system

Abstract

The system according to the embodiment aims to quickly and effectively identify the cause of a product accident when it occurs, develop an appropriate analysis method, and prepare a report for submission to government agencies. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a cause identification unit, an analysis method planning unit, and a report creation unit. The collection unit collects accident information. The analysis unit analyzes the information collected by the collection unit. The cause identification unit identifies a probable cause based on the information analyzed by the analysis unit. The analysis method planning unit plans an effective analysis method based on the cause identified by the cause identification unit. The report creation unit creates a report for government agencies based on the analysis method planned by the analysis method planning unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, when a product accident occurs, it is difficult to quickly and effectively identify the cause, devise an appropriate analysis method, and prepare a report for government agencies.

[0005] The system according to the embodiment aims to quickly and effectively identify the cause of a product accident when it occurs, develop an appropriate analysis method, and prepare a report for submission to government agencies. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a cause identification unit, an analysis method planning unit, and a report creation unit. The collection unit collects accident information. The analysis unit analyzes the information collected by the collection unit. The cause identification unit identifies a probable cause based on the information analyzed by the analysis unit. The analysis method planning unit plans an effective analysis method based on the cause identified by the cause identification unit. The report creation unit creates a report for government agencies based on the analysis method planned by the analysis method planning unit. [Effects of the Invention]

[0007] When a product accident occurs, the system according to the embodiment can quickly and effectively identify the cause, develop an appropriate analysis method, and prepare a report for submission to government agencies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An accident response system according to an embodiment of the present invention is a system for responding quickly and effectively to serious product accidents, such as fires and smoke caused by product defects or malfunctions. This accident response system collects accident information, inputs necessary information, and then proposes a suspected cause and an effective analysis method. Based on this information, the system prepares a report for submission to government agencies. For example, when collecting accident information, detailed information such as the location and time of the accident, the extent of the damage, and the product's model number and serial number are input. For example, in the case of a fire, the location and time of the fire, the extent of the damage, and the model number and serial number of the product being used are input. This information is entered into the system and serves as the basis for analysis. The system then analyzes the input information. The system identifies a suspected cause based on past accident data, product design information, and usage conditions. For example, if similar accidents have occurred in the past, the system references the accident data to identify common causes. Furthermore, the system analyzes the product's design information and usage conditions to identify design issues or incorrect usage. Furthermore, the system proposes an effective analysis method based on the suspected cause. For example, it proposes an analysis of each product component or a reproduction experiment of the usage environment. This allows for more accurate identification of the cause of the accident. Finally, a report is prepared for submission to government agencies based on the proposed analysis method. The report includes detailed information about the accident, the suspected cause, the analysis method, and future countermeasures. This enables prompt and accurate reporting, allowing measures to be taken to prevent recurrence. This system makes it possible to respond quickly and effectively even in the event of a serious product accident caused by a product defect or malfunction. By consistently performing the process from collecting accident information to analyzing it and preparing the report, it is possible to improve the efficiency and accuracy of accident responses. As a result, the accident response system allows for prompt and effective responses even in the event of a serious product accident caused by a product defect or malfunction.

[0029] An accident response system according to an embodiment includes a collection unit, an analysis unit, a cause identification unit, an analysis method planning unit, and a report creation unit. The collection unit collects accident information. The collection unit collects detailed information, such as the location and time of the accident, the extent of the damage, and the product model number and serial number. For example, the collection unit can manually input the information at the accident site or automatically acquire the information from a database. The collection unit can also collect photos and videos of the accident site. For example, the collection unit can take photos of the accident site and upload them to the system. The collection unit can also record videos of the accident site and store them in the system. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information based on, for example, past accident data, product design information, usage status, and the like. For example, the analysis unit refers to a past accident database to identify the cause of similar accidents. The analysis unit can also analyze product design information to identify design problems. The analysis unit can also analyze product usage status to identify incorrect usage. The cause identification unit identifies a suspected cause based on the information analyzed by the analysis unit. The cause identification unit identifies a suspected cause based on, for example, past accident data, product design information, usage conditions, etc. For example, the cause identification unit refers to past accident data to identify common causes. The cause identification unit can also analyze product design information to identify design problems. Furthermore, the cause identification unit can analyze product usage conditions to identify incorrect usage. The analysis method planning unit plans an effective analysis method based on the causes identified by the cause identification unit. The analysis method planning unit proposes, for example, an analysis of each product component or an experiment reproducing the usage environment. For example, the analysis method planning unit disassembles the product components and analyzes each component. The analysis method planning unit can also reproduce the usage environment and conduct an experiment. Furthermore, the analysis method planning unit can perform analysis using a simulation. The report creation unit creates a report to a government agency based on the analysis method planned by the analysis method planning unit. The report preparation unit prepares a report including, for example, detailed information about the accident, a presumed cause, an analysis method, future measures, etc. For example, the report preparation unit writes detailed information about the accident and explains the presumed cause.The report creation unit can also describe the analysis method and propose future countermeasures. As a result, the accident response system according to the embodiment can improve the efficiency and accuracy of accident response by consistently performing the process from collecting accident information to analyzing it and creating a report.

[0030] The collection unit can collect multiple pieces of information, including the location and time of the accident, the damage situation, and the product model number or serial number. For example, the collection unit collects the location of the accident as GPS data. For example, the collection unit obtains the coordinates of the accident site and stores them in the system. The collection unit can also record the time of the accident as a timestamp. For example, the collection unit can record the exact time the accident occurred and store it in the system. Furthermore, the collection unit can collect the damage situation as photos and videos. For example, the collection unit can take photos of the accident site and upload them to the system. The collection unit can also record videos of the accident site and store them in the system. This allows for the collection of detailed accident information to enrich the basic data for analysis.

[0031] The analysis unit can analyze information based on past accident data, product design information, usage status, etc. The analysis unit, for example, refers to a past accident database to identify the cause of similar accidents. For example, the analysis unit searches past accident data to identify common causes. The analysis unit can also analyze product design information to identify design problems. For example, the analysis unit can analyze product design drawings to identify design defects. Furthermore, the analysis unit can analyze product usage status to identify incorrect usage. For example, the analysis unit can analyze the product's usage history to identify incorrect usage. This makes it possible to more accurately identify the cause by analyzing based on past data and design information.

[0032] The cause identification unit can identify a suspected cause based on past accident data, product design information, usage status, etc. The cause identification unit, for example, refers to past accident data to identify common causes. For example, the cause identification unit searches past accident data to identify the causes of similar accidents. The cause identification unit can also analyze product design information to identify design problems. For example, the cause identification unit analyzes product design drawings to identify design defects. Furthermore, the cause identification unit can analyze product usage status to identify incorrect usage. For example, the cause identification unit analyzes the product's usage history to identify incorrect usage. In this way, by identifying the cause based on past data and design information, measures to prevent recurrence can be taken.

[0033] The analysis method planning unit can propose an analysis of each component of the product or an experiment to reproduce the usage environment. For example, the analysis method planning unit disassembles the components of the product and analyzes each component. For example, the analysis method planning unit disassembles the components of the product and analyzes the material properties of each component. The analysis method planning unit can also reproduce the usage environment and conduct an experiment. For example, the analysis method planning unit can identify the cause of the accident by reproducing the usage environment of the product and conducting an experiment. Furthermore, the analysis method planning unit can also perform an analysis using a simulation. For example, the analysis method planning unit can use a simulation to reproduce the operation of the product and identify the cause of the accident. By proposing an analysis of each component or an experiment to reproduce the product, the cause of the accident can be more accurately identified.

[0034] The report creation unit can create a report that includes detailed information about the accident, the suspected cause, an analysis method, future measures, etc. The report creation unit, for example, describes detailed information about the accident and explains the suspected cause. For example, the report creation unit describes detailed information such as the location of the accident, the time of the accident, and the extent of the damage. The report creation unit can also explain the suspected cause and describe the analysis method. For example, the report creation unit can explain the suspected cause and describe the analysis method. Furthermore, the report creation unit can also propose future measures. For example, the report creation unit can propose future measures and take measures to prevent recurrence. In this way, creating a detailed report enables quick and accurate reporting, and measures to prevent recurrence can be taken.

[0035] When collecting accident information, the collection unit can simultaneously collect environmental sounds and videos from the scene to provide more detailed information. For example, the collection unit collects audio from the accident scene and records the situation at the time of the accident in detail. For example, the collection unit collects audio from the accident scene using a microphone and stores it in a system. The collection unit can also collect video from the accident scene to provide visual information. For example, the collection unit captures video from the accident scene using a camera and stores it in a system. Furthermore, the collection unit can synchronize and collect environmental sounds and videos to enable a more accurate understanding of the situation. For example, the collection unit synchronizes and collects environmental sounds and videos and stores them in a system. In this way, by collecting environmental sounds and videos, the detailed situation of the accident can be understood. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the collected audio data and video data into a generation AI and have the generation AI analyze the data.

[0036] When collecting accident information, the collection unit can collect victim testimony and witness testimony via audio input. The collection unit, for example, collects victim testimony via audio input and records detailed information. For example, the collection unit records victim testimony via a microphone and saves it in a system. The collection unit can also collect witness testimony via audio input to understand the circumstances of the accident. For example, the collection unit records witness testimony via a microphone and saves it in a system. Furthermore, the collection unit can simultaneously collect testimonies from both the victim and witness to obtain information from multiple perspectives. For example, the collection unit simultaneously records testimonies from the victim and witness and saves them in a system. In this way, detailed information can be obtained by collecting victim and witness testimony via audio input. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input recorded audio data into a generation AI and have the generation AI convert it into text data.

[0037] When collecting accident information, the collection unit can simultaneously collect environmental data such as the temperature and humidity at the accident site. For example, the collection unit collects the temperature at the accident site to identify the cause of the fire. For example, the collection unit measures the temperature at the accident site with a sensor and stores it in a system. The collection unit can also collect humidity at the accident site to understand the environmental conditions. For example, the collection unit measures the humidity at the accident site with a sensor and stores it in a system. Furthermore, the collection unit can simultaneously collect temperature and humidity to provide comprehensive environmental data. For example, the collection unit simultaneously measures temperature and humidity and stores it in a system. In this way, by collecting environmental data such as temperature and humidity, the cause of the accident can be more accurately identified. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the collected environmental data to a generation AI and have the generation AI analyze the data.

[0038] The collection unit can automatically collect related information on social media when collecting accident information. The collection unit, for example, collects posts related to the accident on social media. For example, the collection unit searches for posts on social media using specific hashtags or keywords and saves them in the system. The collection unit can also collect images and videos related to the accident from social media. For example, the collection unit downloads images and videos from social media and saves them in the system. The collection unit can also collect eyewitness information on social media to understand details of the accident. For example, the collection unit searches for eyewitness information on social media and saves it in the system. In this way, by collecting information on social media, detailed circumstances of the accident can be understood. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data posted on social media into a generation AI and have the generation AI analyze the data.

[0039] During analysis, the analysis unit can evaluate the reliability of the collected data and exclude low-reliability data. The analysis unit, for example, checks the source of the data and excludes low-reliability data. For example, the analysis unit checks the source of the data and excludes low-reliability data. The analysis unit can also check the consistency of the data and exclude contradictory data. For example, the analysis unit checks the consistency of the data and excludes contradictory data. Furthermore, the analysis unit can score the reliability of the data and exclude data with a low score. For example, the analysis unit scores the reliability of the data and excludes data with a low score. This eliminates low-reliability data, thereby improving the accuracy of the analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI evaluate the reliability of the data.

[0040] During analysis, the analysis unit can execute different analysis algorithms in parallel and select the most appropriate result. For example, the analysis unit can execute multiple analysis algorithms simultaneously and compare the results. The analysis unit can also evaluate the results of each algorithm and select the most reliable result. For example, the analysis unit can evaluate the results of each algorithm and select the most reliable result. Furthermore, the analysis unit can integrate the results of different algorithms and provide a comprehensive analysis result. For example, the analysis unit can integrate the results of different algorithms and provide a comprehensive analysis result. This allows the execution of different analysis algorithms in parallel to obtain the most appropriate analysis result. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input multiple analysis algorithms into a generation AI and have the generation AI evaluate and select the results.

[0041] During analysis, the analysis unit can link with a database of other similar accidents and perform analysis based on a wider range of data. The analysis unit, for example, links with a database of similar accidents and refers to past cases. For example, the analysis unit links with a database of similar accidents and refers to past cases. The analysis unit can also identify common causes based on other accident data. For example, the analysis unit can identify common causes based on other accident data. Furthermore, the analysis unit can integrate data of similar accidents and perform a comprehensive analysis. For example, the analysis unit integrates data of similar accidents and performs a comprehensive analysis. In this way, by linking with databases of other similar accidents, analysis can be performed based on a wider range of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of other similar accidents into the generation AI and have the generation AI analyze the data.

[0042] The analysis unit updates data in real time during analysis and can perform analysis based on the latest information. The analysis unit, for example, reflects data collected in real time in the analysis. For example, the analysis unit reflects data collected in real time in the analysis. The analysis unit can also update the analysis results in real time in response to data updates. For example, the analysis unit updates the analysis results in real time in response to data updates. Furthermore, the analysis unit can also provide analysis results based on the latest information. For example, the analysis unit provides analysis results based on the latest information. In this way, by updating data in real time, analysis can be performed based on the latest information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data updated in real time to the generation AI and have the generation AI analyze the data.

[0043] When identifying a cause, the cause identification unit can formulate multiple hypotheses and perform verification based on each hypothesis. For example, the cause identification unit formulates multiple hypotheses and verifies data based on each hypothesis. The cause identification unit can also evaluate the reliability of each hypothesis and select the most reliable hypothesis. For example, the cause identification unit evaluates the reliability of each hypothesis and selects the most reliable hypothesis. Furthermore, the cause identification unit can also perform verification using a different analysis method for each hypothesis. For example, the cause identification unit performs verification using a different analysis method for each hypothesis. In this way, by formulating and verifying multiple hypotheses, the most reliable cause can be identified. Some or all of the above-described processing in the cause identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the cause identification unit can input multiple hypotheses to a generation AI and have the generation AI verify the hypotheses.

[0044] During cause identification, the cause identification unit can investigate the product's manufacturing process or distribution process and identify potential problems. For example, the cause identification unit can investigate the product's manufacturing process in detail and identify manufacturing problems. The cause identification unit can also investigate the product's distribution process and identify distribution problems. For example, the cause identification unit can investigate the product's distribution process and identify distribution problems. The cause identification unit can also investigate the manufacturing process and distribution process in an integrated manner and identify potential problems. For example, the cause identification unit can investigate the manufacturing process and distribution process in an integrated manner and identify potential problems. In this way, potential problems can be identified by investigating the manufacturing process or distribution process in detail. Some or all of the above-mentioned processing in the cause identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the cause identification unit can input data on the manufacturing process or distribution process into the generation AI and have the generation AI analyze the data.

[0045] When identifying causes, the cause identification unit can refer to data on other products or similar products to identify common problems. The cause identification unit, for example, refers to data on other products to identify common problems. For example, the cause identification unit refers to data on other products to identify common problems. The cause identification unit can also refer to data on similar products to identify common problems. For example, the cause identification unit refers to data on similar products to identify common problems. Furthermore, the cause identification unit can integrate data on other products and similar products to identify comprehensive problems. For example, the cause identification unit integrates data on other products and similar products to identify comprehensive problems. In this way, common problems can be identified by referring to data on other products or similar products. Some or all of the above-described processing in the cause identification unit may be performed using, or without, AI. For example, the cause identification unit can input data on other products or similar products into the generation AI and have the generation AI analyze the data.

[0046] When identifying causes, the cause identification unit can identify causes by taking into account the product usage environment and the user's behavioral patterns. For example, the cause identification unit can identify causes caused by the environment by taking into account the product usage environment. The cause identification unit can also identify causes caused by behavior by taking into account the user's behavioral patterns. For example, the cause identification unit can identify causes caused by behavior by taking into account the user's behavioral patterns. Furthermore, the cause identification unit can identify a comprehensive cause by taking into account the usage environment and behavioral patterns in an integrated manner. For example, the cause identification unit can identify a comprehensive cause by taking into account the usage environment and behavioral patterns in an integrated manner. In this way, a comprehensive cause can be identified by taking into account the usage environment and behavioral patterns. Some or all of the above-described processing in the cause identification unit may be performed using, or without, AI. For example, the cause identification unit can input data on the usage environment and behavioral patterns into a generation AI and have the generation AI analyze the data.

[0047] When planning an analysis method, the analysis method planning unit can refer to past examples of successful and unsuccessful analysis methods and select the optimal method. The analysis method planning unit, for example, refers to past successful examples and proposes a similar analysis method. For example, the analysis method planning unit refers to past successful examples and proposes a similar analysis method. The analysis method planning unit can also refer to past unsuccessful examples and propose an analysis method that avoids failure. For example, the analysis method planning unit refers to past unsuccessful examples and proposes an analysis method that avoids failure. Furthermore, the analysis method planning unit can comprehensively refer to successful and unsuccessful examples and select the optimal analysis method. For example, the analysis method planning unit comprehensively refers to successful and unsuccessful examples and selects the optimal analysis method. In this way, the optimal analysis method can be selected by referring to past successful and unsuccessful examples. Some or all of the above-mentioned processing in the analysis method planning unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis method planning unit can input data of past analysis methods into a generation AI and have the generation AI analyze the data.

[0048] When planning an analysis method, the analysis method planning unit can propose an analysis method customized according to the product characteristics and usage environment. The analysis method planning unit, for example, proposes an analysis method according to the product characteristics. For example, the analysis method planning unit proposes an analysis method according to the product characteristics. The analysis method planning unit can also propose an analysis method according to the usage environment. For example, the analysis method planning unit proposes an analysis method according to the usage environment. Furthermore, the analysis method planning unit can propose a customized analysis method by comprehensively considering the product characteristics and the usage environment. For example, the analysis method planning unit proposes a customized analysis method by comprehensively considering the product characteristics and the usage environment. This enables more appropriate analysis by proposing an analysis method according to the product characteristics and the usage environment. Some or all of the above-mentioned processing in the analysis method planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis method planning unit can input data on the product characteristics and usage environment into the generation AI and have the generation AI analyze the data.

[0049] When planning an analysis method, the analysis method planning unit can propose an analysis method by incorporating the opinions and advice of other experts. The analysis method planning unit, for example, collects the opinions of other experts and reflects them in the analysis method. For example, the analysis method planning unit collects the opinions of other experts and reflects them in the analysis method. The analysis method planning unit can also adjust the analysis method based on the advice of experts. For example, the analysis method planning unit adjusts the analysis method based on the advice of experts. Furthermore, the analysis method planning unit can integrate the opinions of multiple experts and propose an optimal analysis method. For example, the analysis method planning unit integrates the opinions of multiple experts and proposes an optimal analysis method. In this way, by incorporating the opinions and advice of other experts, a more reliable analysis method can be proposed. Some or all of the above-mentioned processing in the analysis method planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis method planning unit can input the opinions and advice of experts into the generation AI and cause the generation AI to analyze the data.

[0050] When planning an analysis method, the analysis method planning unit can propose an analysis method that incorporates the latest technologies and techniques. The analysis method planning unit, for example, proposes a method that incorporates the latest analysis technology. For example, the analysis method planning unit proposes a method that incorporates the latest analysis technology. The analysis method planning unit can also propose an analysis method that uses a new technique. For example, the analysis method planning unit proposes an analysis method that uses a new technique. Furthermore, the analysis method planning unit can integrate the latest technologies and techniques to propose an optimal analysis method. For example, the analysis method planning unit integrates the latest technologies and techniques to propose an optimal analysis method. This makes it possible to propose a more effective analysis method by incorporating the latest technologies and techniques. Some or all of the above-mentioned processing in the analysis method planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis method planning unit can input data on the latest technologies and techniques into the generation AI and have the generation AI analyze the data.

[0051] When creating a report, the report creation unit can refer to the format and content of past reports to create an optimal report. For example, the report creation unit refers to the format of past reports and creates a report in a similar format. For example, the report creation unit refers to the format of past reports and creates a report in a similar format. The report creation unit can also refer to the content of past reports to create a report including necessary information. For example, the report creation unit refers to the content of past reports and creates a report including necessary information. Furthermore, the report creation unit can also create an optimal report based on successful examples of past reports. For example, the report creation unit creates an optimal report based on successful examples of past reports. In this way, an optimal report can be created by referring to past reports. Some or all of the above-mentioned processing in the report creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the report creation unit can input data of past reports into a generation AI and have the generation AI analyze the data.

[0052] When creating a report, the report creation unit can create a customized report in accordance with the request of a government agency. The report creation unit, for example, creates a report in a format in accordance with the request of a government agency. For example, the report creation unit creates a report in a format in accordance with the request of a government agency. The report creation unit can also create a report including necessary information based on the request of a government agency. For example, the report creation unit creates a report including necessary information based on the request of a government agency. The report creation unit can also create a customized report in accordance with the request of a government agency. For example, the report creation unit creates a customized report in accordance with the request of a government agency. This enables quick and accurate reporting by creating a report in accordance with the request of a government agency. Some or all of the above-mentioned processing in the report creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the report creation unit can input data requested by a government agency into a generation AI and have the generation AI create a report.

[0053] When creating a report, the report creation unit can refer to other related documents and data to enrich the content of the report. For example, the report creation unit can refer to other related documents and include necessary information in the report. For example, the report creation unit can refer to other related documents and include necessary information in the report. The report creation unit can also refer to related data to enrich the content of the report. For example, the report creation unit can refer to related data to enrich the content of the report. Furthermore, the report creation unit can integrate other documents and data to create a comprehensive report. For example, the report creation unit can integrate other documents and data to create a comprehensive report. In this way, the content of the report can be enriched by referring to other related documents and data. Some or all of the above-mentioned processing in the report creation unit may be performed using AI, for example, or may be performed without using AI. For example, the report creation unit can input other related documents and data into a generation AI and have the generation AI analyze the data.

[0054] When creating a report, the report creation unit can adjust the format depending on the destination and method of submission of the report. For example, the report creation unit creates the report in a format according to the request of the destination. For example, the report creation unit creates the report in a format according to the request of the destination. The report creation unit can also create the report in an appropriate format depending on the submission method. For example, the report creation unit creates the report in an appropriate format depending on the submission method. Furthermore, the report creation unit can also create the report in an optimal format taking into account the destination and submission method. For example, the report creation unit creates the report in an optimal format taking into account the destination and submission method. In this way, by creating the report in a format according to the destination and submission method, it is possible to create a report that meets the request of the destination. Some or all of the above-mentioned processing in the report creation unit may be performed using, or without, AI. For example, the report creation unit can input data on the destination and submission method into a generation AI and have the generation AI adjust the format.

[0055] During analysis, the analysis unit can evaluate the reliability of the collected data and exclude low-reliability data. The analysis unit, for example, checks the source of the data and excludes low-reliability data. For example, the analysis unit checks the source of the data and excludes low-reliability data. The analysis unit can also check the consistency of the data and exclude contradictory data. For example, the analysis unit checks the consistency of the data and excludes contradictory data. Furthermore, the analysis unit can score the reliability of the data and exclude data with a low score. For example, the analysis unit scores the reliability of the data and excludes data with a low score. This eliminates low-reliability data, thereby improving the accuracy of the analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI evaluate the reliability of the data.

[0056] During analysis, the analysis unit can execute different analysis algorithms in parallel and select the most appropriate result. For example, the analysis unit can execute multiple analysis algorithms simultaneously and compare the results. The analysis unit can also evaluate the results of each algorithm and select the most reliable result. For example, the analysis unit can evaluate the results of each algorithm and select the most reliable result. Furthermore, the analysis unit can integrate the results of different algorithms and provide a comprehensive analysis result. For example, the analysis unit can integrate the results of different algorithms and provide a comprehensive analysis result. This allows the execution of different analysis algorithms in parallel to obtain the most appropriate analysis result. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input multiple analysis algorithms into a generation AI and have the generation AI evaluate and select the results.

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

[0058] The accident response system can also be equipped with a real-time data collection function. For example, the collection unit can acquire data in real time from sensors at the accident site and immediately reflect it in the system. The collection unit can also collect aerial footage of the accident site in real time using a drone. Furthermore, the collection unit can perform live streaming of the accident site, enabling monitoring from a remote location. This makes it possible to collect data in real time and respond quickly.

[0059] When analyzing accident information, the analysis unit can integrate information from different data sources and perform a comprehensive analysis. For example, the analysis unit can integrate product design information, usage status, environmental data, social media posts, etc. to more accurately identify the cause. The analysis unit can also analyze correlations between different data sources to identify potential problems. Furthermore, the analysis unit can evaluate the reliability of the data and prioritize analysis of highly reliable data. This enables comprehensive data analysis and more accurate cause identification.

[0060] The analysis method planning department can incorporate the opinions of experts in other fields when planning an analysis method. For example, it can collect opinions from experts in product design, use environments, and data analysis, and reflect them in the analysis method. The analysis method planning department can also integrate knowledge from different fields to propose more effective analysis methods. Furthermore, the analysis method planning department can identify areas for improvement in the analysis method based on the opinions of experts and select the optimal method. This makes it possible to plan an analysis method that utilizes knowledge from other fields.

[0061] When collecting accident information, the collection unit can simultaneously collect environmental data such as the temperature, humidity, and air pressure at the accident site. For example, the collection unit can measure the temperature at the accident site using a sensor and store the data in the system. The collection unit can also measure the humidity at the accident site using a sensor and store the data in the system. Furthermore, the collection unit can measure the air pressure to understand the environmental conditions. By collecting environmental data, the cause of the accident can be more accurately identified.

[0062] When identifying the cause, the cause identification unit can investigate the manufacturing process and distribution process of the product in detail to identify potential problems. For example, the cause identification unit can investigate the manufacturing process of the product in detail to identify manufacturing problems. The cause identification unit can also investigate the distribution process of the product to identify distribution problems. Furthermore, the cause identification unit can investigate the manufacturing process and distribution process in an integrated manner to identify potential problems. In this way, potential problems can be identified by investigating the manufacturing process and distribution process in detail.

[0063] When creating a report, the report creation department can refer to other related documents and data to enrich the content of the report. For example, the report creation department can refer to other related documents to include necessary information in the report. The report creation department can also refer to related data to enrich the content of the report. Furthermore, the report creation department can integrate other documents and data to create a comprehensive report. This allows the content of the report to be enriched by referring to other related documents and data.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects accident information. The collection unit collects detailed information such as the location and time of the accident, the extent of the damage, and the product model number and serial number. The collection unit can manually enter the information at the accident site or automatically retrieve it from a database. The collection unit can also collect photos and videos of the accident site. For example, the collection unit can take photos of the accident site and upload them to the system. It can also record videos of the accident site and save them in the system. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes information based on past accident data, product design information, usage status, etc. It refers to a database of past accidents to identify the causes of similar accidents. It can also analyze product design information to identify design problems. It can also analyze product usage status to identify incorrect usage. Step 3: The cause identification unit identifies the probable cause based on the information analyzed by the analysis unit. The cause identification unit identifies the probable cause based on, for example, past accident data, product design information, and usage conditions. It references past accident data to identify common causes. It can also analyze product design information to identify design problems. It can also analyze product usage conditions to identify incorrect usage. Step 4: The analysis method planning section plans an effective analysis method based on the causes identified by the cause identification section. For example, the analysis method planning section proposes an analysis of each product part or an experiment to reproduce the usage environment. The analysis method planning section disassembles the product parts and analyzes each part. It can also reproduce the usage environment and conduct experiments. It can also perform analysis using simulations. Step 5: The report preparation department prepares a report for the government agency based on the analysis method proposed by the analysis method planning department. The report preparation department prepares a report that includes, for example, detailed information about the accident, the suspected cause, the analysis method, and future measures. The report preparation department describes the detailed information about the accident and explains the suspected cause. It can also describe the analysis method and propose future measures.

[0066] (Example 2) An accident response system according to an embodiment of the present invention is a system for responding quickly and effectively to serious product accidents, such as fires and smoke caused by product defects or malfunctions. This accident response system collects accident information, inputs necessary information, and then proposes a suspected cause and an effective analysis method. Based on this information, the system prepares a report for submission to government agencies. For example, when collecting accident information, detailed information such as the location and time of the accident, the extent of the damage, and the product's model number and serial number are input. For example, in the case of a fire, the location and time of the fire, the extent of the damage, and the model number and serial number of the product being used are input. This information is entered into the system and serves as the basis for analysis. The system then analyzes the input information. The system identifies a suspected cause based on past accident data, product design information, and usage conditions. For example, if similar accidents have occurred in the past, the system references the accident data to identify common causes. Furthermore, the system analyzes the product's design information and usage conditions to identify design issues or incorrect usage. Furthermore, the system proposes an effective analysis method based on the suspected cause. For example, it proposes an analysis of each product component or a reproduction experiment of the usage environment. This allows for more accurate identification of the cause of the accident. Finally, a report is prepared for submission to government agencies based on the proposed analysis method. The report includes detailed information about the accident, the suspected cause, the analysis method, and future countermeasures. This enables prompt and accurate reporting, allowing measures to be taken to prevent recurrence. This system makes it possible to respond quickly and effectively even in the event of a serious product accident caused by a product defect or malfunction. By consistently performing the process from collecting accident information to analyzing it and preparing the report, it is possible to improve the efficiency and accuracy of accident responses. As a result, the accident response system allows for prompt and effective responses even in the event of a serious product accident caused by a product defect or malfunction.

[0067] An accident response system according to an embodiment includes a collection unit, an analysis unit, a cause identification unit, an analysis method planning unit, and a report creation unit. The collection unit collects accident information. The collection unit collects detailed information, such as the location and time of the accident, the extent of the damage, and the product model number and serial number. For example, the collection unit can manually input the information at the accident site or automatically acquire the information from a database. The collection unit can also collect photos and videos of the accident site. For example, the collection unit can take photos of the accident site and upload them to the system. The collection unit can also record videos of the accident site and store them in the system. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information based on, for example, past accident data, product design information, usage status, and the like. For example, the analysis unit refers to a past accident database to identify the cause of similar accidents. The analysis unit can also analyze product design information to identify design problems. The analysis unit can also analyze product usage status to identify incorrect usage. The cause identification unit identifies a suspected cause based on the information analyzed by the analysis unit. The cause identification unit identifies a suspected cause based on, for example, past accident data, product design information, usage conditions, etc. For example, the cause identification unit refers to past accident data to identify common causes. The cause identification unit can also analyze product design information to identify design problems. Furthermore, the cause identification unit can analyze product usage conditions to identify incorrect usage. The analysis method planning unit plans an effective analysis method based on the causes identified by the cause identification unit. The analysis method planning unit proposes, for example, an analysis of each product component or an experiment reproducing the usage environment. For example, the analysis method planning unit disassembles the product components and analyzes each component. The analysis method planning unit can also reproduce the usage environment and conduct an experiment. Furthermore, the analysis method planning unit can perform analysis using a simulation. The report creation unit creates a report to a government agency based on the analysis method planned by the analysis method planning unit. The report preparation unit prepares a report including, for example, detailed information about the accident, a presumed cause, an analysis method, future measures, etc. For example, the report preparation unit writes detailed information about the accident and explains the presumed cause.The report creation unit can also describe the analysis method and propose future countermeasures. As a result, the accident response system according to the embodiment can improve the efficiency and accuracy of accident response by consistently performing the process from collecting accident information to analyzing it and creating a report.

[0068] The collection unit can collect multiple pieces of information, including the location and time of the accident, the damage situation, and the product model number or serial number. For example, the collection unit collects the location of the accident as GPS data. For example, the collection unit obtains the coordinates of the accident site and stores them in the system. The collection unit can also record the time of the accident as a timestamp. For example, the collection unit can record the exact time the accident occurred and store it in the system. Furthermore, the collection unit can collect the damage situation as photos and videos. For example, the collection unit can take photos of the accident site and upload them to the system. The collection unit can also record videos of the accident site and store them in the system. This allows for the collection of detailed accident information to enrich the basic data for analysis.

[0069] The analysis unit can analyze information based on past accident data, product design information, usage status, etc. The analysis unit, for example, refers to a past accident database to identify the cause of similar accidents. For example, the analysis unit searches past accident data to identify common causes. The analysis unit can also analyze product design information to identify design problems. For example, the analysis unit can analyze product design drawings to identify design defects. Furthermore, the analysis unit can analyze product usage status to identify incorrect usage. For example, the analysis unit can analyze the product's usage history to identify incorrect usage. This makes it possible to more accurately identify the cause by analyzing based on past data and design information.

[0070] The cause identification unit can identify a suspected cause based on past accident data, product design information, usage status, etc. The cause identification unit, for example, refers to past accident data to identify common causes. For example, the cause identification unit searches past accident data to identify the causes of similar accidents. The cause identification unit can also analyze product design information to identify design problems. For example, the cause identification unit analyzes product design drawings to identify design defects. Furthermore, the cause identification unit can analyze product usage status to identify incorrect usage. For example, the cause identification unit analyzes the product's usage history to identify incorrect usage. In this way, by identifying the cause based on past data and design information, measures to prevent recurrence can be taken.

[0071] The analysis method planning unit can propose an analysis of each component of the product or an experiment to reproduce the usage environment. For example, the analysis method planning unit disassembles the components of the product and analyzes each component. For example, the analysis method planning unit disassembles the components of the product and analyzes the material properties of each component. The analysis method planning unit can also reproduce the usage environment and conduct an experiment. For example, the analysis method planning unit can identify the cause of the accident by reproducing the usage environment of the product and conducting an experiment. Furthermore, the analysis method planning unit can also perform an analysis using a simulation. For example, the analysis method planning unit can use a simulation to reproduce the operation of the product and identify the cause of the accident. By proposing an analysis of each component or an experiment to reproduce the product, the cause of the accident can be more accurately identified.

[0072] The report creation unit can create a report that includes detailed information about the accident, the suspected cause, an analysis method, future measures, etc. The report creation unit, for example, describes detailed information about the accident and explains the suspected cause. For example, the report creation unit describes detailed information such as the location of the accident, the time of the accident, and the extent of the damage. The report creation unit can also explain the suspected cause and describe the analysis method. For example, the report creation unit can explain the suspected cause and describe the analysis method. Furthermore, the report creation unit can also propose future measures. For example, the report creation unit can propose future measures and take measures to prevent recurrence. In this way, creating a detailed report enables quick and accurate reporting, and measures to prevent recurrence can be taken.

[0073] The collection unit can estimate the user's emotions and adjust the timing of collecting accident information based on the estimated user emotions. For example, if the user is nervous, the collection unit delays the collection timing to wait for the user to calm down. For example, if the user is nervous, the collection unit delays the collection timing. The collection unit can also immediately start collecting information if the user is relaxed. For example, if the user is relaxed, the collection unit immediately starts collecting information. Furthermore, if the user is impatient, the collection unit can quickly collect information to reduce the user's burden. For example, if the user is impatient, the collection unit quickly collects information to reduce the user's burden. This enables more appropriate information collection by adjusting the collection timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0074] When collecting accident information, the collection unit can simultaneously collect environmental sounds and videos from the scene to provide more detailed information. For example, the collection unit collects audio from the accident scene and records the situation at the time of the accident in detail. For example, the collection unit collects audio from the accident scene using a microphone and stores it in a system. The collection unit can also collect video from the accident scene to provide visual information. For example, the collection unit captures video from the accident scene using a camera and stores it in a system. Furthermore, the collection unit can synchronize and collect environmental sounds and videos to enable a more accurate understanding of the situation. For example, the collection unit synchronizes and collects environmental sounds and videos and stores them in a system. In this way, by collecting environmental sounds and videos, the detailed situation of the accident can be understood. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the collected audio data and video data into a generation AI and have the generation AI analyze the data.

[0075] When collecting accident information, the collection unit can collect victim testimony and witness testimony via audio input. The collection unit, for example, collects victim testimony via audio input and records detailed information. For example, the collection unit records victim testimony via a microphone and saves it in a system. The collection unit can also collect witness testimony via audio input to understand the circumstances of the accident. For example, the collection unit records witness testimony via a microphone and saves it in a system. Furthermore, the collection unit can simultaneously collect testimonies from both the victim and witness to obtain information from multiple perspectives. For example, the collection unit simultaneously records testimonies from the victim and witness and saves them in a system. In this way, detailed information can be obtained by collecting victim and witness testimony via audio input. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input recorded audio data into a generation AI and have the generation AI convert it into text data.

[0076] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, when the user is nervous, the collection unit prioritizes collecting important information. For example, when the user is nervous, the collection unit prioritizes collecting important information. The collection unit can also collect detailed information when the user is relaxed. For example, when the user is relaxed, the collection unit collects detailed information. Furthermore, when the user is impatient, the collection unit can prioritize information that needs to be collected quickly. For example, when the user is impatient, the collection unit prioritizes information that needs to be collected quickly. In this way, by determining the priority of information according to the user's emotions, important information can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0077] When collecting accident information, the collection unit can simultaneously collect environmental data such as the temperature and humidity at the accident site. For example, the collection unit collects the temperature at the accident site to identify the cause of the fire. For example, the collection unit measures the temperature at the accident site with a sensor and stores it in a system. The collection unit can also collect humidity at the accident site to understand the environmental conditions. For example, the collection unit measures the humidity at the accident site with a sensor and stores it in a system. Furthermore, the collection unit can simultaneously collect temperature and humidity to provide comprehensive environmental data. For example, the collection unit simultaneously measures temperature and humidity and stores it in a system. In this way, by collecting environmental data such as temperature and humidity, the cause of the accident can be more accurately identified. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the collected environmental data to a generation AI and have the generation AI analyze the data.

[0078] The collection unit can automatically collect related information on social media when collecting accident information. The collection unit, for example, collects posts related to the accident on social media. For example, the collection unit searches for posts on social media using specific hashtags or keywords and saves them in the system. The collection unit can also collect images and videos related to the accident from social media. For example, the collection unit downloads images and videos from social media and saves them in the system. The collection unit can also collect eyewitness information on social media to understand details of the accident. For example, the collection unit searches for eyewitness information on social media and saves it in the system. In this way, by collecting information on social media, detailed circumstances of the accident can be understood. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data posted on social media into a generation AI and have the generation AI analyze the data.

[0079] The analysis unit can estimate the user's emotions and adjust the speed of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit slows down the speed of the analysis to make it easier for the user to understand. For example, if the user is nervous, the analysis unit slows down the speed of the analysis. Furthermore, if the user is relaxed, the analysis unit can normalize the speed of the analysis. For example, if the user is relaxed, the analysis unit normalizes the speed of the analysis. Furthermore, if the user is impatient, the analysis unit can speed up the speed of the analysis to provide results more quickly. For example, if the user is impatient, the analysis unit speeds up the speed of the analysis. This enables more appropriate analysis by adjusting the speed of the analysis according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0080] During analysis, the analysis unit can evaluate the reliability of the collected data and exclude low-reliability data. The analysis unit, for example, checks the source of the data and excludes low-reliability data. For example, the analysis unit checks the source of the data and excludes low-reliability data. The analysis unit can also check the consistency of the data and exclude contradictory data. For example, the analysis unit checks the consistency of the data and excludes contradictory data. Furthermore, the analysis unit can score the reliability of the data and exclude data with a low score. For example, the analysis unit scores the reliability of the data and excludes data with a low score. This eliminates low-reliability data, thereby improving the accuracy of the analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI evaluate the reliability of the data.

[0081] During analysis, the analysis unit can execute different analysis algorithms in parallel and select the most appropriate result. For example, the analysis unit can execute multiple analysis algorithms simultaneously and compare the results. The analysis unit can also evaluate the results of each algorithm and select the most reliable result. For example, the analysis unit can evaluate the results of each algorithm and select the most reliable result. Furthermore, the analysis unit can integrate the results of different algorithms and provide a comprehensive analysis result. For example, the analysis unit can integrate the results of different algorithms and provide a comprehensive analysis result. This allows the execution of different analysis algorithms in parallel to obtain the most appropriate analysis result. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input multiple analysis algorithms into a generation AI and have the generation AI evaluate and select the results.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. For example, if the user is relaxed, the analysis unit provides a display method including detailed information. Furthermore, if the user is impatient, the analysis unit can provide a display method that emphasizes the main points. For example, if the user is impatient, the analysis unit provides a display method that emphasizes the main points. This allows the display method to be adjusted according to the user's emotions, thereby providing analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0083] During analysis, the analysis unit can link with a database of other similar accidents and perform analysis based on a wider range of data. The analysis unit, for example, links with a database of similar accidents and refers to past cases. For example, the analysis unit links with a database of similar accidents and refers to past cases. The analysis unit can also identify common causes based on other accident data. For example, the analysis unit can identify common causes based on other accident data. Furthermore, the analysis unit can integrate data of similar accidents and perform a comprehensive analysis. For example, the analysis unit integrates data of similar accidents and performs a comprehensive analysis. In this way, by linking with databases of other similar accidents, analysis can be performed based on a wider range of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of other similar accidents into the generation AI and have the generation AI analyze the data.

[0084] The analysis unit updates data in real time during analysis and can perform analysis based on the latest information. The analysis unit, for example, reflects data collected in real time in the analysis. For example, the analysis unit reflects data collected in real time in the analysis. The analysis unit can also update the analysis results in real time in response to data updates. For example, the analysis unit updates the analysis results in real time in response to data updates. Furthermore, the analysis unit can also provide analysis results based on the latest information. For example, the analysis unit provides analysis results based on the latest information. In this way, by updating data in real time, analysis can be performed based on the latest information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data updated in real time to the generation AI and have the generation AI analyze the data.

[0085] The cause identification unit can estimate the user's emotions and determine the priority of cause identification based on the estimated user's emotions. For example, when the user is nervous, the cause identification unit prioritizes identifying important causes. For example, when the user is nervous, the cause identification unit prioritizes identifying important causes. Furthermore, when the user is relaxed, the cause identification unit can also identify detailed causes. For example, when the user is relaxed, the cause identification unit identifies detailed causes. Furthermore, when the user is impatient, the cause identification unit can prioritize causes that need to be identified quickly. For example, when the user is impatient, the cause identification unit prioritizes causes that need to be identified quickly. In this way, by determining the priority of cause identification according to the user's emotions, it is possible to prioritize identifying important causes. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the cause identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the cause identification unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0086] When identifying a cause, the cause identification unit can formulate multiple hypotheses and perform verification based on each hypothesis. For example, the cause identification unit formulates multiple hypotheses and verifies data based on each hypothesis. The cause identification unit can also evaluate the reliability of each hypothesis and select the most reliable hypothesis. For example, the cause identification unit evaluates the reliability of each hypothesis and selects the most reliable hypothesis. Furthermore, the cause identification unit can also perform verification using a different analysis method for each hypothesis. For example, the cause identification unit performs verification using a different analysis method for each hypothesis. In this way, by formulating and verifying multiple hypotheses, the most reliable cause can be identified. Some or all of the above-described processing in the cause identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the cause identification unit can input multiple hypotheses to a generation AI and have the generation AI verify the hypotheses.

[0087] During cause identification, the cause identification unit can investigate the product's manufacturing process or distribution process and identify potential problems. For example, the cause identification unit can investigate the product's manufacturing process in detail and identify manufacturing problems. The cause identification unit can also investigate the product's distribution process and identify distribution problems. For example, the cause identification unit can investigate the product's distribution process and identify distribution problems. The cause identification unit can also investigate the manufacturing process and distribution process in an integrated manner and identify potential problems. For example, the cause identification unit can investigate the manufacturing process and distribution process in an integrated manner and identify potential problems. In this way, potential problems can be identified by investigating the manufacturing process or distribution process in detail. Some or all of the above-mentioned processing in the cause identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the cause identification unit can input data on the manufacturing process or distribution process into the generation AI and have the generation AI analyze the data.

[0088] The cause identification unit can estimate the user's emotions and adjust the order in which the cause identification results are displayed based on the estimated user's emotions. For example, if the user is nervous, the cause identification unit displays important results first. For example, if the user is nervous, the cause identification unit displays important results first. Furthermore, if the user is relaxed, the cause identification unit can sequentially display detailed results. For example, if the user is relaxed, the cause identification unit sequentially displays detailed results. Furthermore, if the user is impatient, the cause identification unit can first display results that need to be displayed quickly. For example, if the user is impatient, the cause identification unit displays results that need to be displayed quickly. In this way, by adjusting the display order of the results according to the user's emotions, important results can be displayed preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the cause identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the cause identification unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0089] When identifying causes, the cause identification unit can refer to data on other products or similar products to identify common problems. The cause identification unit, for example, refers to data on other products to identify common problems. For example, the cause identification unit refers to data on other products to identify common problems. The cause identification unit can also refer to data on similar products to identify common problems. For example, the cause identification unit refers to data on similar products to identify common problems. Furthermore, the cause identification unit can integrate data on other products and similar products to identify comprehensive problems. For example, the cause identification unit integrates data on other products and similar products to identify comprehensive problems. In this way, common problems can be identified by referring to data on other products or similar products. Some or all of the above-described processing in the cause identification unit may be performed using, or without, AI. For example, the cause identification unit can input data on other products or similar products into the generation AI and have the generation AI analyze the data.

[0090] When identifying causes, the cause identification unit can identify causes by taking into account the product usage environment and the user's behavioral patterns. For example, the cause identification unit can identify causes caused by the environment by taking into account the product usage environment. The cause identification unit can also identify causes caused by behavior by taking into account the user's behavioral patterns. For example, the cause identification unit can identify causes caused by behavior by taking into account the user's behavioral patterns. Furthermore, the cause identification unit can identify a comprehensive cause by taking into account the usage environment and behavioral patterns in an integrated manner. For example, the cause identification unit can identify a comprehensive cause by taking into account the usage environment and behavioral patterns in an integrated manner. In this way, a comprehensive cause can be identified by taking into account the usage environment and behavioral patterns. Some or all of the above-described processing in the cause identification unit may be performed using, or without, AI. For example, the cause identification unit can input data on the usage environment and behavioral patterns into a generation AI and have the generation AI analyze the data.

[0091] The analysis method planning unit can estimate the user's emotions and adjust the proposed analysis method based on the estimated user's emotions. For example, if the user is nervous, the analysis method planning unit can propose a simple and easy-to-understand analysis method. For example, if the user is nervous, the analysis method planning unit can propose a simple and easy-to-understand analysis method. Furthermore, if the user is relaxed, the analysis method planning unit can also propose a detailed analysis method. For example, if the user is relaxed, the analysis method planning unit can propose a detailed analysis method. Furthermore, if the user is impatient, the analysis method planning unit can propose a quickly executable analysis method. For example, if the user is impatient, the analysis method planning unit can propose a quickly executable analysis method. This allows the proposal content to be adjusted according to the user's emotions, thereby proposing a more appropriate analysis method. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis method planning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis method planning unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0092] When planning an analysis method, the analysis method planning unit can refer to past examples of successful and unsuccessful analysis methods and select the optimal method. The analysis method planning unit, for example, refers to past successful examples and proposes a similar analysis method. For example, the analysis method planning unit refers to past successful examples and proposes a similar analysis method. The analysis method planning unit can also refer to past unsuccessful examples and propose an analysis method that avoids failure. For example, the analysis method planning unit refers to past unsuccessful examples and proposes an analysis method that avoids failure. Furthermore, the analysis method planning unit can comprehensively refer to successful and unsuccessful examples and select the optimal analysis method. For example, the analysis method planning unit comprehensively refers to successful and unsuccessful examples and selects the optimal analysis method. In this way, the optimal analysis method can be selected by referring to past successful and unsuccessful examples. Some or all of the above-mentioned processing in the analysis method planning unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis method planning unit can input data of past analysis methods into a generation AI and have the generation AI analyze the data.

[0093] When planning an analysis method, the analysis method planning unit can propose an analysis method customized according to the product characteristics and usage environment. The analysis method planning unit, for example, proposes an analysis method according to the product characteristics. For example, the analysis method planning unit proposes an analysis method according to the product characteristics. The analysis method planning unit can also propose an analysis method according to the usage environment. For example, the analysis method planning unit proposes an analysis method according to the usage environment. Furthermore, the analysis method planning unit can propose a customized analysis method by comprehensively considering the product characteristics and the usage environment. For example, the analysis method planning unit proposes a customized analysis method by comprehensively considering the product characteristics and the usage environment. This enables more appropriate analysis by proposing an analysis method according to the product characteristics and the usage environment. Some or all of the above-mentioned processing in the analysis method planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis method planning unit can input data on the product characteristics and usage environment into the generation AI and have the generation AI analyze the data.

[0094] The analysis method planning unit can estimate the user's emotions and determine the priority of analysis methods based on the estimated user emotions. For example, when the user is nervous, the analysis method planning unit prioritizes suggesting important analysis methods. For example, when the user is nervous, the analysis method planning unit prioritizes suggesting important analysis methods. Furthermore, when the user is relaxed, the analysis method planning unit can also suggest detailed analysis methods. For example, when the user is relaxed, the analysis method planning unit proposes detailed analysis methods. Furthermore, when the user is impatient, the analysis method planning unit can also prioritize suggesting analysis methods that can be quickly executed. For example, when the user is impatient, the analysis method planning unit prioritizes suggesting analysis methods that can be quickly executed. In this way, by determining the priority of analysis methods according to the user's emotions, it is possible to prioritize suggesting important analysis methods. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis method planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis method planning unit may input user facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0095] When planning an analysis method, the analysis method planning unit can propose an analysis method by incorporating the opinions and advice of other experts. The analysis method planning unit, for example, collects the opinions of other experts and reflects them in the analysis method. For example, the analysis method planning unit collects the opinions of other experts and reflects them in the analysis method. The analysis method planning unit can also adjust the analysis method based on the advice of experts. For example, the analysis method planning unit adjusts the analysis method based on the advice of experts. Furthermore, the analysis method planning unit can integrate the opinions of multiple experts and propose an optimal analysis method. For example, the analysis method planning unit integrates the opinions of multiple experts and proposes an optimal analysis method. In this way, by incorporating the opinions and advice of other experts, a more reliable analysis method can be proposed. Some or all of the above-mentioned processing in the analysis method planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis method planning unit can input the opinions and advice of experts into the generation AI and cause the generation AI to analyze the data.

[0096] When planning an analysis method, the analysis method planning unit can propose an analysis method that incorporates the latest technologies and techniques. The analysis method planning unit, for example, proposes a method that incorporates the latest analysis technology. For example, the analysis method planning unit proposes a method that incorporates the latest analysis technology. The analysis method planning unit can also propose an analysis method that uses a new technique. For example, the analysis method planning unit proposes an analysis method that uses a new technique. Furthermore, the analysis method planning unit can integrate the latest technologies and techniques to propose an optimal analysis method. For example, the analysis method planning unit integrates the latest technologies and techniques to propose an optimal analysis method. This makes it possible to propose a more effective analysis method by incorporating the latest technologies and techniques. Some or all of the above-mentioned processing in the analysis method planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis method planning unit can input data on the latest technologies and techniques into the generation AI and have the generation AI analyze the data.

[0097] The report creation unit can estimate the user's emotions and adjust the presentation style of the report based on the estimated user's emotions. For example, if the user is nervous, the report creation unit uses a simple and easy-to-understand presentation style. For example, if the user is nervous, the report creation unit uses a simple and easy-to-understand presentation style. Furthermore, if the user is relaxed, the report creation unit can use a presentation style that includes detailed information. For example, if the user is relaxed, the report creation unit uses a presentation style that includes detailed information. Furthermore, if the user is anxious, the report creation unit can use a presentation style that can be quickly understood. For example, if the user is anxious, the report creation unit uses a presentation style that can be quickly understood. In this way, by adjusting the presentation style according to the user's emotions, a more understandable report can be created. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the report creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the report creation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0098] When creating a report, the report creation unit can refer to the format and content of past reports to create an optimal report. For example, the report creation unit refers to the format of past reports and creates a report in a similar format. For example, the report creation unit refers to the format of past reports and creates a report in a similar format. The report creation unit can also refer to the content of past reports to create a report including necessary information. For example, the report creation unit refers to the content of past reports and creates a report including necessary information. Furthermore, the report creation unit can also create an optimal report based on successful examples of past reports. For example, the report creation unit creates an optimal report based on successful examples of past reports. In this way, an optimal report can be created by referring to past reports. Some or all of the above-mentioned processing in the report creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the report creation unit can input data of past reports into a generation AI and have the generation AI analyze the data.

[0099] When creating a report, the report creation unit can create a customized report in accordance with the request of a government agency. The report creation unit, for example, creates a report in a format in accordance with the request of a government agency. For example, the report creation unit creates a report in a format in accordance with the request of a government agency. The report creation unit can also create a report including necessary information based on the request of a government agency. For example, the report creation unit creates a report including necessary information based on the request of a government agency. The report creation unit can also create a customized report in accordance with the request of a government agency. For example, the report creation unit creates a customized report in accordance with the request of a government agency. This enables quick and accurate reporting by creating a report in accordance with the request of a government agency. Some or all of the above-mentioned processing in the report creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the report creation unit can input data requested by a government agency into a generation AI and have the generation AI create a report.

[0100] The report creation unit can estimate the user's emotions and determine the priority of reports based on the estimated user's emotions. For example, when the user is nervous, the report creation unit prioritizes creating important reports. For example, when the user is nervous, the report creation unit prioritizes creating important reports. The report creation unit can also create detailed reports when the user is relaxed. For example, when the user is relaxed, the report creation unit creates detailed reports. Furthermore, when the user is impatient, the report creation unit can prioritize reports that need to be created quickly. For example, when the user is impatient, the report creation unit prioritizes reports that need to be created quickly. In this way, by determining the priority of reports according to the user's emotions, important reports can be created preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the report creation unit may be performed using, for example, AI, or without AI. For example, the report creation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0101] When creating a report, the report creation unit can refer to other related documents and data to enrich the content of the report. For example, the report creation unit can refer to other related documents and include necessary information in the report. For example, the report creation unit can refer to other related documents and include necessary information in the report. The report creation unit can also refer to related data to enrich the content of the report. For example, the report creation unit can refer to related data to enrich the content of the report. Furthermore, the report creation unit can integrate other documents and data to create a comprehensive report. For example, the report creation unit can integrate other documents and data to create a comprehensive report. In this way, the content of the report can be enriched by referring to other related documents and data. Some or all of the above-mentioned processing in the report creation unit may be performed using AI, for example, or may be performed without using AI. For example, the report creation unit can input other related documents and data into a generation AI and have the generation AI analyze the data.

[0102] When creating a report, the report creation unit can adjust the format depending on the destination and method of submission of the report. For example, the report creation unit creates the report in a format according to the request of the destination. For example, the report creation unit creates the report in a format according to the request of the destination. The report creation unit can also create the report in an appropriate format depending on the submission method. For example, the report creation unit creates the report in an appropriate format depending on the submission method. Furthermore, the report creation unit can also create the report in an optimal format taking into account the destination and submission method. For example, the report creation unit creates the report in an optimal format taking into account the destination and submission method. In this way, by creating the report in a format according to the destination and submission method, it is possible to create a report that meets the request of the destination. Some or all of the above-mentioned processing in the report creation unit may be performed using, or without, AI. For example, the report creation unit can input data on the destination and submission method into a generation AI and have the generation AI adjust the format.

[0103] The analysis unit can estimate the user's emotions and adjust the speed of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit slows down the speed of the analysis to make it easier for the user to understand. For example, if the user is nervous, the analysis unit slows down the speed of the analysis. Furthermore, if the user is relaxed, the analysis unit can normalize the speed of the analysis. For example, if the user is relaxed, the analysis unit normalizes the speed of the analysis. Furthermore, if the user is impatient, the analysis unit can speed up the speed of the analysis to provide results more quickly. For example, if the user is impatient, the analysis unit speeds up the speed of the analysis. This enables more appropriate analysis by adjusting the speed of the analysis according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0104] During analysis, the analysis unit can evaluate the reliability of the collected data and exclude low-reliability data. The analysis unit, for example, checks the source of the data and excludes low-reliability data. For example, the analysis unit checks the source of the data and excludes low-reliability data. The analysis unit can also check the consistency of the data and exclude contradictory data. For example, the analysis unit checks the consistency of the data and excludes contradictory data. Furthermore, the analysis unit can score the reliability of the data and exclude data with a low score. For example, the analysis unit scores the reliability of the data and excludes data with a low score. This eliminates low-reliability data, thereby improving the accuracy of the analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI evaluate the reliability of the data.

[0105] During analysis, the analysis unit can execute different analysis algorithms in parallel and select the most appropriate result. For example, the analysis unit can execute multiple analysis algorithms simultaneously and compare the results. The analysis unit can also evaluate the results of each algorithm and select the most reliable result. For example, the analysis unit can evaluate the results of each algorithm and select the most reliable result. Furthermore, the analysis unit can integrate the results of different algorithms and provide a comprehensive analysis result. For example, the analysis unit can integrate the results of different algorithms and provide a comprehensive analysis result. This allows the execution of different analysis algorithms in parallel to obtain the most appropriate analysis result. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input multiple analysis algorithms into a generation AI and have the generation AI evaluate and select the results.

[0106] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. For example, if the user is relaxed, the analysis unit provides a display method including detailed information. Furthermore, if the user is impatient, the analysis unit can provide a display method that emphasizes the main points. For example, if the user is impatient, the analysis unit provides a display method that emphasizes the main points. This allows the display method to be adjusted according to the user's emotions, thereby providing analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, cause identification unit, analysis method planning unit, and report creation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects photos and videos of the accident scene using the camera 42 and microphone 38B of the smart device 14 and uploads them to the system via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes past accident data and product design information. The cause identification unit, realized, for example, by the specific processing unit 290 of the data processing device 12, identifies a probable cause based on the analyzed information. The analysis method planning unit, realized, for example, by the specific processing unit 290 of the data processing device 12, plans an effective analysis method. The report creation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates a report for government agencies. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, cause identification unit, analysis method planning unit, and report creation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects photos and videos of the accident scene using the camera 42 and microphone 238 of the smart glasses 214 and uploads them to the system via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes past accident data and product design information. The cause identification unit, realized, for example, by the specific processing unit 290 of the data processing device 12, identifies a probable cause based on the analyzed information. The analysis method planning unit, realized, for example, by the specific processing unit 290 of the data processing device 12, plans an effective analysis method. The report creation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates a report for government agencies. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, cause identification unit, analysis method planning unit, and report creation unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects photos and videos of the accident scene using the camera 42 and microphone 238 of the headset-type terminal 314 and uploads them to the system via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes past accident data and product design information. The cause identification unit, realized, for example, by the specific processing unit 290 of the data processing device 12, identifies a probable cause based on the analyzed information. The analysis method planning unit, realized, for example, by the specific processing unit 290 of the data processing device 12, plans an effective analysis method. The report creation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates a report for government agencies. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, cause identification unit, analysis method planning unit, and report creation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects photos and videos of the accident scene using the camera 42 and microphone 238 of the robot 414 and uploads them to the system via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes past accident data and product design information. The cause identification unit, realized, for example, by the specific processing unit 290 of the data processing device 12, identifies a probable cause based on the analyzed information. The analysis method planning unit, realized, for example, by the specific processing unit 290 of the data processing device 12, plans an effective analysis method. The report creation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates a report for government agencies.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The accident response system can also be equipped with a real-time data collection function. For example, the collection unit can acquire data in real time from sensors at the accident site and immediately reflect it in the system. The collection unit can also collect aerial footage of the accident site in real time using a drone. Furthermore, the collection unit can perform live streaming of the accident site, enabling monitoring from a remote location. This makes it possible to collect data in real time and respond quickly.

[0109] When collecting accident information, the collection department can estimate the emotions of victims and witnesses and adjust the interview process based on the estimated emotions. For example, if the victim is in a state of shock, the collection department can temporarily halt the interview and wait for the victim to calm down. Also, if the witness is nervous, the collection department can first ask questions to relax them and gradually collect more detailed information. Furthermore, if the victim or witness is anxious, the collection department can quickly collect the necessary information and reduce their burden. This makes it possible to collect information while taking their emotions into consideration.

[0110] When analyzing accident information, the analysis unit can integrate information from different data sources and perform a comprehensive analysis. For example, the analysis unit can integrate product design information, usage status, environmental data, social media posts, etc. to more accurately identify the cause. The analysis unit can also analyze correlations between different data sources to identify potential problems. Furthermore, the analysis unit can evaluate the reliability of the data and prioritize analysis of highly reliable data. This enables comprehensive data analysis and more accurate cause identification.

[0111] When identifying the cause of an accident, the cause identification unit can estimate the user's emotions and adjust the cause identification approach based on the estimated emotions. For example, if the user is feeling anxious, the cause identification unit can identify the cause while providing a detailed explanation to provide a sense of security. Also, if the user is feeling impatient, the cause identification unit can provide a quick result and reduce the user's anxiety. Furthermore, if the user is relaxed, the cause identification unit can perform detailed data analysis to provide deeper insights. This makes it possible to identify the cause according to the user's emotions.

[0112] The analysis method planning department can incorporate the opinions of experts in other fields when planning an analysis method. For example, it can collect opinions from experts in product design, use environments, and data analysis, and reflect them in the analysis method. The analysis method planning department can also integrate knowledge from different fields to propose more effective analysis methods. Furthermore, the analysis method planning department can identify areas for improvement in the analysis method based on the opinions of experts and select the optimal method. This makes it possible to plan an analysis method that utilizes knowledge from other fields.

[0113] When creating a report, the report creation unit can estimate the user's emotions and adjust the content of the report based on the estimated emotions. For example, if the user is nervous, the report creation unit can use simple, easy-to-understand expressions. If the user is relaxed, the report creation unit can also create a report that includes detailed information. Furthermore, if the user is impatient, the report creation unit can also create a report that focuses on the main points and can be quickly understood. This makes it possible to create a report that corresponds to the user's emotions.

[0114] When collecting accident information, the collection unit can simultaneously collect environmental data such as the temperature, humidity, and air pressure at the accident site. For example, the collection unit can measure the temperature at the accident site using a sensor and store the data in the system. The collection unit can also measure the humidity at the accident site using a sensor and store the data in the system. Furthermore, the collection unit can measure the air pressure to understand the environmental conditions. By collecting environmental data, the cause of the accident can be more accurately identified.

[0115] During analysis, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is impatient, the analysis unit can also provide a display method that focuses on the main points. In this way, by providing a display method that corresponds to the user's emotions, it is possible to provide analysis results that are easier to understand.

[0116] When identifying the cause, the cause identification unit can investigate the manufacturing process and distribution process of the product in detail to identify potential problems. For example, the cause identification unit can investigate the manufacturing process of the product in detail to identify manufacturing problems. The cause identification unit can also investigate the distribution process of the product to identify distribution problems. Furthermore, the cause identification unit can investigate the manufacturing process and distribution process in an integrated manner to identify potential problems. In this way, potential problems can be identified by investigating the manufacturing process and distribution process in detail.

[0117] When creating a report, the report creation department can refer to other related documents and data to enrich the content of the report. For example, the report creation department can refer to other related documents to include necessary information in the report. The report creation department can also refer to related data to enrich the content of the report. Furthermore, the report creation department can integrate other documents and data to create a comprehensive report. This allows the content of the report to be enriched by referring to other related documents and data.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection unit collects accident information. The collection unit collects detailed information such as the location and time of the accident, the extent of the damage, and the product model number and serial number. The collection unit can manually enter the information at the accident site or automatically retrieve it from a database. The collection unit can also collect photos and videos of the accident site. For example, the collection unit can take photos of the accident site and upload them to the system. It can also record videos of the accident site and save them in the system. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes information based on past accident data, product design information, usage status, etc. It refers to a database of past accidents to identify the causes of similar accidents. It can also analyze product design information to identify design problems. It can also analyze product usage status to identify incorrect usage. Step 3: The cause identification unit identifies the probable cause based on the information analyzed by the analysis unit. The cause identification unit identifies the probable cause based on, for example, past accident data, product design information, and usage conditions. It references past accident data to identify common causes. It can also analyze product design information to identify design problems. It can also analyze product usage conditions to identify incorrect usage. Step 4: The analysis method planning section plans an effective analysis method based on the causes identified by the cause identification section. For example, the analysis method planning section proposes an analysis of each product part or an experiment to reproduce the usage environment. The analysis method planning section disassembles the product parts and analyzes each part. It can also reproduce the usage environment and conduct experiments. It can also perform analysis using simulations. Step 5: The report preparation department prepares a report for the government agency based on the analysis method proposed by the analysis method planning department. The report preparation department prepares a report that includes, for example, detailed information about the accident, the suspected cause, the analysis method, and future measures. The report preparation department describes the detailed information about the accident and explains the suspected cause. It can also describe the analysis method and propose future measures.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] 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.

[0169] 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.

[0170] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects accident information; an analysis unit that analyzes the information collected by the collection unit; a cause identification unit that identifies a cause that is inferred based on the information analyzed by the analysis unit; an analysis method planning unit that plans an effective analysis method based on the cause identified by the cause identification unit; a report creation unit that creates a report to a government agency based on the analysis method created by the analysis method creation unit. A system characterized by:

2. The collecting unit Collect multiple pieces of information, including the location and time of the accident, the extent of the damage, and the product model number or serial number.

2. The system of claim 1.

3. The analysis unit Analyze information based on past accident data, product design information, usage status, etc.

2. The system of claim 1.

4. The cause identification unit Identify the probable cause based on past accident data, product design information, usage conditions, etc.

2. The system of claim 1.

5. The analysis method planning unit Propose an analysis of each product part or an experiment to reproduce the usage environment 2. The system of claim 1.

6. The report creation unit Prepare a report that includes detailed information about the accident, the suspected cause, analysis methods, and future measures.

2. The system of claim 1.

7. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting accident information based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit When collecting accident information, environmental sounds and images from the scene are collected at the same time to provide more detailed information.

2. The system of claim 1.

Images