System

Abstract

An object of the system according to the embodiment is to present a countermeasure or a recurrence prevention measure when an accident occurs, and to predict an accident that will occur in the future and present a measure.SOLUTION: A system according to an embodiment includes an accident countermeasure presentation unit, a recurrence prevention measure presentation unit, an accident occurrence prediction unit, and a measure presentation unit. The accident countermeasure presentation unit presents an optimal countermeasure for the accident that has occurred. The recurrence prevention measure presentation unit presents a recurrence prevention measure based on the countermeasure presented by the accident countermeasure presentation unit. The accident occurrence prediction unit predicts an accident having a high probability of occurring in the future. The countermeasure presentation unit presents a countermeasure against the accident predicted by the accident occurrence prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had problems in that it is insufficient to respond when an accident occurs or to present measures to prevent recurrence, and it is difficult to predict and take measures against future accidents.

[0005] The system according to the embodiment aims to propose responses when an accident occurs and measures to prevent recurrence, and to predict future accidents and propose countermeasures. [Means for solving the problem]

[0006] The system according to the embodiment includes an accident response measure presentation unit, a recurrence prevention measure presentation unit, an accident occurrence prediction unit, and a measure presentation unit. The accident response measure presentation unit presents optimal response measures for an accident that has already occurred. The recurrence prevention measure presentation unit presents recurrence prevention measures based on the response measures presented by the accident response measure presentation unit. The accident occurrence prediction unit predicts accidents that are likely to occur in the future. The measure presentation unit presents measures for accidents predicted by the accident occurrence prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose responses when an accident occurs and measures to prevent recurrence, and can predict future accidents and propose countermeasures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The accident management system according to an embodiment of the present invention is a system for managing accidents and predicting their occurrence. This system uses generative AI to propose optimal response measures and measures to prevent recurrence in the event of an accident, and in cooperation with work management tools and schedules, it presents accidents that are likely to occur in the future and countermeasures for them. This allows the accident management system to efficiently manage and predict accidents.

[0029] An accident management system according to an embodiment includes an accident response measure presentation unit, a recurrence prevention measure presentation unit, an accident occurrence prediction unit, and a countermeasure presentation unit. The accident response measure presentation unit presents optimal countermeasures for an accident that has occurred. For example, the accident response measure presentation unit uses a generation AI to analyze detailed information about the accident and propose emergency response procedures. The accident response measure presentation unit can also make proposals for optimizing resource allocation. The accident response measure presentation unit can also evaluate the scope of the accident's impact and propose appropriate countermeasures. The recurrence prevention measure presentation unit presents recurrence prevention measures based on the countermeasures presented by the accident response measure presentation unit. For example, the recurrence prevention measure presentation unit uses a generation AI to analyze past accident data and propose an education program. The recurrence prevention measure presentation unit can also make proposals for process improvement. The recurrence prevention measure presentation unit can also identify the cause of the accident and propose recurrence prevention measures based on the cause. The accident occurrence prediction unit predicts accidents that are likely to occur in the future. For example, the accident occurrence prediction unit uses a generation AI to analyze past data and perform risk assessment. The accident prediction unit can also predict the risk of an accident in cooperation with a work management tool or a schedule. The accident prediction unit can also predict the risk of an accident taking into account the season and social conditions. The countermeasure presentation unit presents countermeasures for accidents predicted by the accident prediction unit. For example, the countermeasure presentation unit proposes technical countermeasures using a generation AI. The countermeasure presentation unit can also propose organizational countermeasures. The countermeasure presentation unit can also present specific countermeasures for reducing the risk of an accident. This allows the accident management system according to the embodiment to efficiently manage and predict accidents. For example, the accident management system can quickly present countermeasures for preventing accidents from occurring. The accident management system can also quickly and appropriately respond after an accident occurs. The accident management system can also present specific countermeasures for preventing accidents from recurring.

[0030] The recurrence prevention measure presentation unit can use a machine learning algorithm to identify patterns of similar past accidents and generate recurrence prevention measures. For example, the recurrence prevention measure presentation unit inputs detailed information about the accident, and the generation AI uses a machine learning algorithm to analyze patterns of similar past accidents. For example, it identifies common points between accidents based on past data and proposes recurrence prevention measures. The recurrence prevention measure presentation unit can also use a machine learning algorithm to identify the conditions and causes of accidents and generate recurrence prevention measures based on those. The recurrence prevention measure presentation unit can also use a machine learning algorithm to analyze the frequency of accidents and the scope of their impact and propose recurrence prevention measures. This makes it possible to identify patterns of similar past accidents and generate recurrence prevention measures.

[0031] The countermeasure presentation unit can perform a simulation, analyze the extent of the accident's impact, and present the simulation results for selecting the optimal countermeasure. For example, the countermeasure presentation unit inputs detailed information about the accident, and the generation AI performs a simulation to analyze the extent of the accident's impact. For example, it visualizes the extent of the accident's impact and proposes the optimal countermeasure. The countermeasure presentation unit can also use a simulation to evaluate the extent of the accident's impact and select countermeasures based on that. The countermeasure presentation unit can also present specific countermeasures for minimizing the impact of the accident based on the simulation results. This makes it possible to simulate the extent of the accident's impact and select the optimal countermeasure.

[0032] The accident prediction unit introduces an anomaly detection algorithm and can detect behavior that deviates from normal work patterns at an early stage. For example, the accident prediction unit inputs work status and schedules, and the generation AI uses an anomaly detection algorithm to detect behavior that deviates from normal work patterns. For example, it can detect abnormal behavior at an early stage and predict the risk of an accident. The accident prediction unit can also use an anomaly detection algorithm to analyze changes in work patterns and predict the risk of an accident based on that. The accident prediction unit can also use an anomaly detection algorithm to monitor deviations from work patterns in real time and detect them at an early stage. This allows for early detection of behavior that deviates from normal work patterns.

[0033] The accident prediction unit can integrate past accident data and real-time business data to perform dynamic risk assessment. For example, the accident prediction unit inputs past accident data and real-time business data, and the generation AI integrates them to perform dynamic risk assessment. For example, the accident prediction unit evaluates current risks based on past data and predicts accident occurrence. Furthermore, the accident prediction unit can perform more accurate predictions by integrating past accident data and real-time business data to perform risk assessment. Furthermore, the accident prediction unit can monitor and predict accident risk in real time based on dynamic risk assessment. This makes it possible to integrate past accident data and real-time business data to perform dynamic risk assessment.

[0034] The accident prediction unit can integrate data from different regions and cultural spheres and make predictions from a global perspective. For example, the accident prediction unit inputs data from different regions and cultural spheres, and the generation AI integrates them to make accident predictions from a global perspective. For example, it makes predictions that take into account risk factors for each region. The accident prediction unit can also perform global risk assessments based on data from different regions and cultural spheres. The accident prediction unit can also integrate data from different regions and cultural spheres, evaluate risk factors for each region, and make predictions based on that. This makes it possible to integrate data from different regions and cultural spheres and make predictions from a global perspective.

[0035] The accident prediction unit can analyze the interactions between different business processes and identify complex risk factors. For example, the accident prediction unit inputs the business situation and schedule, and the generation AI analyzes the interactions between different business processes. For example, it identifies risk factors that occur when multiple business processes overlap. The accident prediction unit can also identify complex risk factors based on the interactions between different business processes and make predictions based on them. The accident prediction unit can also analyze the interactions between different business processes, evaluate risk factors, and make predictions based on them. This makes it possible to analyze the interactions between different business processes and identify complex risk factors.

[0036] The data source analysis unit analyzes the correlations between different data sources, making it possible to build a more accurate accident prediction model. For example, the data source analysis unit inputs data sources, and the generation AI analyzes the correlations between the different data sources. For example, the data source analysis unit builds an accident prediction model by integrating past accident data with real-time business data. The data source analysis unit can also improve the accuracy of the accident prediction model based on the correlations between different data sources. The data source analysis unit can also analyze the correlations, build an accident prediction model, and perform risk assessment based on that. This makes it possible to analyze the correlations between different data sources and build a more accurate accident prediction model.

[0037] The data source analysis unit can introduce time series analysis to predict future trends from past data. For example, the data source analysis unit inputs a data source, and the generation AI analyzes the past data using time series analysis. For example, the data source analysis unit can predict the risk of future accidents based on past accident data. The data source analysis unit can also use time series analysis to predict future trends from past data and perform risk assessments based on that. The data source analysis unit can also predict future trends based on time series analysis and propose countermeasures. This makes it possible to introduce time series analysis to predict future trends from past data.

[0038] The data source analysis unit can integrate data from different industries to obtain cross-industry insights. For example, the data source analysis unit inputs data sources and the generation AI analyzes data from different industries. For example, data from the manufacturing and medical industries is integrated to perform risk assessment. The data source analysis unit can also obtain cross-industry insights based on data from different industries. The data source analysis unit can also integrate data from different industries, evaluate risk factors between industries, and make predictions based on the evaluation. This makes it possible to integrate data from different industries to obtain cross-industry insights.

[0039] The data source analysis unit can integrate different data formats and perform multimodal analysis. For example, the data source analysis unit inputs a data source and the generation AI analyzes the different data formats. For example, text data and image data are integrated to evaluate the risk of an accident occurring. The data source analysis unit can also perform multimodal analysis based on different data formats. The data source analysis unit can also integrate text data, image data, audio data, etc., and perform risk evaluation based on that. This makes it possible to integrate different data formats and perform multimodal analysis.

[0040] The time and social situation analysis unit can integrate data on abnormal weather and natural disasters and make predictions that take environmental risks into account. For example, the time and social situation analysis unit inputs data on the time and social situation, and the generation AI analyzes the data on abnormal weather and natural disasters. For example, the risk of an accident is predicted based on the risk of abnormal weather and natural disasters. The time and social situation analysis unit can also integrate data on abnormal weather and natural disasters and make predictions that take environmental risks into account based on that data. The time and social situation analysis unit can also evaluate environmental risks based on data on abnormal weather and natural disasters and make predictions based on that. This makes it possible to integrate data on abnormal weather and natural disasters and make predictions that take environmental risks into account.

[0041] The time and social situation analysis unit can analyze the impact of political and economic events and reflect this in risk assessment. For example, the time and social situation analysis unit inputs data on time and social situations, and the generation AI analyzes the impact of political and economic events. For example, the risk of an accident occurring is predicted based on the impact of political events. The time and social situation analysis unit can also perform risk assessment based on the impact of political and economic events. The time and social situation analysis unit can also analyze the impact of political and economic events and perform risk assessment based on that. This makes it possible to analyze the impact of political and economic events and reflect this in risk assessment.

[0042] The time and social situation analysis unit can integrate data from different regions and perform risk assessments from a global perspective. For example, the time and social situation analysis unit inputs data on time and social situations, and the generation AI analyzes the data from different regions. For example, it performs risk assessments that take into account risk factors for each region. The time and social situation analysis unit can also perform global risk assessments based on data from different regions. The time and social situation analysis unit can also integrate data from different regions, evaluate risk factors for each region, and perform risk assessments based on that. This makes it possible to integrate data from different regions and perform risk assessments from a global perspective.

[0043] The time and social situation analysis unit can integrate different data formats and perform multimodal analysis. For example, the time and social situation analysis unit inputs data on time and social situations, and the generation AI analyzes the different data formats. For example, text data and image data are integrated to perform risk assessment. The time and social situation analysis unit can also perform multimodal analysis based on different data formats. The time and social situation analysis unit can also integrate text data, image data, audio data, etc., and perform risk assessment based on that. This makes it possible to integrate different data formats and perform multimodal analysis.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The accident prediction unit employs an anomaly detection algorithm to detect behavior that deviates from normal work patterns at an early stage. For example, work status and schedules are input, and an anomaly detection algorithm is used to detect behavior that deviates from normal work patterns. Abnormal behavior is detected early and the risk of an accident is predicted. It is also possible to analyze changes in work patterns and predict the risk of an accident based on that. Deviations from work patterns can also be monitored in real time and detected at an early stage. This allows for the early detection of behavior that deviates from normal work patterns.

[0046] The accident prediction unit can integrate past accident data and real-time business data to perform dynamic risk assessment. For example, past accident data and real-time business data are input and integrated to perform dynamic risk assessment. Current risks are evaluated based on the past data, and accident predictions are made. In addition, by integrating past accident data and real-time business data to perform risk assessment, more accurate predictions can be made. Furthermore, based on dynamic risk assessment, accident risk can also be monitored and predicted in real time. This makes it possible to integrate past accident data and real-time business data to perform dynamic risk assessment.

[0047] The accident prediction unit can integrate data from different regions and cultural spheres and make predictions from a global perspective. For example, it inputs data from different regions and cultural spheres, integrates them, and makes accident predictions from a global perspective. It makes predictions that take into account risk factors for each region. It can also make global risk assessments based on data from different regions and cultural spheres. It can also integrate data from different regions and cultural spheres, evaluate risk factors for each region, and make predictions based on that. This makes it possible to integrate data from different regions and cultural spheres and make predictions from a global perspective.

[0048] The data source analysis unit can analyze the correlations between different data sources and build a more accurate accident prediction model. For example, it inputs data sources and analyzes the correlations between the different data sources. It builds an accident prediction model by integrating past accident data with real-time business data. It can also improve the accuracy of the accident prediction model based on the correlations between different data sources. It can also analyze the correlations, build an accident prediction model, and perform risk assessments based on that. This makes it possible to analyze the correlations between different data sources and build a more accurate accident prediction model.

[0049] The seasonal and social situation analysis unit can integrate data on abnormal weather and natural disasters and make predictions that take environmental risks into account. For example, it inputs data on seasonal and social situations and analyzes data on abnormal weather and natural disasters. It predicts the risk of accidents based on the risk of abnormal weather and natural disasters. It can also integrate data on abnormal weather and natural disasters and make predictions that take environmental risks into account based on that. Furthermore, it can evaluate environmental risks based on data on abnormal weather and natural disasters and make predictions based on that. This makes it possible to integrate data on abnormal weather and natural disasters and make predictions that take environmental risks into account.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The accident response measure suggestion component suggests optimal response measures for an accident that has already occurred. For example, it uses generative AI to analyze detailed information about the accident and propose emergency response procedures. It can also suggest ways to optimize resource allocation and evaluate the scope of the accident's impact to propose appropriate countermeasures. Step 2: The recurrence prevention measures presentation component presents recurrence prevention measures based on the response measures presented by the incident response measures presentation component. For example, it can use generative AI to analyze past accident data, propose training programs and process improvements, and identify the causes of accidents and present recurrence prevention measures based on those. Step 3: The accident prediction unit predicts accidents that are likely to occur in the future. For example, it uses generative AI to analyze past data and perform risk assessment. It can also link with work management tools and schedules to predict accident risks, taking into account the time of year and social conditions. Step 4: The countermeasure proposal unit proposes countermeasures for the accidents predicted by the accident prediction unit. For example, the generation AI can be used to propose technical and organizational countermeasures, and specific measures to reduce the risk of accidents occurring can be proposed.

[0052] (Example 2) The accident management system according to an embodiment of the present invention is a system for managing accidents and predicting their occurrence. This system uses generative AI to propose optimal response measures and measures to prevent recurrence in the event of an accident, and in cooperation with work management tools and schedules, it presents accidents that are likely to occur in the future and countermeasures for them. This allows the accident management system to efficiently manage and predict accidents.

[0053] An accident management system according to an embodiment includes an accident response measure presentation unit, a recurrence prevention measure presentation unit, an accident occurrence prediction unit, and a countermeasure presentation unit. The accident response measure presentation unit presents optimal countermeasures for an accident that has occurred. For example, the accident response measure presentation unit uses a generation AI to analyze detailed information about the accident and propose emergency response procedures. The accident response measure presentation unit can also make proposals for optimizing resource allocation. The accident response measure presentation unit can also evaluate the scope of the accident's impact and propose appropriate countermeasures. The recurrence prevention measure presentation unit presents recurrence prevention measures based on the countermeasures presented by the accident response measure presentation unit. For example, the recurrence prevention measure presentation unit uses a generation AI to analyze past accident data and propose an education program. The recurrence prevention measure presentation unit can also make proposals for process improvement. The recurrence prevention measure presentation unit can also identify the cause of the accident and propose recurrence prevention measures based on the cause. The accident occurrence prediction unit predicts accidents that are likely to occur in the future. For example, the accident occurrence prediction unit uses a generation AI to analyze past data and perform risk assessment. The accident prediction unit can also predict the risk of an accident in cooperation with a work management tool or a schedule. The accident prediction unit can also predict the risk of an accident taking into account the season and social conditions. The countermeasure presentation unit presents countermeasures for accidents predicted by the accident prediction unit. For example, the countermeasure presentation unit proposes technical countermeasures using a generation AI. The countermeasure presentation unit can also propose organizational countermeasures. The countermeasure presentation unit can also present specific countermeasures for reducing the risk of an accident. This allows the accident management system according to the embodiment to efficiently manage and predict accidents. For example, the accident management system can quickly present countermeasures for preventing accidents from occurring. The accident management system can also quickly and appropriately respond after an accident occurs. The accident management system can also present specific countermeasures for preventing accidents from recurring.

[0054] The accident response measure presentation unit can use the emotion estimation function to propose measures to reduce employees' psychological stress. For example, the accident response measure presentation unit inputs detailed information about an accident, and the generation AI uses the emotion estimation function to analyze employees' psychological stress. For example, the accident response measure presentation unit can monitor employees' emotional state in real time after an accident and propose counseling or relaxation methods to reduce stress. The accident response measure presentation unit can also propose vacation systems or adjustments to working hours to reduce stress based on employees' emotional data. The accident response measure presentation unit can also propose appropriate communication methods taking into account employees' emotional state. This makes it possible to propose measures to reduce employees' psychological stress.

[0055] The recurrence prevention measure presentation unit can use a machine learning algorithm to identify patterns of similar past accidents and generate recurrence prevention measures. For example, the recurrence prevention measure presentation unit inputs detailed information about the accident, and the generation AI uses a machine learning algorithm to analyze patterns of similar past accidents. For example, it identifies common points between accidents based on past data and proposes recurrence prevention measures. The recurrence prevention measure presentation unit can also use a machine learning algorithm to identify the conditions and causes of accidents and generate recurrence prevention measures based on those. The recurrence prevention measure presentation unit can also use a machine learning algorithm to analyze the frequency of accidents and the scope of their impact and propose recurrence prevention measures. This makes it possible to identify patterns of similar past accidents and generate recurrence prevention measures.

[0056] The countermeasure presentation unit can perform a simulation, analyze the extent of the accident's impact, and present the simulation results for selecting the optimal countermeasure. For example, the countermeasure presentation unit inputs detailed information about the accident, and the generation AI performs a simulation to analyze the extent of the accident's impact. For example, it visualizes the extent of the accident's impact and proposes the optimal countermeasure. The countermeasure presentation unit can also use a simulation to evaluate the extent of the accident's impact and select countermeasures based on that. The countermeasure presentation unit can also present specific countermeasures for minimizing the impact of the accident based on the simulation results. This makes it possible to simulate the extent of the accident's impact and select the optimal countermeasure.

[0057] The accident prediction unit can use the emotion estimation function to make predictions that take into account the stress level and fatigue level of employees. For example, the accident prediction unit inputs work status and schedules, and the generation AI uses the emotion estimation function to analyze the stress level and fatigue level of employees. For example, it predicts that the risk of accidents increases when the employee's stress level is high. The accident prediction unit can also use the emotion estimation function to evaluate the employee's fatigue level and predict the risk of accidents based on that. The accident prediction unit can also make predictions that take into account the stress level and fatigue level based on the employee's emotion data. This makes it possible to make predictions that take into account the employee's stress level and fatigue level.

[0058] The accident prediction unit introduces an anomaly detection algorithm and can detect behavior that deviates from normal work patterns at an early stage. For example, the accident prediction unit inputs work status and schedules, and the generation AI uses an anomaly detection algorithm to detect behavior that deviates from normal work patterns. For example, it can detect abnormal behavior at an early stage and predict the risk of an accident. The accident prediction unit can also use an anomaly detection algorithm to analyze changes in work patterns and predict the risk of an accident based on that. The accident prediction unit can also use an anomaly detection algorithm to monitor deviations from work patterns in real time and detect them at an early stage. This allows for early detection of behavior that deviates from normal work patterns.

[0059] The accident prediction unit can integrate past accident data and real-time business data to perform dynamic risk assessment. For example, the accident prediction unit inputs past accident data and real-time business data, and the generation AI integrates them to perform dynamic risk assessment. For example, the accident prediction unit evaluates current risks based on past data and predicts accident occurrence. Furthermore, the accident prediction unit can perform more accurate predictions by integrating past accident data and real-time business data to perform risk assessment. Furthermore, the accident prediction unit can monitor and predict accident risk in real time based on dynamic risk assessment. This makes it possible to integrate past accident data and real-time business data to perform dynamic risk assessment.

[0060] The accident prediction unit can integrate data from different regions and cultural spheres and make predictions from a global perspective. For example, the accident prediction unit inputs data from different regions and cultural spheres, and the generation AI integrates them to make accident predictions from a global perspective. For example, it makes predictions that take into account risk factors for each region. The accident prediction unit can also perform global risk assessments based on data from different regions and cultural spheres. The accident prediction unit can also integrate data from different regions and cultural spheres, evaluate risk factors for each region, and make predictions based on that. This makes it possible to integrate data from different regions and cultural spheres and make predictions from a global perspective.

[0061] The accident prediction unit can use the emotion estimation function to monitor the emotional state of employees in real time and make predictions according to changes in emotions. For example, the accident prediction unit inputs work status and schedules, and the generation AI uses the emotion estimation function to monitor the emotional state of employees in real time. For example, it predicts the risk of an accident occurring according to changes in emotions. The accident prediction unit can also use the emotion estimation function to analyze changes in emotions based on employee emotional data and make predictions based on that. The accident prediction unit can also use the emotion estimation function to monitor the emotional state of employees in real time and make predictions according to changes in emotions. This makes it possible to monitor the emotional state of employees in real time and make predictions according to changes in emotions.

[0062] The accident prediction unit can analyze the interactions between different business processes and identify complex risk factors. For example, the accident prediction unit inputs the business situation and schedule, and the generation AI analyzes the interactions between different business processes. For example, it identifies risk factors that occur when multiple business processes overlap. The accident prediction unit can also identify complex risk factors based on the interactions between different business processes and make predictions based on them. The accident prediction unit can also analyze the interactions between different business processes, evaluate risk factors, and make predictions based on them. This makes it possible to analyze the interactions between different business processes and identify complex risk factors.

[0063] The data source analysis unit can use the emotion estimation function to perform multidimensional analysis including employee emotion data. For example, the data source analysis unit inputs a data source, and the generation AI uses the emotion estimation function to analyze employee emotion data. For example, the data source analysis unit evaluates the risk of an accident occurring based on the emotion data and proposes countermeasures. The data source analysis unit can also use the emotion estimation function to perform multidimensional analysis of employee emotion data and perform risk assessment based on that analysis. The data source analysis unit can also predict the risk of an accident occurring based on the multidimensional analysis including the emotion data and propose countermeasures. This makes it possible to perform multidimensional analysis including employee emotion data.

[0064] The data source analysis unit analyzes the correlations between different data sources, making it possible to build a more accurate accident prediction model. For example, the data source analysis unit inputs data sources, and the generation AI analyzes the correlations between the different data sources. For example, the data source analysis unit builds an accident prediction model by integrating past accident data with real-time business data. The data source analysis unit can also improve the accuracy of the accident prediction model based on the correlations between different data sources. The data source analysis unit can also analyze the correlations, build an accident prediction model, and perform risk assessment based on that. This makes it possible to analyze the correlations between different data sources and build a more accurate accident prediction model.

[0065] The data source analysis unit can introduce time series analysis to predict future trends from past data. For example, the data source analysis unit inputs a data source, and the generation AI analyzes the past data using time series analysis. For example, the data source analysis unit can predict the risk of future accidents based on past accident data. The data source analysis unit can also use time series analysis to predict future trends from past data and perform risk assessments based on that. The data source analysis unit can also predict future trends based on time series analysis and propose countermeasures. This makes it possible to introduce time series analysis to predict future trends from past data.

[0066] The data source analysis unit can integrate data from different industries to obtain cross-industry insights. For example, the data source analysis unit inputs data sources and the generation AI analyzes data from different industries. For example, data from the manufacturing and medical industries is integrated to perform risk assessment. The data source analysis unit can also obtain cross-industry insights based on data from different industries. The data source analysis unit can also integrate data from different industries, evaluate risk factors between industries, and make predictions based on the evaluation. This makes it possible to integrate data from different industries to obtain cross-industry insights.

[0067] The data source analysis unit can use the emotion estimation function to collect employee emotion data in real time and perform analysis according to changes in emotion. For example, the data source analysis unit inputs a data source, and the generation AI uses the emotion estimation function to collect employee emotion data in real time. For example, the risk of an accident occurring is evaluated according to changes in emotion. The data source analysis unit can also use the emotion estimation function to collect employee emotion data in real time and perform analysis based on that. The data source analysis unit can also analyze changes in emotion based on the emotion data and perform risk assessment based on that. This makes it possible to collect employee emotion data in real time and perform analysis according to changes in emotion.

[0068] The data source analysis unit can integrate different data formats and perform multimodal analysis. For example, the data source analysis unit inputs a data source and the generation AI analyzes the different data formats. For example, text data and image data are integrated to evaluate the risk of an accident occurring. The data source analysis unit can also perform multimodal analysis based on different data formats. The data source analysis unit can also integrate text data, image data, audio data, etc., and perform risk evaluation based on that. This makes it possible to integrate different data formats and perform multimodal analysis.

[0069] The time and social situation analysis unit can use the emotion estimation function to perform risk assessment taking into account the emotional trends of society as a whole. For example, the time and social situation analysis unit inputs data on the time and social situation, and the generation AI uses the emotion estimation function to analyze the emotional trends of society as a whole. For example, a risk assessment is performed based on the emotional trends of society as a whole. The time and social situation analysis unit can also use the emotion estimation function to perform risk assessment taking into account the emotional trends of society as a whole. The time and social situation analysis unit can also perform risk assessment based on the emotional trends and propose countermeasures based on the risk assessment. This makes it possible to perform risk assessment taking into account the emotional trends of society as a whole.

[0070] The time and social situation analysis unit can integrate data on abnormal weather and natural disasters and make predictions that take environmental risks into account. For example, the time and social situation analysis unit inputs data on the time and social situation, and the generation AI analyzes the data on abnormal weather and natural disasters. For example, the risk of an accident is predicted based on the risk of abnormal weather and natural disasters. The time and social situation analysis unit can also integrate data on abnormal weather and natural disasters and make predictions that take environmental risks into account based on that data. The time and social situation analysis unit can also evaluate environmental risks based on data on abnormal weather and natural disasters and make predictions based on that. This makes it possible to integrate data on abnormal weather and natural disasters and make predictions that take environmental risks into account.

[0071] The time and social situation analysis unit can analyze the impact of political and economic events and reflect this in risk assessment. For example, the time and social situation analysis unit inputs data on time and social situations, and the generation AI analyzes the impact of political and economic events. For example, the risk of an accident occurring is predicted based on the impact of political events. The time and social situation analysis unit can also perform risk assessment based on the impact of political and economic events. The time and social situation analysis unit can also analyze the impact of political and economic events and perform risk assessment based on that. This makes it possible to analyze the impact of political and economic events and reflect this in risk assessment.

[0072] The time and social situation analysis unit can integrate data from different regions and perform risk assessments from a global perspective. For example, the time and social situation analysis unit inputs data on time and social situations, and the generation AI analyzes the data from different regions. For example, it performs risk assessments that take into account risk factors for each region. The time and social situation analysis unit can also perform global risk assessments based on data from different regions. The time and social situation analysis unit can also integrate data from different regions, evaluate risk factors for each region, and perform risk assessments based on that. This makes it possible to integrate data from different regions and perform risk assessments from a global perspective.

[0073] The time and social situation analysis unit can use the emotion estimation function to monitor emotional trends across society in real time and perform risk assessments in response to changes in emotions. For example, the time and social situation analysis unit inputs data on time and social situations, and the generation AI uses the emotion estimation function to monitor emotional trends across society in real time. For example, it performs risk assessments in response to changes in emotions. The time and social situation analysis unit can also use the emotion estimation function to monitor emotional trends across society in real time and perform risk assessments based on the trends. The time and social situation analysis unit can also perform risk assessments in response to changes in emotions based on the emotional trends. This makes it possible to monitor emotional trends across society in real time and perform risk assessments in response to changes in emotions.

[0074] The time and social situation analysis unit can integrate different data formats and perform multimodal analysis. For example, the time and social situation analysis unit inputs data on time and social situations, and the generation AI analyzes the different data formats. For example, text data and image data are integrated to perform risk assessment. The time and social situation analysis unit can also perform multimodal analysis based on different data formats. The time and social situation analysis unit can also integrate text data, image data, audio data, etc., and perform risk assessment based on that. This makes it possible to integrate different data formats and perform multimodal analysis.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The accident response measure suggestion unit can estimate the emotions of employees and, based on the estimated emotions, suggest measures to reduce the psychological stress of employees. For example, it can monitor the emotional state of employees in real time after an accident and suggest counseling or relaxation methods to reduce stress. It can also suggest leave systems or adjustments to working hours to reduce stress based on employee emotional data. It can also suggest appropriate communication methods taking into account the emotional state of employees. This makes it possible to suggest measures to reduce the psychological stress of employees.

[0077] The recurrence prevention measures presentation unit can analyze employee emotional data using the emotion estimation function and propose recurrence prevention measures based on changes in emotion. For example, based on employee emotional data, it can evaluate the impact that emotional changes have on the recurrence of accidents and propose an educational program based on that. It can also analyze the emotional data to propose process improvements based on the employee's emotional state. Furthermore, it can propose recurrence prevention measures that take into account the employee's emotional state based on the emotional data. This makes it possible to analyze employee emotional data and propose recurrence prevention measures based on changes in emotion.

[0078] The accident prediction unit can use the emotion estimation function to make predictions that take into account employees' stress levels and fatigue levels. For example, it inputs work status and schedules and analyzes employees' stress levels and fatigue levels. It predicts that the risk of accidents increases when employees' stress levels are high. It can also evaluate employees' fatigue levels and predict the risk of accidents based on that. Furthermore, it can make predictions that take into account employees' stress levels and fatigue levels based on employees' emotion data. This makes it possible to make predictions that take into account employees' stress levels and fatigue levels.

[0079] The accident prediction unit can use the emotion estimation function to monitor the emotional state of employees in real time and make predictions according to changes in their emotions. For example, work status and schedules can be input and the emotional state of employees can be monitored in real time. The risk of an accident occurring can be predicted according to changes in emotions. It can also analyze changes in emotions based on employee emotional data and make predictions based on these. It can also monitor the emotional state of employees in real time and make predictions according to changes in emotions. This makes it possible to monitor the emotional state of employees in real time and make predictions according to changes in emotions.

[0080] The data source analysis unit can use the emotion estimation function to perform multidimensional analysis including employee emotion data. For example, it inputs a data source and analyzes employee emotion data. It evaluates the risk of an accident based on the emotion data and proposes countermeasures. It can also perform multidimensional analysis of the emotion data and perform risk assessment based on that. Furthermore, it can predict the risk of an accident based on the multidimensional analysis including the emotion data and propose countermeasures. This makes it possible to perform multidimensional analysis including employee emotion data.

[0081] The accident prediction unit employs an anomaly detection algorithm to detect behavior that deviates from normal work patterns at an early stage. For example, work status and schedules are input, and an anomaly detection algorithm is used to detect behavior that deviates from normal work patterns. Abnormal behavior is detected early and the risk of an accident is predicted. It is also possible to analyze changes in work patterns and predict the risk of an accident based on that. Deviations from work patterns can also be monitored in real time and detected at an early stage. This allows for the early detection of behavior that deviates from normal work patterns.

[0082] The accident prediction unit can integrate past accident data and real-time business data to perform dynamic risk assessment. For example, past accident data and real-time business data are input and integrated to perform dynamic risk assessment. Current risks are evaluated based on the past data, and accident predictions are made. In addition, by integrating past accident data and real-time business data to perform risk assessment, more accurate predictions can be made. Furthermore, based on dynamic risk assessment, accident risk can also be monitored and predicted in real time. This makes it possible to integrate past accident data and real-time business data to perform dynamic risk assessment.

[0083] The accident prediction unit can integrate data from different regions and cultural spheres and make predictions from a global perspective. For example, it inputs data from different regions and cultural spheres, integrates them, and makes accident predictions from a global perspective. It makes predictions that take into account risk factors for each region. It can also make global risk assessments based on data from different regions and cultural spheres. It can also integrate data from different regions and cultural spheres, evaluate risk factors for each region, and make predictions based on that. This makes it possible to integrate data from different regions and cultural spheres and make predictions from a global perspective.

[0084] The data source analysis unit can analyze the correlations between different data sources and build a more accurate accident prediction model. For example, it inputs data sources and analyzes the correlations between the different data sources. It builds an accident prediction model by integrating past accident data with real-time business data. It can also improve the accuracy of the accident prediction model based on the correlations between different data sources. It can also analyze the correlations, build an accident prediction model, and perform risk assessments based on that. This makes it possible to analyze the correlations between different data sources and build a more accurate accident prediction model.

[0085] The seasonal and social situation analysis unit can integrate data on abnormal weather and natural disasters and make predictions that take environmental risks into account. For example, it inputs data on seasonal and social situations and analyzes data on abnormal weather and natural disasters. It predicts the risk of accidents based on the risk of abnormal weather and natural disasters. It can also integrate data on abnormal weather and natural disasters and make predictions that take environmental risks into account based on that. Furthermore, it can evaluate environmental risks based on data on abnormal weather and natural disasters and make predictions based on that. This makes it possible to integrate data on abnormal weather and natural disasters and make predictions that take environmental risks into account.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The accident response measure suggestion component suggests optimal response measures for an accident that has already occurred. For example, it uses generative AI to analyze detailed information about the accident and propose emergency response procedures. It can also suggest ways to optimize resource allocation and evaluate the scope of the accident's impact to propose appropriate countermeasures. Step 2: The recurrence prevention measures presentation component presents recurrence prevention measures based on the response measures presented by the incident response measures presentation component. For example, it can use generative AI to analyze past accident data, propose training programs and process improvements, and identify the causes of accidents and present recurrence prevention measures based on those. Step 3: The accident prediction unit predicts accidents that are likely to occur in the future. For example, it uses generative AI to analyze past data and perform risk assessment. It can also link with work management tools and schedules to predict accident risks, taking into account the time of year and social conditions. Step 4: The countermeasure proposal unit proposes countermeasures for the accidents predicted by the accident prediction unit. For example, the generation AI can be used to propose technical and organizational countermeasures, and specific measures to reduce the risk of accidents occurring can be proposed.

[0088] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0094] 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.

[0095] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0096] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0097] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0098] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0099] 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.

[0100] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] 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.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] 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.

[0114] 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.

[0115] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

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

[0124] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0128] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0129] 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.

[0130] 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.

[0131] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0138] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0139] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0140] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0141] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0142] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0143] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0144] 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.

[0145] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0146] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0147] 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.

[0148] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0149] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0150] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0151] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0152] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0153] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0154] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0155] 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. an accident response measure suggestion unit that suggests optimal response measures for an accident that has occurred; a recurrence prevention measure presentation unit that presents a recurrence prevention measure based on the response measure presented by the accident response measure presentation unit; an accident occurrence prediction unit that predicts accidents that are likely to occur in the future; a countermeasure presentation unit that presents countermeasures for the accident predicted by the accident occurrence prediction unit. A system characterized by:

2. The accident response measure presentation unit Propose measures to reduce psychological stress among employees 2. The system of claim 1.

3. The recurrence prevention measure presentation unit Using machine learning algorithms to identify patterns of similar past accidents and generate measures to prevent recurrence 2. The system of claim 1.

4. The accident occurrence prediction unit Implement anomaly detection algorithms to quickly detect deviations from normal business patterns 2. The system of claim 1.

5. The data source analysis section Conduct multidimensional analysis including employee sentiment data 2. The system of claim 1.

6. The Times and Social Situation Analysis Department Conduct risk assessments that take into account the sentiment trends of society as a whole 2. The system of claim 1.

7. The accident occurrence prediction unit The above predictions take into account employee stress levels and fatigue levels 2. The system of claim 1.

8. The accident occurrence prediction unit Monitor employees' emotional states in real time and make predictions based on changes in their emotions 2. The system of claim 1.

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