities based on past incident information. The search unit can derive the best response using AI. The evaluation unit evaluates whether the occurrence was suppressed according to the prediction. The evaluation unit evaluates, for example, whether the predicted incident actually occurred and accumulates the result. The evaluation unit can be used as a regular event that competes between AI and human intelligence. Thereby, the incident prediction system according to the embodiment can collect, analyze, notify, search, and evaluate incident information. A part or all of the above-mentioned processing in the evaluation unit may be performed using AI, or may be performed without using AI. For example, the evaluation unit compares the predicted results with the actual occurrence and provides feedback to improve the accuracy of the evaluation.

[0070] The collection unit collects incident information. The incident information includes, for example, the date and time of occurrence, the location of occurrence, the type of accident, the scope of the impact, the solution method, measures to prevent recurrence, and the status of reporting to the relevant authorities, but is not limited to such examples. The collection unit accepts incident information from, for example, each group company. The collection unit can use a manual collection method or an automatic collection method. For example, the collection unit can automatically acquire incident information from the system of each group company. The collection unit can also manually input the incident information. Specifically, the collection unit can connect to the system of each group company and automatically acquire the incident information through an API. This allows the collection of incident information to be performed quickly and accurately. In the case of manual collection, the person in charge can input the incident information using a dedicated input form. This input form covers each item of incident information and also has a check function to prevent input errors. In addition, the collection unit can flexibly set the frequency and method of collecting incident information. For example, in the case of automatic collection in real time, it can be set to acquire data at regular time intervals. In the case of manual collection, a reminder function is provided to encourage regular reporting, supporting the person in charge to remember to input information. This enables the collection unit to collect incident information efficiently and effectively, thereby improving the reliability of the entire system.

[0071] The analysis unit uses AI to analyze the incident information collected by the collection unit. For example, the analysis unit uses machine learning algorithms and deep learning technology to predict which group company will have what kind of accident, when, and at what time. For example, the analysis unit learns past incident data and builds a model to predict future incident occurrences. Specifically, the analysis unit uses past incident data to analyze accident occurrence patterns and trends. This allows the risk of incident occurrence under specific conditions to be evaluated and a prediction model to be built. For example, a machine learning algorithm is used to extract features from past data, and a prediction model is trained based on these features. When deep learning technology is used, a neural network is built, and the incident data is used as input to output a prediction result. At this time, the accuracy of the model is evaluated using past data, and the model parameters are adjusted as necessary. Furthermore, the analysis unit can continuously update the prediction model using new incident information collected in real time. This enables highly accurate predictions that reflect the latest information. In addition, the analysis unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. This allows the analysis unit to improve the accuracy of incident predictions and increase the reliability of the entire system.

[0072] The Notification Department notifies each group company of the prediction results obtained by the Analysis Department. The Notification Department notifies, for example, the type of predicted accident, the time of occurrence, the location of occurrence, the scope of impact, and the method of response. The Notification Department can communicate the prediction results to each group company using email or a notification system. Specifically, the Notification Department creates a detailed report based on the prediction results and sends it to the person in charge at each group company. This report includes detailed information on the predicted incident and recommended countermeasures, and supports the person in charge to respond quickly. In addition, the Notification Department has a real-time notification function and can issue an immediate warning in the event of an emergency. For example, if the predicted incident is likely to have a significant impact, the Notification Department sends a push notification to the person in charge's smartphone or computer to encourage a quick response. Furthermore, the Notification Department can manage the history of the prediction results and compare past prediction results with the actual occurrence situation. This makes it possible to evaluate the accuracy and effectiveness of the prediction and use it to improve the system. The Notification Department can strengthen cooperation with each group company and maximize the effectiveness of the incident prediction system.

[0073] The search unit searches and analyzes similar incident information. For example, based on past incident information, the search unit instantly searches and analyzes the occurrence status and solution method of the same type of incident, the derived impact, recurrence prevention measures, and the report status to the relevant authorities. The search unit can use AI to derive the best response. Specifically, the search unit accesses a past incident database and quickly searches for incident information that matches specific conditions. For example, a search is performed by specifying conditions such as a specific type of accident, the location of occurrence, and the impact range, and related incident information is extracted. Furthermore, the search unit can propose optimal countermeasures based on the extracted incident information. For example, the solution methods and recurrence prevention measures when a similar incident occurred in the past are referred to to derive countermeasures appropriate for the current situation. In addition, the search unit has a function to visually display the analysis results of the incident information, supporting the person in charge to easily understand. For example, the frequency of occurrence and the impact range of the incident can be displayed in graphs and charts, allowing visual understanding. This allows the search unit to quickly and accurately search and analyze incident information and provide optimal countermeasures.

[0074] The evaluation department evaluates whether the occurrence was prevented according to the prediction. For example, the evaluation department evaluates whether the predicted incident actually occurred and accumulates the results. The evaluation department can be used as a regular event where AI and human intelligence compete. Specifically, the evaluation department compares the prediction result with the actual occurrence situation and evaluates the accuracy and effectiveness of the prediction. For example, if a predicted incident actually occurs, the evaluation department analyzes in detail the cause of the occurrence, the scope of the impact, and the effectiveness of the countermeasures, and accumulates the evaluation results. This makes it possible to provide feedback to improve the accuracy of the prediction model. In addition, if the prediction result does not match the actual occurrence situation, the evaluation department analyzes the cause and uses it to improve the prediction model. Furthermore, the evaluation department regularly reports the evaluation results and contributes to the improvement of the entire system. For example, by creating a report based on the evaluation results and providing it to the person in charge of each group company, it promotes the improvement of the accuracy of the prediction model and the improvement of the countermeasures. The evaluation department can hold a regular event where AI and human intelligence compete to improve the prediction accuracy. This allows the evaluation department to continuously evaluate the accuracy and effectiveness of the prediction and improve the reliability of the entire system.

[0075] The collection unit can receive incident information from each group company. The collection unit, for example, receives incident information from each group company. The collection unit can use a manual collection method or an automatic collection method. For example, the collection unit automatically acquires incident information from the system of each group company. The collection unit can also manually input incident information. This makes it possible to accept incident information from each group company. 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 use an AI model for automatically acquiring incident information from the system of each group company.

[0076] The analysis unit can analyze the collected incident information using AI and predict what kind of accident will occur at which group company and at a specific time. The analysis unit can analyze the collected incident information using AI and predict what kind of accident will occur at which group company and at a specific time. The analysis unit analyzes the incident information using, for example, a machine learning algorithm or deep learning technology. For example, the analysis unit learns past incident data and builds a model that predicts future incident occurrences. The analysis unit can predict a range such as a date, week, or month as a specific time. For example, the analysis unit predicts incidents that are likely to occur in the next month based on past data. This makes it possible to analyze the incident information and predict accidents. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected incident information to the generation AI and cause the generation AI to execute the prediction of accidents.

[0077] The notification unit can notify each group company of the type of the predicted accident, the time of occurrence, the location of occurrence, the extent of the impact, and the method of response. The notification unit notifies each group company of the type of the predicted accident, the time of occurrence, the location of occurrence, the extent of the impact, and the method of response. The notification unit can communicate the prediction result to each group company, for example, by email or a notification system. The notification unit can notify, for example, a traffic accident, a work accident, etc. as the type of the predicted accident. The time of occurrence can be notified of a range such as a date, a week, or a month. The location of occurrence can be notified of a range such as a building, a region, or a country. The impact range can be notified of the number of people and the region affected. The response method can be notified of an emergency response procedure, a long-term response procedure, etc. Thereby, information on the predicted accident can be notified to each group company. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the prediction result into an AI model and cause the generation AI to execute the optimal notification method.

[0078] The search unit can instantly search and analyze the occurrence status and solution method of the same type of incident, derived effects, measures to prevent recurrence, and the report status to the relevant authorities, and derive the best response. The search unit instantly searches and analyzes the occurrence status and solution method of the same type of incident, derived effects, measures to prevent recurrence, and the report status to the relevant authorities, and derives the best response. For example, based on past incident information, the search unit searches and analyzes the occurrence status and solution method of the same type of incident, derived effects, measures to prevent recurrence, and the report status to the relevant authorities. The search unit can use AI to derive the best response. For example, if a similar accident occurs in the past, the search unit can refer to the solution method and measures to prevent recurrence. The search unit can search for the occurrence status of the incident information, such as the frequency of occurrence and the location of occurrence. As a solution method, it can search for technical solutions and management solutions. As derived effects, it can search for secondary damage and related problems. As measures to prevent recurrence, it can search for preventive measures and improvement measures. As the report status to the relevant authorities, it can search for the timing of the report and the report content. This makes it possible to search and analyze similar incident information and derive the best response. Some or all of the above-mentioned processing in the search unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the search unit may input similar incident information to the generation AI and cause the generation AI to perform search and analysis to derive the best response.

[0079] The evaluation unit accumulates information on whether the occurrence was prevented against the prediction, and can be used as a regular event to compare AI and human intelligence. The evaluation unit accumulates information on whether the occurrence was prevented against the prediction, and can be used as a regular event to compare AI and human intelligence. The evaluation unit, for example, evaluates whether the predicted incident actually occurred and accumulates the result. The evaluation unit can be used as a regular event to compete between AI and human intelligence. For example, the evaluation unit compares the prediction result of the AI ​​with the prediction result of a human and evaluates the accuracy. The evaluation unit can set the frequency and evaluation criteria of the regular event and perform evaluation periodically. Thereby, information on whether the occurrence was prevented against the prediction is accumulated, and can be used as a regular event to compete between AI and human intelligence. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit compares the prediction result with the actual occurrence situation and provides feedback to improve the accuracy of the evaluation.

[0080] The collection unit can estimate the user's emotion and adjust the collection timing of the incident information based on the estimated user's emotion. The collection unit estimates the user's emotion and adjusts the collection timing of the incident information based on the estimated user's emotion. For example, when the user is stressed, the collection unit delays the collection timing to reduce the burden on the user. When the user is relaxed, the collection unit can instantly collect incident information and prompt a quick response. When the user is in a hurry, the collection unit can advance the collection timing to quickly acquire information. This makes it possible to adjust the collection timing of the incident information based on 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, 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 cause the generation AI to adjust the collection timing.

[0081] The collection unit can analyze the past incident information of each group company and select the optimal collection method. The collection unit analyzes the past incident information of each group company and selects the optimal collection method. The collection unit, for example, identifies a pattern of frequently occurring accidents from the past incident information and selects a collection method based on the pattern. The collection unit can analyze the past response history of each group company and select the most effective collection method. The collection unit can evaluate the collection method of the past incident information and select the optimal method. This makes it possible to analyze the past incident information and select the optimal collection method. 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 past incident information to the generation AI and cause the generation AI to select the optimal collection method.

[0082] The collection unit can filter the incident information based on the current business status and risk level of each group company when collecting the incident information. The collection unit can filter the incident information based on the current business status and risk level of each group company when collecting the incident information. For example, the collection unit acquires the current business status of each group company in real time and preferentially collects incident information related to high-risk business. The collection unit can evaluate the risk level of each group company and filter incident information related to high-risk business. The collection unit can comprehensively judge the business status and risk level of each group company and collect optimal incident information. This makes it possible to filter the incident information based on the business status and risk level. A 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 business status data of each group company to the generation AI and cause the generation AI to execute filtering.

[0083] The collection unit can estimate the user's emotion and determine the priority of the incident information to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion and determines the priority of the incident information to be collected based on the estimated user's emotion. For example, when the user is stressed, the collection unit postpones the collection of low-importance incident information and prioritizes the collection of high-importance information. When the user is relaxed, the collection unit can collect all the incident information evenly. When the user is in a hurry, the collection unit can prioritize the collection of incident information that requires a quick response. This allows the priority of the incident information to be determined based on 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, 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 cause the generation AI to execute the determination of the priority of the incident information.

[0084] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of each group company when collecting incident information. The collection unit preferentially collects highly relevant information in consideration of the geographical location information of each group company when collecting incident information. For example, the collection unit preferentially collects incident information occurring in the vicinity based on the geographical location information of each group company. The collection unit can preferentially collect incident information in a highly geographically relevant area. The collection unit can preferentially collect incident information having a wide range of influence in consideration of the geographical location information of each group company. This makes it possible to preferentially collect highly relevant information in consideration of the geographical location information. 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 geographical location information of each group company to the generation AI and cause the generation AI to execute collection of highly relevant information.

[0085] The collection unit can analyze the social media activity of each group company when collecting incident information, and collect related information. The collection unit can analyze the social media activity of each group company when collecting incident information, and collect related information. The collection unit, for example, monitors the social media activity of each group company and collects posts related to the incident. The collection unit can analyze discussions about the incident on social media and collect related information. The collection unit can analyze the social media activity of each group company, grasp trends related to the incident, and collect information. This makes it possible to analyze social media activity and collect related information. 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 social media activity data to the generation AI and cause the generation AI to execute collection of 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. 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. When the user is relaxed, the analysis unit can provide a detailed analysis result. When the user is in a hurry, the analysis unit can provide a concise analysis result that covers the main points. This makes it possible to adjust the expression method of the analysis based on 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, 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 cause the generation AI to 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 incident information during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the incident information during the analysis. The analysis unit, for example, performs a detailed analysis on incident information with a high level of importance. The analysis unit can perform a simplified analysis on incident information with a low level of importance. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the incident information. This makes it possible to adjust the level of detail of the analysis based on the importance of the incident information. 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 importance data of the incident information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of the incident during analysis. The analysis unit can apply different analysis algorithms depending on the category of the incident during analysis. The analysis unit can apply a specific analysis algorithm to a safety accident, for example. The analysis unit can apply another analysis algorithm to an environmental accident. The analysis unit can select and apply an optimal analysis algorithm depending on the category of the incident. This makes it possible to apply different analysis algorithms depending on the category of the incident. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input incident category data to the generation AI and cause the generation AI to select and apply an 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. 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. When the user is relaxed, the analysis unit can provide a detailed analysis result. When the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows the length of the analysis to be adjusted based on 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 cause the generation AI to adjust the length of the analysis.

[0090] The analysis unit can determine the priority of the analysis based on the occurrence time of the incident information during the analysis. The analysis unit determines the priority of the analysis based on the occurrence time of the incident information during the analysis. For example, the analysis unit prioritizes the analysis of recently occurring incident information. The analysis unit can postpone the analysis of incident information that occurred earlier. The analysis unit can gradually adjust the priority of the analysis based on the occurrence time of the incident information. This makes it possible to determine the priority of the analysis based on the occurrence time of the incident information. 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 occurrence time data of the incident information to the generation AI and cause the generation AI to determine the priority of the analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the incident information during analysis. The analysis unit adjusts the order of analysis based on the relevance of the incident information during analysis. For example, the analysis unit preferentially analyzes highly relevant incident information. The analysis unit can analyze less relevant incident information later. The analysis unit can adjust the order of analysis stepwise based on the relevance of the incident information. This makes it possible to adjust the order of analysis based on the relevance of the incident information. 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 relevance data of the incident information to the generation AI and cause the generation AI to adjust the order of analysis.

[0092] The notification unit can estimate the user's emotion and adjust the expression method of the notification based on the estimated user's emotion. The notification unit can estimate the user's emotion and adjust the expression method of the notification based on the estimated user's emotion. For example, when the user is nervous, the notification unit provides a simple and highly visible notification. When the user is relaxed, the notification unit can provide a detailed notification. When the user is in a hurry, the notification unit can provide a concise notification that covers the main points. This makes it possible to adjust the expression method of the notification based on 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 notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the notification.

[0093] The notification unit can adjust the level of detail of the notification based on the importance of the predicted accident at the time of notification. The notification unit adjusts the level of detail of the notification based on the importance of the predicted accident at the time of notification. For example, the notification unit issues a detailed notification for an accident with a high level of importance. The notification unit can issue a simplified notification for an accident with a low level of importance. The notification unit can gradually adjust the level of detail of the notification according to the importance of the predicted accident. This makes it possible to adjust the level of detail of the notification based on the importance of the predicted accident. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the importance data of the predicted accident to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0094] The notification unit can apply different notification algorithms depending on the category of the predicted accident at the time of notification. The notification unit applies different notification algorithms depending on the category of the predicted accident at the time of notification. The notification unit applies a specific notification algorithm to a safety accident, for example. The notification unit can apply another notification algorithm to an environmental accident. The notification unit can select and apply an optimal notification algorithm depending on the category of the predicted accident. This makes it possible to apply different notification algorithms depending on the category of the predicted accident. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input category data of the predicted accident to the generation AI and cause the generation AI to select and apply the optimal notification algorithm.

[0095] The notification unit can estimate the user's emotion and adjust the length of the notification based on the estimated user's emotion. The notification unit can estimate the user's emotion and adjust the length of the notification based on the estimated user's emotion. For example, when the user is in a hurry, the notification unit provides a short and to the point notification. When the user is relaxed, the notification unit can provide a detailed notification. When the user is excited, the notification unit can provide a notification with a visually stimulating effect. This allows the length of the notification to be adjusted based on 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 notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the notification.

[0096] The notification unit can determine the priority of the notification based on the predicted time of occurrence of the accident at the time of notification. The notification unit determines the priority of the notification based on the predicted time of occurrence of the accident at the time of notification. For example, when the predicted time of occurrence of the accident is near, the notification unit prioritizes the notification. The notification unit can postpone the notification of the accident that is far away. The notification unit can gradually adjust the priority of the notification based on the predicted time of occurrence of the accident. This makes it possible to determine the priority of the notification based on the predicted time of occurrence of the accident. Part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the predicted time of occurrence data of the accident to the generation AI and cause the generation AI to execute the determination of the priority of the notification.

[0097] The notification unit can adjust the order of notification based on the predicted relevance of the accidents at the time of notification. The notification unit adjusts the order of notification based on the predicted relevance of the accidents at the time of notification. For example, the notification unit prioritizes the notification of highly relevant accidents. The notification unit can notify less relevant accidents at a later date. The notification unit can gradually adjust the order of notification based on the predicted relevance of the accidents. This makes it possible to adjust the order of notification based on the predicted relevance of the accidents. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the relevance data of the predicted accidents to the generation AI and cause the generation AI to adjust the order of notification.

[0098] The search unit can estimate the user's emotion and adjust the display method of the search results based on the estimated user's emotion. The search unit can estimate the user's emotion and adjust the display method of the search results based on the estimated user's emotion. For example, when the user is nervous, the search unit provides a simple search result with high visibility. When the user is relaxed, the search unit can provide a detailed search result. When the user is in a hurry, the search unit can provide a concise search result with the main points. This makes it possible to adjust the display method of the search results based on 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, 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 search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the search results.

[0099] The search unit can improve the accuracy of the search by considering the interrelationships of the incident information at the time of the search. The search unit can improve the accuracy of the search by considering the interrelationships of the incident information at the time of the search. For example, the search unit analyzes the interrelationships of the incident information and preferentially displays information with high relevance. The search unit can improve the accuracy of the search result by considering the interrelationships of the incident information. The search unit can provide an optimal search result based on the interrelationships of the incident information. This makes it possible to improve the accuracy of the search by considering the interrelationships of the incident information. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI or may be performed without using AI. For example, the search unit can input interrelationship data of the incident information to the generation AI and cause the generation AI to execute the improvement of the search accuracy.

[0100] The search unit can perform a search while taking into consideration the attribute information of the submitter of the incident information at the time of searching. The search unit performs a search while taking into consideration the attribute information of the submitter of the incident information at the time of searching. The search unit preferentially displays highly relevant information based on, for example, the attribute information of the submitter of the incident information. The search unit can improve the accuracy of the search result by taking into consideration the attribute information of the submitter. The search unit can provide optimal search results based on the attribute information of the submitter. This allows a search to be performed while taking into consideration the attribute information of the submitter. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input attribute information data of the submitter to the generation AI and cause the generation AI to execute a search.

[0101] The search unit can estimate the user's emotion and adjust the display order of the search results based on the estimated user's emotion. The search unit can estimate the user's emotion and adjust the display order of the search results based on the estimated user's emotion. For example, when the user is nervous, the search unit can preferentially display important information. When the user is relaxed, the search unit can preferentially display detailed information. When the user is in a hurry, the search unit can preferentially display information that covers the main points. This makes it possible to adjust the display order of the search results based on 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, 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 search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display order of the search results.

[0102] The search unit can perform a search taking into consideration the geographical distribution of the incident information at the time of a search. The search unit performs a search taking into consideration the geographical distribution of the incident information at the time of a search. The search unit preferentially displays highly relevant information based on the geographical distribution of the incident information, for example. The search unit can improve the accuracy of the search results by taking into consideration the geographical distribution. The search unit can provide optimal search results based on the geographical distribution. This makes it possible to perform a search taking into consideration the geographical distribution. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input geographical distribution data of the incident information to the generation AI and cause the generation AI to execute a search.

[0103] The search unit can improve the accuracy of the search by referring to the literature related to the incident information at the time of the search. The search unit can improve the accuracy of the search by referring to the literature related to the incident information at the time of the search. For example, the search unit preferentially displays information with high relevance based on the literature related to the incident information. The search unit can improve the accuracy of the search result by referring to the related literature. The search unit can provide an optimal search result based on the related literature. This can improve the accuracy of the search by referring to the related literature. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the literature data related to the incident information to the generation AI and cause the generation AI to execute the search.

[0104] The evaluation unit can estimate the user's emotion and adjust the evaluation method based on the estimated user's emotion. The evaluation unit can estimate the user's emotion and adjust the evaluation method based on the estimated user's emotion. For example, when the user is nervous, the evaluation unit provides a simple and highly visible evaluation method. When the user is relaxed, the evaluation unit can provide a detailed evaluation method. When the user is in a hurry, the evaluation unit can provide a concise evaluation method that focuses on the main points. This makes it possible to adjust the evaluation method based on 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, 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 evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the evaluation method.

[0105] The evaluation unit can improve the accuracy of the evaluation by analyzing the actual occurrence situation with respect to the prediction at the time of evaluation. The evaluation unit can improve the accuracy of the evaluation by analyzing the actual occurrence situation with respect to the prediction at the time of evaluation. The evaluation unit, for example, compares the actual occurrence situation with the prediction and improves the accuracy of the evaluation. The evaluation unit can analyze the actual occurrence situation and evaluate the accuracy of the prediction. The evaluation unit can analyze the difference between the prediction and the actual occurrence situation and improve the accuracy of the evaluation. Thereby, the accuracy of the evaluation can be improved by analyzing the actual occurrence situation with respect to the prediction. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI. For example, the evaluation unit can input the prediction result and actual occurrence situation data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the evaluation.

[0106] The evaluation unit can perform the evaluation while taking into consideration the attribute information of each group company at the time of evaluation. The evaluation unit performs the evaluation while taking into consideration the attribute information of each group company at the time of evaluation. The evaluation unit improves the accuracy of the evaluation, for example, based on the attribute information of each group company. The evaluation unit can adjust the evaluation method while taking into consideration the attribute information. The evaluation unit can provide an optimal evaluation method based on the attribute information of each group company. This allows the evaluation to be performed while taking into consideration the attribute information of each group company. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input attribute information data of each group company to the generation AI and cause the generation AI to perform the evaluation.

[0107] The evaluation unit can estimate the user's emotion and adjust the display method of the evaluation result based on the estimated user's emotion. The evaluation unit estimates the user's emotion and adjusts the display method of the evaluation result based on the estimated user's emotion. For example, when the user is nervous, the evaluation unit provides a simple and highly visible evaluation result. When the user is relaxed, the evaluation unit can provide a detailed evaluation result. When the user is in a hurry, the evaluation unit can provide a concise evaluation result that covers the main points. This makes it possible to adjust the display method of the evaluation result based on 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, 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 evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the evaluation result.

[0108] The evaluation unit can perform the evaluation taking into consideration the geographical distribution of the incident information at the time of evaluation. The evaluation unit performs the evaluation taking into consideration the geographical distribution of the incident information at the time of evaluation. The evaluation unit improves the accuracy of the evaluation, for example, based on the geographical distribution of the incident information. The evaluation unit can adjust the evaluation method taking into consideration the geographical distribution. The evaluation unit can provide an optimal evaluation method based on the geographical distribution. This makes it possible to perform the evaluation taking into consideration the geographical distribution. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input geographical distribution data of the incident information to the generation AI and cause the generation AI to perform the evaluation.

[0109] The evaluation unit can improve the accuracy of the evaluation by referring to the related literature of the incident information at the time of evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to the related literature of the incident information at the time of evaluation. The evaluation unit can improve the accuracy of the evaluation, for example, based on the related literature of the incident information. The evaluation unit can adjust the evaluation method by referring to the related literature. The evaluation unit can provide an optimal evaluation method based on the related literature. This can improve the accuracy of the evaluation by referring to the related literature. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the related literature data of the incident information to the generation AI and cause the generation AI to execute the evaluation.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The incident prediction system can further evaluate the skill level of employees of each group company and optimize the division of roles for responding to incidents based on the skill level. For example, the collection unit collects skill data of each employee, and the analysis unit proposes optimal division of roles based on the skill data. The notification unit notifies each employee of the results of the division of roles, and the search unit can propose a training program for improving skills based on past incident response history. The evaluation unit can evaluate the effectiveness of the division of roles and provide feedback to promote improvement of skill levels.

[0112] The collection unit can monitor the health status of employees of each group company and adjust the timing of collecting incident information based on the health status. For example, if an employee is tired, the collection timing can be delayed to reduce the burden on the employee. If the employee is healthy, incident information can be collected immediately to encourage a quick response. This makes it possible to adjust the timing of collecting incident information based on the employee's health status.

[0113] When analyzing incident information, the analysis department can adjust the analysis algorithm taking into account the culture and business processes of each group company. For example, a specific analysis algorithm can be applied to group companies with different cultural backgrounds. If the business processes are different, a different analysis algorithm can be applied. This makes it possible to provide optimal analysis results according to culture and business processes.

[0114] The notification department can determine the priority of notifications based on the predicted scope of impact of the accident. For example, priority is given to notifications of accidents with a wide scope of impact. Accidents with a narrow scope of impact can be notified later. This makes it possible to adjust the priority of notifications based on the scope of impact.

[0115] When searching for incident information, the search unit can adjust the search results taking into account the workload of each group company. For example, important information can be displayed preferentially for group companies with a high workload. When the workload is low, detailed information can be provided. This makes it possible to adjust the search results based on the workload.

[0116] The evaluation unit can estimate the user's emotion and adjust the evaluation feedback method based on the estimated user's emotion. For example, if the user is stressed, simple positive feedback can be provided. If the user is relaxed, detailed feedback can be provided. This makes it possible to adjust the evaluation feedback method based on the user's emotion.

[0117] The collection unit can estimate the user's emotion and adjust the format of the incident information to be collected based on the estimated user's emotion. For example, when the user is nervous, the information can be collected in a concise format. When the user is relaxed, the information can be collected in a detailed format. This makes it possible to adjust the collection format of the incident information based on the user's emotion.

[0118] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis result based on the estimated user's emotions. For example, if the user is in a hurry, a short and to the point notification can be provided. If the user is relaxed, a detailed notification can be provided. In this way, the notification method of the analysis result can be adjusted based on the user's emotions.

[0119] The notification unit can estimate the user's emotions and adjust the timing of the notification based on the estimated user's emotions. For example, if the user is feeling stressed, the timing of the notification can be delayed to reduce the burden on the user. If the user is relaxed, the notification can be made immediately to encourage a quick response. This makes it possible to adjust the timing of the notification based on the user's emotions.

[0120] The evaluation unit can estimate the user's emotion and adjust the evaluation criteria based on the estimated user's emotion. For example, if the user is nervous, the evaluation can be performed using loose criteria. If the user is relaxed, the evaluation can be performed using strict criteria. This makes it possible to adjust the evaluation criteria based on the user's emotion.

[0121] The process flow of the second embodiment will be briefly described below.

[0122] Step 1: The collection department collects incident information. Incident information includes the date and time of occurrence, the location of occurrence, the type of accident, the scope of impact, the solution, measures to prevent recurrence, and the status of reporting to the relevant authorities. The collection department accepts incident information from each group company. The collection department can use manual or automatic collection methods. For example, incident information is automatically acquired from the system of each group company. Incident information can also be entered manually. Step 2: The analysis department uses AI to analyze the incident information collected by the collection department. Using machine learning algorithms and deep learning technology, the analysis department predicts which group company will experience an accident, when, and what type of accident will occur. It learns from past incident data and builds a model to predict future incidents. Step 3: The Notification Department announces the prediction results obtained by the Analysis Department to each group company. The Notification Department announces the predicted type of accident, the time of occurrence, the location of occurrence, the scope of impact, how to respond, etc. The prediction results can be communicated to each group company using email or a notification system. Step 4: The search unit searches and analyzes similar incident information. Based on past incident information, the search unit instantly searches and analyzes the occurrence status and resolution methods of similar incidents, their derived effects, measures to prevent recurrence, and the status of reporting to relevant authorities. Using AI, it is possible to derive the best response. Step 5: The evaluation unit evaluates whether the predicted incidents were prevented from occurring. The evaluation unit evaluates whether the predicted incidents actually occurred and accumulates the results. The evaluation unit compares the prediction results with the actual occurrences and provides feedback to improve the accuracy of the evaluation.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements including the above-mentioned collection unit, analysis unit, notification unit, search unit, and evaluation 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 is realized by the control unit 46A of the smart device 14, and collects incident information from each group company. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected incident information by AI. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and notifies each group company of the prediction result. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and searches and analyzes the same type of incident information. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates whether the occurrence was suppressed according to the prediction. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements including the above-mentioned collection unit, analysis unit, notification unit, search unit, and evaluation 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 is realized by the control unit 46A of the smart glasses 214, and collects incident information from each group company. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected incident information by AI. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214, and notifies each group company of the prediction result. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and searches and analyzes the same type of incident information. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates whether the occurrence was suppressed according to the prediction. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements including the above-mentioned collection unit, analysis unit, notification unit, search unit, and evaluation 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 is realized by the control unit 46A of the headset type terminal 314, and collects incident information from each group company. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected incident information by AI. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314, and notifies each group company of the prediction result. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and searches and analyzes the same type of incident information. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates whether the occurrence was suppressed according to the prediction. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements including the above-mentioned collection unit, analysis unit, notification unit, search unit, and evaluation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414, and collects incident information from each group company. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected incident information by AI. The notification unit is realized, for example, by the control unit 46A of the robot 414, and notifies each group company of the prediction result. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and searches and analyzes the same type of incident information. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates whether the occurrence was suppressed according to the prediction. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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 on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0180] 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.

[0181] 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 experiences positive feelings such as "I want more" or "I want to know more."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Appendix 1) A collection unit that collects incident information; an analysis unit that analyzes the incident information collected by the collection unit; a notification unit that notifies a prediction result obtained by the analysis unit; A search section that searches and analyzes similar incident information; An evaluation unit that evaluates whether or not the occurrence has been suppressed according to the prediction. A system characterized by: (Appendix 2) The collecting unit includes: Accepts incident information from each group company 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze collected incident information using AI to predict which group company will experience what type of accident, when, and at what time. 2. The system of claim 1, (Appendix 4) The notification section: The predicted type of accident, the time of occurrence, the location of the accident, the scope of the impact, and the response method will be announced to each group company. 2. The system of claim 1, (Appendix 5) The search unit is Instantly search and analyze the occurrence status of similar incidents, their resolution, their derived impact, measures to prevent recurrence, and the status of reports to the relevant authorities to determine the best response. 2. The system of claim 1, (Appendix 6) The evaluation unit is Accumulate information on whether predictions were made and whether they were successful in preventing occurrence, and use this information as a regular event to compare AI and human intelligence. 2. The system of claim 1, (Appendix 7) The collecting unit includes: To estimate a user's emotion and adjust the timing of collecting incident information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze past incident information from each group company and select the optimal collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting incident information, filter it based on the current business situation and risk level of each group company. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Estimate user emotions and prioritize incident information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting incident information, the geographic location information of each group company will be taken into consideration to prioritize the collection of relevant information. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting incident information, analyze the social media activities of each group company and collect related information. 2. The system of claim 1, (Appendix 13) 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 14) The analysis unit is During analysis, adjust the level of detail of the analysis based on the importance of the incident information 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, apply different analysis algorithms depending on the incident category 2. The system of claim 1, (Appendix 16) 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 17) The analysis unit is During analysis, prioritize analysis based on when the incident information occurred 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of incident information 2. The system of claim 1, (Appendix 19) The notification section: Infer a user's emotions and adjust the way the announcement is presented based on the estimated user's emotions. 2. The system of claim 1, (Appendix 20) The notification section: When issuing a notice, adjust the level of detail in the notice based on the predicted severity of the incident. 2. The system of claim 1, (Appendix 21) The notification section: At the time of notification, different notification algorithms are applied depending on the predicted accident category. 2. The system of claim 1, (Appendix 22) The notification section: Estimate the user's emotion and adjust the length of the announcement based on the estimated user's emotion. 2. The system of claim 1, (Appendix 23) The notification section: When announcing a disaster, the priority of the announcement will be determined based on the predicted timing of the accident. 2. The system of claim 1, (Appendix 24) The notification section: Adjust the order of announcements based on predicted accident relevance during announcements 2. The system of claim 1, (Appendix 25) The search unit is Infer user sentiment and adjust how search results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 26) The search unit is When searching, the accuracy of the search is improved by considering the correlation of incident information. 2. The system of claim 1, (Appendix 27) The search unit is When searching, the attribute information of the person who submitted the incident information is taken into consideration. 2. The system of claim 1, (Appendix 28) The search unit is Infer user sentiment and adjust the order of search results based on the inferred sentiment 2. The system of claim 1, (Appendix 29) The search unit is When searching, the geographic distribution of incident information is taken into account. 2. The system of claim 1, (Appendix 30) The search unit is When searching, refer to related literature on incident information to improve search accuracy 2. The system of claim 1, (Appendix 31) The evaluation unit is Inferring user emotions and adjusting the evaluation method based on the estimated user emotions 2. The system of claim 1, (Appendix 32) The evaluation unit is During evaluation, analyze actual occurrences against predictions to improve the accuracy of the evaluation 2. The system of claim 1, (Appendix 33) The evaluation unit is When evaluating, the attribute information of each group company is taken into consideration. 2. The system of claim 1, (Appendix 34) The evaluation unit is Inferring user emotion and adjusting the way rating results are displayed based on the estimated user emotion 2. The system of claim 1, (Appendix 35) The evaluation unit is When assessing, the geographic distribution of incident information is taken into account. 2. The system of claim 1, (Appendix 36) The evaluation unit is During the evaluation, refer to relevant literature on incident information to improve the accuracy of the evaluation. 2. The system of claim 1, [Explanation of symbols]

[0195] 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 unit that collects incident information; an analysis unit that analyzes the incident information collected by the collection unit; a notification unit that notifies a prediction result obtained by the analysis unit; A search unit that searches and analyzes similar incident information; An evaluation unit that evaluates whether or not the occurrence has been suppressed according to the prediction. A system characterized by:

2. The collecting unit includes: Accepts incident information from each group company 2. The system of claim 1 .

3. The analysis unit is Analyze collected incident information using AI to predict which group company will experience what type of accident, when, and at what time.

2. The system of claim 1 .

4. The notification section: The predicted type of accident, the time of occurrence, the location of the accident, the scope of the impact, and the response method will be announced to each group company.

2. The system of claim 1 .

5. The search unit is Instantly search and analyze the occurrence status of similar incidents, their resolution, their ramifications, measures to prevent recurrence, and the status of reports to the relevant authorities to determine the best response.

2. The system of claim 1 .

6. The evaluation unit is Accumulate information on whether predictions were made and whether the occurrences were prevented, and use it as a regular event to compare AI and human intelligence.

2. The system of claim 1 .

7. The collecting unit includes: To estimate a user's emotion and adjust the timing of collecting incident information based on the estimated user's emotion.

2. The system of claim 1 .

8. The collecting unit includes: Analyze past incident information from each group company and select the optimal collection method 2. The system of claim 1 .

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