system

Abstract

The system according to this embodiment aims to analyze traffic accident records and provide accident prevention measures and countermeasures. [Solution] The system according to the embodiment comprises an anonymization unit, an analysis unit, an extraction unit, and a provision unit. The anonymization unit anonymizes the traffic accident records. The analysis unit analyzes the traffic accident records anonymized by the anonymization unit. The extraction unit extracts accident patterns and risk factors from the records analyzed by the analysis unit. The provision unit provides accident prevention measures and countermeasures based on the risk factors extracted by the extraction unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the records of traffic accidents have not been fully utilized effectively to provide accident prevention measures and countermeasures, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the records of traffic accidents and provide accident prevention measures and countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an anonymization unit, an analysis unit, an extraction unit, and a provision unit. The anonymization unit anonymizes the records of traffic accidents. The analysis unit analyzes the records of traffic accidents anonymized by the anonymization unit. The extraction unit extracts accident patterns and risk factors from the records analyzed by the analysis unit. The provision unit provides accident prevention measures and countermeasures based on the risk factors extracted by the extraction unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze records of traffic accidents and provide accident prevention measures and countermeasures. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The traffic accident risk analysis system according to an embodiment of the present invention is a system that anonymizes traffic accident records and extracts and analyzes accident patterns and related risk factors using a large-scale language model. The traffic accident risk analysis system anonymizes traffic accident records and extracts and analyzes accident patterns and related risk factors using a large-scale language model. Furthermore, the traffic accident risk analysis system provides accident prevention measures and countermeasures. For example, it is being considered that the traffic accident risk analysis system will provide risks that take into account road conditions, congestion levels, weather, etc., through car navigation systems and location-based applications. First, the traffic accident risk analysis system anonymizes traffic accident records. In this process, personal information and specific vehicle information are deleted to protect privacy. For example, the traffic accident risk analysis system anonymizes information such as the location and time of the accident, the type of accident, and the vehicles involved. Next, the traffic accident risk analysis system analyzes the anonymized traffic accident records using a large-scale language model. The large-scale language model analyzes a large amount of data and extracts accident patterns and related risk factors. For example, if the accident rate is high under specific road conditions or weather conditions, the traffic accident risk analysis system identifies the risk factors. Furthermore, the traffic accident risk analysis system provides accident prevention measures and countermeasures based on the identified risk factors. For example, if the risk is high under specific road conditions or weather conditions, the traffic accident risk analysis system provides this risk to drivers through car navigation systems and location-based apps. This allows drivers to understand the risks in advance and take appropriate measures. Through this mechanism, the traffic accident risk analysis system can minimize the risk of traffic accidents and create a safe road environment. For instance, by allowing drivers to understand high-risk road conditions and weather conditions in advance, the system can prevent accidents. In addition, by providing information to transportation professionals, the traffic accident risk analysis system can reduce risks for transportation companies.In this way, the traffic accident risk analysis system can reduce the damage caused by traffic accidents and improve traffic safety by anonymizing traffic accident records, extracting and analyzing accident patterns and risk factors using large-scale language models, and providing accident prevention measures and countermeasures. As a result, the traffic accident risk analysis system can minimize the risk of traffic accidents and create a safe road environment.

[0029] The traffic accident risk analysis system according to this embodiment comprises an anonymization unit, an analysis unit, an extraction unit, and a provision unit. The anonymization unit anonymizes the records of traffic accidents. The anonymization unit protects privacy by, for example, deleting personal information and specific vehicle information. For example, the anonymization unit anonymizes information such as the location and time of the accident, the type of accident, and the vehicles involved. The analysis unit analyzes the traffic accident records anonymized by the anonymization unit. The analysis unit analyzes the anonymized traffic accident records using, for example, a large-scale language model. For example, the analysis unit analyzes a large amount of data and extracts accident patterns and associated risk factors. The extraction unit extracts accident patterns and risk factors from the records analyzed by the analysis unit. For example, the extraction unit identifies risk factors if the accident rate is high under specific road conditions or weather conditions. The provision unit provides accident prevention measures and countermeasures based on the risk factors extracted by the extraction unit. For example, if the risk is high under specific road conditions or weather conditions, the provision unit provides that risk to the driver through a car navigation system or location information application. As a result, the traffic accident risk analysis system according to the embodiment can minimize the risk of traffic accidents and realize a safe road environment. Some or all of the above-described processes in the anonymization unit, analysis unit, extraction unit, and provision unit may be performed using AI, for example, or without using AI. For example, the anonymization unit can input traffic accident records into the AI ​​and cause the AI ​​to delete personal information and specific vehicle information. The analysis unit can input the anonymized traffic accident records into the AI ​​and cause the AI ​​to extract accident patterns and risk factors. The extraction unit can input the analyzed records into the AI ​​and cause the AI ​​to identify risk factors. The provision unit can input the extracted risk factors into the AI ​​and cause the AI ​​to provide accident prevention measures and countermeasures.

[0030] The anonymization unit anonymizes traffic accident records. For example, it removes personal information and specific vehicle information to protect privacy. Specifically, traffic accident records may contain personal information such as the driver's name, address, contact information, vehicle license plate number, and vehicle identification number. The anonymization unit has the ability to automatically detect, delete, or mask this information. For example, it uses natural language processing technology to extract personal information from text data and anonymize it based on specific patterns. It can also use facial recognition and license plate recognition technologies to detect elements that can identify individuals in image data and blur or delete them. Furthermore, the anonymization unit can continuously learn and improve using machine learning algorithms to enhance the accuracy of anonymization. For example, it incorporates the results of past anonymization processes as feedback to improve anonymization accuracy. The anonymization unit also has a function to evaluate the quality of anonymized data and can reprocess data if the anonymization is insufficient. This allows the anonymization unit to safely and effectively anonymize traffic accident records, enabling data use while protecting privacy.

[0031] The analysis unit analyzes the anonymized traffic accident records from the anonymization unit. The analysis unit uses, for example, large-scale language models to analyze the anonymized traffic accident records. Specifically, the analysis unit analyzes the traffic accident records using natural language processing techniques to extract accident patterns and risk factors. For example, traffic accident records include information such as the location, time, weather, road conditions, type and speed of vehicles involved, and the driver's condition. The analysis unit statistically analyzes this information to identify accident rates and risk factors under specific conditions. Furthermore, the analysis unit can use machine learning algorithms to learn from past accident data and perform highly accurate analyses on new accident data. For example, it can use deep learning techniques to detect complex patterns and correlations and build accident prediction models. The analysis unit can also analyze data in real time and perform immediate risk assessments. This allows the analysis unit to quickly and accurately analyze traffic accident records and reveal accident patterns and risk factors.

[0032] The extraction unit extracts accident patterns and risk factors from records analyzed by the analysis unit. Specifically, the extraction unit identifies accident rates and risk factors under specific conditions based on the data provided by the analysis unit. For example, if the accident rate is high under specific road or weather conditions, it identifies the risk factors and provides information for taking countermeasures. The extraction unit uses machine learning algorithms to extract important features and patterns from the analyzed data and identify risk factors. For example, it uses algorithms such as decision trees and random forests to identify factors that contribute to accident occurrence and evaluate their importance. In addition, the extraction unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the extraction unit to accurately identify risk factors for traffic accidents and provide information for taking accident prevention measures and countermeasures. Furthermore, the extraction unit can visualize the extracted risk factors and provide them in an easy-to-understand format. For example, it can use graphs and charts to show the distribution and trends of risk factors, making them easily understandable to stakeholders. This allows the extraction unit to effectively identify risk factors for traffic accidents and provide information for implementing accident prevention measures and countermeasures.

[0033] The information provision unit provides accident prevention measures and countermeasures based on the risk factors identified by the information extraction unit. Specifically, if the risk is high under specific road conditions or weather conditions, the unit provides this risk information to the driver through car navigation systems and location-based apps. Based on real-time updated risk information, the information provision unit provides appropriate action instructions to the driver. For example, if a particular road is slippery, it will instruct the driver to slow down or suggest an alternative route. The information provision unit can also collect driver feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, by recording what actions drivers take based on the information provided and analyzing the results, the accuracy of the information provided can be improved. Furthermore, the information provision unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only car navigation screen displays but also voice guidance, smartphone notifications, and email. This allows the information provision unit to provide drivers with risk information quickly and reliably, minimizing the risk of traffic accidents. In addition, the information provision unit can also collaborate with local governments and traffic management authorities to provide information for implementing comprehensive traffic safety measures. For example, if accidents are occurring frequently in a particular area, this information can be provided to the local government to encourage road repairs and revisions to traffic regulations. This allows the information provider to effectively offer accident prevention measures and countermeasures, thereby creating a safer road environment.

[0034] The service provider can provide risk information that takes into account road conditions, congestion, weather, etc., through car navigation systems and location-based apps. For example, the service provider can provide risk information that takes into account road congestion, road surface conditions, rainfall, and visibility. By providing risk information that takes into account road conditions and weather, drivers can understand the risks in advance and take appropriate measures. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data such as road conditions and weather into AI and have the AI ​​provide risk information.

[0035] The information provision unit can provide information to transportation professionals. For example, the information provision unit can provide information on the risk of traffic accidents to truck drivers and logistics managers. By providing information to transportation professionals, the risks for transportation companies can be reduced. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can input information for transportation professionals into AI and have the AI ​​perform the provision of risk information.

[0036] The anonymization unit can remove personal information and specific vehicle information to protect privacy. For example, the anonymization unit can remove information such as names, addresses, phone numbers, license plate numbers, and vehicle make and model. By removing personal information and specific vehicle information, privacy can be protected. Some or all of the above processing in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input personal information and specific vehicle information into AI and have the AI ​​perform the deletion.

[0037] The analysis unit can analyze large amounts of data and extract accident patterns and risk factors. For example, the analysis unit can analyze large amounts of data and extract accident patterns and risk factors. For example, the analysis unit can analyze past accident data and traffic sensor data to extract accident patterns and risk factors. This allows for the accurate extraction of accident patterns and risk factors by analyzing large amounts of data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input large amounts of data into AI and have the AI ​​perform the extraction of accident patterns and risk factors.

[0038] The extraction unit can identify risk factors when the accident rate is high under specific road conditions or weather conditions. For example, the extraction unit can identify risk factors by considering factors such as road congestion, road surface conditions, rainfall, and poor visibility. This allows for the identification of risk factors when the accident rate is high under specific road conditions or weather conditions, enabling appropriate preventive measures to be taken. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input data on specific road conditions or weather conditions into AI and have the AI ​​identify risk factors.

[0039] The anonymization unit can apply different anonymization algorithms based on the location and time of the accident. For example, in the case of an accident occurring in an urban area, the anonymization unit can remove detailed location information and convert it into broader location information. Furthermore, in the case of an accident occurring at night, the anonymization unit can remove time information and retain only the date. Additionally, in the case of an accident occurring on a highway, the anonymization unit can remove specific interchange information and retain only the route name. This allows for the protection of privacy while maintaining data usefulness by applying different anonymization algorithms based on the location and time of the accident. Some or all of the above processing in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input accident location and time data into AI and have the AI ​​perform the application of an anonymization algorithm.

[0040] The anonymization unit can select different anonymization methods depending on the type of data. For example, in the case of image data, the anonymization unit can blur faces using facial recognition technology. In the case of text data, the anonymization unit can also automatically mask personal names and addresses. Furthermore, in the case of audio data, the anonymization unit can remove personally identifiable information using speech recognition technology. By selecting different anonymization methods depending on the type of data, privacy can be protected while maximizing the usefulness of the data. Some or all of the above-described processes in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input the type of data into the AI ​​and have the AI ​​select the anonymization method.

[0041] The anonymization unit can apply different anonymization methods based on the source of the data. For example, if the data is obtained from a public institution, the anonymization unit will apply a strict anonymization method. If the data is obtained from a private company, the anonymization unit can also apply an anonymization method in accordance with the company's policy. Furthermore, if the data is obtained from an individual, the anonymization unit can apply an anonymization method based on the Personal Information Protection Act. This allows for the protection of privacy while maintaining the usefulness of the data by applying different anonymization methods based on the source of the data. Some or all of the above processing in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input the data source into AI and have AI perform the application of the anonymization method.

[0042] The anonymization unit can set different levels of anonymization depending on the purpose of data use. For example, for data used for research purposes, the anonymization unit can perform detailed anonymization and completely remove personal information. For data used for commercial purposes, the anonymization unit can also perform the minimum necessary anonymization to maintain the usefulness of the data. Furthermore, for data used for public safety purposes, the anonymization unit can perform anonymization to the extent that specific individuals cannot be identified. In this way, by setting different levels of anonymization depending on the purpose of data use, privacy can be protected while maintaining the usefulness of the data. Some or all of the above processing in the anonymization unit may be performed using AI, for example, or not using AI. For example, the anonymization unit can input the purpose of data use into AI and have AI perform the setting of the anonymization level.

[0043] The analysis unit can apply different analysis algorithms depending on the type of data. For example, in the case of image data, the analysis unit can perform analysis by applying an image recognition algorithm. Furthermore, in the case of text data, the analysis unit can perform analysis by applying a natural language processing algorithm. In addition, in the case of audio data, the analysis unit can perform analysis by applying a speech recognition algorithm. This allows for analysis while maximizing the usefulness of the data by applying different analysis algorithms depending on the type of data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data type into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0044] The analysis unit can improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit can improve the accuracy of the analysis by identifying data with similar patterns based on past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results to identify outliers and abnormal values. Furthermore, the analysis unit can improve the accuracy by adjusting the parameters of the analysis algorithm based on past analysis results. In this way, the accuracy of the analysis can be improved by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis results into AI and have the AI ​​perform the analysis accuracy improvement.

[0045] The analysis unit can adjust its analysis method based on the data acquisition time. For example, with the latest data, the analysis unit can apply a real-time analysis algorithm to perform analysis quickly. With historical data, the analysis unit can also apply a historical analysis algorithm to analyze long-term trends. Furthermore, with data from a specific period, the analysis unit can apply an analysis algorithm specific to that period. This allows for analysis while maximizing data usefulness by adjusting the analysis method based on the data acquisition time. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data acquisition time into the AI ​​and have the AI ​​adjust the analysis method.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data to improve overall analysis efficiency. Alternatively, the analysis unit can postpone the analysis of less relevant data and prioritize the analysis of important data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows for improved analysis efficiency by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data relevance into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0047] The extraction unit can improve the accuracy of extraction by considering the interrelationships between data. For example, the extraction unit can analyze the correlations between data and extract highly relevant risk factors. The extraction unit can also improve accuracy by identifying outliers and anomalies by considering the interrelationships between data. Furthermore, the extraction unit can improve accuracy by determining the priority of risk factors based on the interrelationships between data. In this way, the accuracy of risk factor extraction can be improved by considering the interrelationships between data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the interrelationships between data into AI and have the AI ​​perform the improvement of extraction accuracy.

[0048] The extraction unit can apply different extraction algorithms depending on the type of data. For example, in the case of image data, the extraction unit can apply an image recognition algorithm to extract risk factors. In the case of text data, the extraction unit can also apply a natural language processing algorithm to extract risk factors. Furthermore, in the case of audio data, the extraction unit can apply a speech recognition algorithm to extract risk factors. This allows for improved accuracy in extracting risk factors by applying the most suitable extraction algorithm according to the type of data. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the data type into the AI ​​and have the AI ​​perform the application of the extraction algorithm.

[0049] The extraction unit can perform extraction while considering the geographical distribution of the data. For example, if the accident rate is high in a particular area, the extraction unit will prioritize the extraction of risk factors in that area. The extraction unit can also improve accuracy by identifying outliers and anomalies while considering the geographical distribution. Furthermore, the extraction unit can improve accuracy by determining the priority of risk factors based on the geographical distribution. In this way, by considering the geographical distribution of the data, risk factors for each region can be accurately extracted. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input geographical distribution data into AI and have the AI ​​perform the extraction.

[0050] The extraction unit can improve the accuracy of its extractions by referring to relevant literature for the data. For example, the extraction unit can improve accuracy by identifying risk factors based on relevant literature. It can also improve accuracy by identifying outliers and abnormal values ​​by referring to relevant literature. Furthermore, the extraction unit can improve accuracy by determining the priority of risk factors based on relevant literature. In this way, the accuracy of risk factor extraction can be improved by referring to relevant literature for the data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input data from relevant literature into AI and have the AI ​​perform the improvement of extraction accuracy.

[0051] The information provider can adjust the level of detail provided based on the importance of the risk factors. For example, in the case of a significant risk factor, the provider can provide detailed preventive measures and countermeasures. In the case of a minor risk factor, the provider can also provide concise preventive measures and countermeasures. Furthermore, the provider can dynamically adjust the level of detail of the information provided according to the importance of the risk factors. This allows the provider to provide users with important information at an appropriate level of detail by adjusting the level of detail based on the importance of the risk factors. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the importance of the risk factors into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the information provided.

[0052] The service provider can apply different service algorithms depending on the category of risk factor. For example, in the case of a weather-related risk factor, the service provider can provide preventive measures based on weather forecast data. Furthermore, in the case of a road-related risk factor, the service provider can provide countermeasures based on road management data. In addition, in the case of a congestion-related risk factor, the service provider can provide preventive measures based on traffic data. This allows the service provider to provide useful preventive measures and countermeasures to users by applying the most appropriate service algorithm according to the category of risk factor. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the risk factor categories into the AI ​​and have the AI ​​apply the service algorithm.

[0053] The information delivery unit can adjust its delivery method based on when the risk factor occurred. For example, in the case of a recently occurring risk factor, the unit can provide preventive measures and countermeasures in real time. Furthermore, in the case of a risk factor that occurred in the past, the unit can provide preventive measures and countermeasures based on historical data. In addition, in the case of a risk factor that occurred during a specific period, the unit can provide preventive measures and countermeasures specific to that period. This allows the information to be delivered to the user at the optimal time by adjusting the delivery method based on when the risk factor occurred. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the timing of the risk factor's occurrence into the AI ​​and have the AI ​​adjust the delivery method.

[0054] The information delivery unit can adjust the order of delivery based on the relevance of risk factors. For example, the delivery unit can prioritize the delivery of highly relevant risk factors to improve the overall efficiency of preventive measures and countermeasures. Alternatively, the delivery unit can postpone less relevant risk factors and prioritize preventive measures and countermeasures for important risk factors. Furthermore, the delivery unit can dynamically adjust the order of delivery based on the relevance of risk factors. This allows for the efficient delivery of information important to the user by adjusting the order of delivery based on the relevance of risk factors. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not. For example, the delivery unit can input the relevance of risk factors into AI and have the AI ​​perform the adjustment of the delivery order.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The traffic accident risk analysis system can also be equipped with a real-time data acquisition unit. The real-time data acquisition unit acquires data in real time from traffic sensors, cameras, weather data, etc., and provides it to the analysis unit. For example, the real-time data acquisition unit can acquire road congestion and weather changes in real time and transmit them to the analysis unit. This allows the analysis unit to analyze accident risks more accurately based on the latest data. In addition, the real-time data acquisition unit can immediately collect data when a traffic accident occurs and provide it to the analysis unit quickly. As a result, the traffic accident risk analysis system enables real-time identification and countermeasures for accident risks, further improving traffic safety.

[0057] The traffic accident risk analysis system may also include a user feedback unit. The user feedback unit collects feedback from drivers and provides it to the analysis unit. For example, the user feedback unit can collect information about dangerous situations perceived by drivers and factors believed to be the cause of accidents. This allows the analysis unit to incorporate data based on the drivers' actual experiences into its analysis. Furthermore, the user feedback unit can adjust the preventative measures and countermeasures provided by the analysis unit based on the feedback provided by the drivers. This enables the traffic accident risk analysis system to provide more effective preventative measures and countermeasures based on the drivers' actual experiences.

[0058] The traffic accident risk analysis system can also be equipped with a prediction unit. This unit predicts future accident risks based on historical and real-time data. For example, it can predict accident rates on specific roads and time periods, providing drivers with advance warnings. Furthermore, the prediction unit can predict fluctuations in accident risk by considering factors such as weather changes and increased traffic volume. This allows the traffic accident risk analysis system to anticipate future accident risks and provide drivers with appropriate countermeasures.

[0059] The traffic accident risk analysis system can also include an education section. This education section provides drivers with educational content on traffic safety. For example, it can provide simulations based on past accident data and information on risk factors. It can also provide training programs to improve drivers' driving skills. This allows the traffic accident risk analysis system to raise drivers' awareness of traffic safety and prevent accidents from occurring.

[0060] The traffic accident risk analysis system can also include a community collaboration unit. This unit collects information on local traffic safety and provides it to the analysis unit. For example, the community collaboration unit can collect opinions and suggestions on traffic safety from local residents. It can also provide information on local traffic safety events and campaigns. This allows the traffic accident risk analysis system to reflect local traffic safety information and provide more effective preventative measures and countermeasures.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The anonymization unit anonymizes the traffic accident records. Specifically, it removes personal information and specific vehicle information to protect privacy. For example, it anonymizes the location and time of the accident, the type of accident, and information about the vehicles involved. Step 2: The analysis unit analyzes the traffic accident records anonymized by the anonymization unit. For example, it uses a large-scale language model to analyze a large amount of data and extract accident patterns and associated risk factors. Step 3: The extraction unit extracts accident patterns and risk factors from the records analyzed by the analysis unit. For example, if the accident rate is high under certain road conditions or weather conditions, the risk factors are identified. Step 4: The provisioning unit provides accident prevention measures and countermeasures based on the risk factors identified by the extraction unit. For example, if the risk is high under specific road conditions or weather conditions, the provisioning unit will inform the driver of that risk through a car navigation system or location-based app.

[0063] (Example of form 2) The traffic accident risk analysis system according to an embodiment of the present invention is a system that anonymizes traffic accident records and extracts and analyzes accident patterns and related risk factors using a large-scale language model. The traffic accident risk analysis system anonymizes traffic accident records and extracts and analyzes accident patterns and related risk factors using a large-scale language model. Furthermore, the traffic accident risk analysis system provides accident prevention measures and countermeasures. For example, it is being considered that the traffic accident risk analysis system will provide risks that take into account road conditions, congestion levels, weather, etc., through car navigation systems and location-based applications. First, the traffic accident risk analysis system anonymizes traffic accident records. In this process, personal information and specific vehicle information are deleted to protect privacy. For example, the traffic accident risk analysis system anonymizes information such as the location and time of the accident, the type of accident, and the vehicles involved. Next, the traffic accident risk analysis system analyzes the anonymized traffic accident records using a large-scale language model. The large-scale language model analyzes a large amount of data and extracts accident patterns and related risk factors. For example, if the accident rate is high under specific road conditions or weather conditions, the traffic accident risk analysis system identifies the risk factors. Furthermore, the traffic accident risk analysis system provides accident prevention measures and countermeasures based on the identified risk factors. For example, if the risk is high under specific road conditions or weather conditions, the traffic accident risk analysis system provides this risk to drivers through car navigation systems and location-based apps. This allows drivers to understand the risks in advance and take appropriate measures. Through this mechanism, the traffic accident risk analysis system can minimize the risk of traffic accidents and create a safe road environment. For instance, by allowing drivers to understand high-risk road conditions and weather conditions in advance, the system can prevent accidents. In addition, by providing information to transportation professionals, the traffic accident risk analysis system can reduce risks for transportation companies.In this way, the traffic accident risk analysis system can reduce the damage caused by traffic accidents and improve traffic safety by anonymizing traffic accident records, extracting and analyzing accident patterns and risk factors using large-scale language models, and providing accident prevention measures and countermeasures. As a result, the traffic accident risk analysis system can minimize the risk of traffic accidents and create a safe road environment.

[0064] The traffic accident risk analysis system according to this embodiment comprises an anonymization unit, an analysis unit, an extraction unit, and a provision unit. The anonymization unit anonymizes the records of traffic accidents. The anonymization unit protects privacy by, for example, deleting personal information and specific vehicle information. For example, the anonymization unit anonymizes information such as the location and time of the accident, the type of accident, and the vehicles involved. The analysis unit analyzes the traffic accident records anonymized by the anonymization unit. The analysis unit analyzes the anonymized traffic accident records using, for example, a large-scale language model. For example, the analysis unit analyzes a large amount of data and extracts accident patterns and associated risk factors. The extraction unit extracts accident patterns and risk factors from the records analyzed by the analysis unit. For example, the extraction unit identifies risk factors if the accident rate is high under specific road conditions or weather conditions. The provision unit provides accident prevention measures and countermeasures based on the risk factors extracted by the extraction unit. For example, if the risk is high under specific road conditions or weather conditions, the provision unit provides that risk to the driver through a car navigation system or location information application. As a result, the traffic accident risk analysis system according to the embodiment can minimize the risk of traffic accidents and realize a safe road environment. Some or all of the above-described processes in the anonymization unit, analysis unit, extraction unit, and provision unit may be performed using AI, for example, or without using AI. For example, the anonymization unit can input traffic accident records into the AI ​​and cause the AI ​​to delete personal information and specific vehicle information. The analysis unit can input the anonymized traffic accident records into the AI ​​and cause the AI ​​to extract accident patterns and risk factors. The extraction unit can input the analyzed records into the AI ​​and cause the AI ​​to identify risk factors. The provision unit can input the extracted risk factors into the AI ​​and cause the AI ​​to provide accident prevention measures and countermeasures.

[0065] The anonymization unit anonymizes traffic accident records. For example, it removes personal information and specific vehicle information to protect privacy. Specifically, traffic accident records may contain personal information such as the driver's name, address, contact information, vehicle license plate number, and vehicle identification number. The anonymization unit has the ability to automatically detect, delete, or mask this information. For example, it uses natural language processing technology to extract personal information from text data and anonymize it based on specific patterns. It can also use facial recognition and license plate recognition technologies to detect elements that can identify individuals in image data and blur or delete them. Furthermore, the anonymization unit can continuously learn and improve using machine learning algorithms to enhance the accuracy of anonymization. For example, it incorporates the results of past anonymization processes as feedback to improve anonymization accuracy. The anonymization unit also has a function to evaluate the quality of anonymized data and can reprocess data if the anonymization is insufficient. This allows the anonymization unit to safely and effectively anonymize traffic accident records, enabling data use while protecting privacy.

[0066] The analysis unit analyzes the anonymized traffic accident records from the anonymization unit. The analysis unit uses, for example, large-scale language models to analyze the anonymized traffic accident records. Specifically, the analysis unit analyzes the traffic accident records using natural language processing techniques to extract accident patterns and risk factors. For example, traffic accident records include information such as the location, time, weather, road conditions, type and speed of vehicles involved, and the driver's condition. The analysis unit statistically analyzes this information to identify accident rates and risk factors under specific conditions. Furthermore, the analysis unit can use machine learning algorithms to learn from past accident data and perform highly accurate analyses on new accident data. For example, it can use deep learning techniques to detect complex patterns and correlations and build accident prediction models. The analysis unit can also analyze data in real time and perform immediate risk assessments. This allows the analysis unit to quickly and accurately analyze traffic accident records and reveal accident patterns and risk factors.

[0067] The extraction unit extracts accident patterns and risk factors from records analyzed by the analysis unit. Specifically, the extraction unit identifies accident rates and risk factors under specific conditions based on the data provided by the analysis unit. For example, if the accident rate is high under specific road or weather conditions, it identifies the risk factors and provides information for taking countermeasures. The extraction unit uses machine learning algorithms to extract important features and patterns from the analyzed data and identify risk factors. For example, it uses algorithms such as decision trees and random forests to identify factors that contribute to accident occurrence and evaluate their importance. In addition, the extraction unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the extraction unit to accurately identify risk factors for traffic accidents and provide information for taking accident prevention measures and countermeasures. Furthermore, the extraction unit can visualize the extracted risk factors and provide them in an easy-to-understand format. For example, it can use graphs and charts to show the distribution and trends of risk factors, making them easily understandable to stakeholders. This allows the extraction unit to effectively identify risk factors for traffic accidents and provide information for implementing accident prevention measures and countermeasures.

[0068] The information provision unit provides accident prevention measures and countermeasures based on the risk factors identified by the information extraction unit. Specifically, if the risk is high under specific road conditions or weather conditions, the unit provides this risk information to the driver through car navigation systems and location-based apps. Based on real-time updated risk information, the information provision unit provides appropriate action instructions to the driver. For example, if a particular road is slippery, it will instruct the driver to slow down or suggest an alternative route. The information provision unit can also collect driver feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, by recording what actions drivers take based on the information provided and analyzing the results, the accuracy of the information provided can be improved. Furthermore, the information provision unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only car navigation screen displays but also voice guidance, smartphone notifications, and email. This allows the information provision unit to provide drivers with risk information quickly and reliably, minimizing the risk of traffic accidents. In addition, the information provision unit can also collaborate with local governments and traffic management authorities to provide information for implementing comprehensive traffic safety measures. For example, if accidents are occurring frequently in a particular area, this information can be provided to the local government to encourage road repairs and revisions to traffic regulations. This allows the information provider to effectively offer accident prevention measures and countermeasures, thereby creating a safer road environment.

[0069] The service provider can provide risk information that takes into account road conditions, congestion, weather, etc., through car navigation systems and location-based apps. For example, the service provider can provide risk information that takes into account road congestion, road surface conditions, rainfall, and visibility. By providing risk information that takes into account road conditions and weather, drivers can understand the risks in advance and take appropriate measures. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data such as road conditions and weather into AI and have the AI ​​provide risk information.

[0070] The information provision unit can provide information to transportation professionals. For example, the information provision unit can provide information on the risk of traffic accidents to truck drivers and logistics managers. By providing information to transportation professionals, the risks for transportation companies can be reduced. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can input information for transportation professionals into AI and have the AI ​​perform the provision of risk information.

[0071] The anonymization unit can remove personal information and specific vehicle information to protect privacy. For example, the anonymization unit can remove information such as names, addresses, phone numbers, license plate numbers, and vehicle make and model. By removing personal information and specific vehicle information, privacy can be protected. Some or all of the above processing in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input personal information and specific vehicle information into AI and have the AI ​​perform the deletion.

[0072] The analysis unit can analyze large amounts of data and extract accident patterns and risk factors. For example, the analysis unit can analyze large amounts of data and extract accident patterns and risk factors. For example, the analysis unit can analyze past accident data and traffic sensor data to extract accident patterns and risk factors. This allows for the accurate extraction of accident patterns and risk factors by analyzing large amounts of data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input large amounts of data into AI and have the AI ​​perform the extraction of accident patterns and risk factors.

[0073] The extraction unit can identify risk factors when the accident rate is high under specific road conditions or weather conditions. For example, the extraction unit can identify risk factors by considering factors such as road congestion, road surface conditions, rainfall, and poor visibility. This allows for the identification of risk factors when the accident rate is high under specific road conditions or weather conditions, enabling appropriate preventive measures to be taken. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input data on specific road conditions or weather conditions into AI and have the AI ​​identify risk factors.

[0074] The anonymization unit can estimate the user's emotions and adjust the level of anonymization based on the estimated emotions. For example, if the user is feeling anxious, the anonymization unit will thoroughly remove personal information and apply a higher level of anonymization. If the user is relaxed, the anonymization unit can perform minimal anonymization to maximize the usefulness of the data. Furthermore, if the user is in a hurry, the anonymization unit can apply standard anonymization techniques to perform anonymization quickly. This allows for better protection of user privacy by adjusting the level of anonymization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the anonymization unit may be performed using AI or not. For example, the anonymization unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the level of anonymization.

[0075] The anonymization unit can apply different anonymization algorithms based on the location and time of the accident. For example, in the case of an accident occurring in an urban area, the anonymization unit can remove detailed location information and convert it into broader location information. Furthermore, in the case of an accident occurring at night, the anonymization unit can remove time information and retain only the date. Additionally, in the case of an accident occurring on a highway, the anonymization unit can remove specific interchange information and retain only the route name. This allows for the protection of privacy while maintaining data usefulness by applying different anonymization algorithms based on the location and time of the accident. Some or all of the above processing in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input accident location and time data into AI and have the AI ​​perform the application of an anonymization algorithm.

[0076] The anonymization unit can select different anonymization methods depending on the type of data. For example, in the case of image data, the anonymization unit can blur faces using facial recognition technology. In the case of text data, the anonymization unit can also automatically mask personal names and addresses. Furthermore, in the case of audio data, the anonymization unit can remove personally identifiable information using speech recognition technology. By selecting different anonymization methods depending on the type of data, privacy can be protected while maximizing the usefulness of the data. Some or all of the above-described processes in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input the type of data into the AI ​​and have the AI ​​select the anonymization method.

[0077] The anonymization unit can estimate the user's emotions and determine the priority of anonymization based on the estimated emotions. For example, if the user is feeling anxious, the anonymization unit will prioritize the deletion of personal information. If the user is relaxed, the anonymization unit can also perform minimal anonymization to maintain the usefulness of the data. Furthermore, if the user is in a hurry, the anonymization unit can apply standard anonymization techniques to perform anonymization quickly. This allows for better protection of user privacy by determining the priority of anonymization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the anonymization unit may be performed using AI or not. For example, the anonymization unit can input user emotion data into an AI and have the AI ​​determine the priority of anonymization.

[0078] The anonymization unit can apply different anonymization methods based on the source of the data. For example, if the data is obtained from a public institution, the anonymization unit will apply a strict anonymization method. If the data is obtained from a private company, the anonymization unit can also apply an anonymization method in accordance with the company's policy. Furthermore, if the data is obtained from an individual, the anonymization unit can apply an anonymization method based on the Personal Information Protection Act. This allows for the protection of privacy while maintaining the usefulness of the data by applying different anonymization methods based on the source of the data. Some or all of the above processing in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input the data source into AI and have AI perform the application of the anonymization method.

[0079] The anonymization unit can set different levels of anonymization depending on the purpose of data use. For example, for data used for research purposes, the anonymization unit can perform detailed anonymization and completely remove personal information. For data used for commercial purposes, the anonymization unit can also perform the minimum necessary anonymization to maintain the usefulness of the data. Furthermore, for data used for public safety purposes, the anonymization unit can perform anonymization to the extent that specific individuals cannot be identified. In this way, by setting different levels of anonymization depending on the purpose of data use, privacy can be protected while maintaining the usefulness of the data. Some or all of the above processing in the anonymization unit may be performed using AI, for example, or not using AI. For example, the anonymization unit can input the purpose of data use into AI and have AI perform the setting of the anonymization level.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can summarize the analysis results concisely and provide them in an easy-to-understand format. If the user is relaxed, the analysis unit can provide detailed analysis results, including background information on the data. Furthermore, if the user is in a hurry, the analysis unit can perform the analysis quickly and provide results that get straight to the point. In this way, by adjusting the analysis method based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​perform the adjustment of the analysis method.

[0081] The analysis unit can apply different analysis algorithms depending on the type of data. For example, in the case of image data, the analysis unit can perform analysis by applying an image recognition algorithm. Furthermore, in the case of text data, the analysis unit can perform analysis by applying a natural language processing algorithm. In addition, in the case of audio data, the analysis unit can perform analysis by applying a speech recognition algorithm. This allows for analysis while maximizing the usefulness of the data by applying different analysis algorithms depending on the type of data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data type into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0082] The analysis unit can improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit can improve the accuracy of the analysis by identifying data with similar patterns based on past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results to identify outliers and abnormal values. Furthermore, the analysis unit can improve the accuracy by adjusting the parameters of the analysis algorithm based on past analysis results. In this way, the accuracy of the analysis can be improved by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis results into AI and have the AI ​​perform the analysis accuracy improvement.

[0083] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will prioritize the analysis of important data. If the user is relaxed, the analysis unit can perform a comprehensive analysis and provide detailed results. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis and provide concise results. This allows for prioritizing the analysis based on the user's emotions, thereby prioritizing the analysis of data that is important to the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​determine the priority of the analysis.

[0084] The analysis unit can adjust its analysis method based on the data acquisition time. For example, with the latest data, the analysis unit can apply a real-time analysis algorithm to perform analysis quickly. With historical data, the analysis unit can also apply a historical analysis algorithm to analyze long-term trends. Furthermore, with data from a specific period, the analysis unit can apply an analysis algorithm specific to that period. This allows for analysis while maximizing data usefulness by adjusting the analysis method based on the data acquisition time. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data acquisition time into the AI ​​and have the AI ​​adjust the analysis method.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data to improve overall analysis efficiency. Alternatively, the analysis unit can postpone the analysis of less relevant data and prioritize the analysis of important data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows for improved analysis efficiency by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data relevance into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0086] The extraction unit can estimate the user's emotions and determine the priority of risk factors to extract based on the estimated emotions. For example, if the user is feeling anxious, the extraction unit will prioritize extracting significant risk factors. If the user is relaxed, the extraction unit can also extract overall risk factors and provide detailed information. Furthermore, if the user is in a hurry, the extraction unit can quickly extract risk factors and provide concise information. This allows for the priority of risk factors to be extracted based on the user's emotions, thereby prioritizing those that are important to the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI or not. For example, the extraction unit can input user emotion data into an AI and have the AI ​​determine the priority of risk factors.

[0087] The extraction unit can improve the accuracy of extraction by considering the interrelationships between data. For example, the extraction unit can analyze the correlations between data and extract highly relevant risk factors. The extraction unit can also improve accuracy by identifying outliers and anomalies by considering the interrelationships between data. Furthermore, the extraction unit can improve accuracy by determining the priority of risk factors based on the interrelationships between data. In this way, the accuracy of risk factor extraction can be improved by considering the interrelationships between data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the interrelationships between data into AI and have the AI ​​perform the improvement of extraction accuracy.

[0088] The extraction unit can apply different extraction algorithms depending on the type of data. For example, in the case of image data, the extraction unit can apply an image recognition algorithm to extract risk factors. In the case of text data, the extraction unit can also apply a natural language processing algorithm to extract risk factors. Furthermore, in the case of audio data, the extraction unit can apply a speech recognition algorithm to extract risk factors. This allows for improved accuracy in extracting risk factors by applying the most suitable extraction algorithm according to the type of data. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the data type into the AI ​​and have the AI ​​perform the application of the extraction algorithm.

[0089] The extraction unit can estimate the user's emotions and adjust the display method of the extracted risk factors based on the estimated user emotions. For example, if the user is feeling anxious, the extraction unit can provide a simple and highly visible display method. If the user is relaxed, the extraction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the extraction unit can provide a concise display method. In this way, by adjusting the display method of risk factors based on the user's emotions, risk factors can be provided in a format that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI, for example, or not using AI. For example, the extraction unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0090] The extraction unit can perform extraction while considering the geographical distribution of the data. For example, if the accident rate is high in a particular area, the extraction unit will prioritize the extraction of risk factors in that area. The extraction unit can also improve accuracy by identifying outliers and anomalies while considering the geographical distribution. Furthermore, the extraction unit can improve accuracy by determining the priority of risk factors based on the geographical distribution. In this way, by considering the geographical distribution of the data, risk factors for each region can be accurately extracted. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input geographical distribution data into AI and have the AI ​​perform the extraction.

[0091] The extraction unit can improve the accuracy of its extractions by referring to relevant literature for the data. For example, the extraction unit can improve accuracy by identifying risk factors based on relevant literature. It can also improve accuracy by identifying outliers and abnormal values ​​by referring to relevant literature. Furthermore, the extraction unit can improve accuracy by determining the priority of risk factors based on relevant literature. In this way, the accuracy of risk factor extraction can be improved by referring to relevant literature for the data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input data from relevant literature into AI and have the AI ​​perform the improvement of extraction accuracy.

[0092] The service provider can estimate the user's emotions and adjust the way preventative measures and countermeasures are presented based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide concise and easy-to-understand language. If the user is relaxed, the service provider can also provide language that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide language that gets straight to the point. By adjusting the way preventative measures and countermeasures are presented based on the user's emotions, information can be provided in a format that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​adjust the language of expression.

[0093] The information provider can adjust the level of detail provided based on the importance of the risk factors. For example, in the case of a significant risk factor, the provider can provide detailed preventive measures and countermeasures. In the case of a minor risk factor, the provider can also provide concise preventive measures and countermeasures. Furthermore, the provider can dynamically adjust the level of detail of the information provided according to the importance of the risk factors. This allows the provider to provide users with important information at an appropriate level of detail by adjusting the level of detail based on the importance of the risk factors. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the importance of the risk factors into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the information provided.

[0094] The service provider can apply different service algorithms depending on the category of risk factor. For example, in the case of a weather-related risk factor, the service provider can provide preventive measures based on weather forecast data. Furthermore, in the case of a road-related risk factor, the service provider can provide countermeasures based on road management data. In addition, in the case of a congestion-related risk factor, the service provider can provide preventive measures based on traffic data. This allows the service provider to provide useful preventive measures and countermeasures to users by applying the most appropriate service algorithm according to the category of risk factor. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the risk factor categories into the AI ​​and have the AI ​​apply the service algorithm.

[0095] The service provider can estimate the user's emotions and determine the priority of preventative measures and countermeasures to provide based on the estimated emotions. For example, if the user is feeling anxious, the service provider will prioritize preventative measures against significant risk factors. If the user is relaxed, the service provider can also provide a more comprehensive set of preventative measures and countermeasures. Furthermore, if the user is in a hurry, the service provider can prioritize preventative measures and countermeasures that can be implemented quickly. This allows for the priority provision of information important to the user by determining the priority of preventative measures and countermeasures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI and have the AI ​​determine the priority of preventative measures and countermeasures.

[0096] The information delivery unit can adjust its delivery method based on when the risk factor occurred. For example, in the case of a recently occurring risk factor, the unit can provide preventive measures and countermeasures in real time. Furthermore, in the case of a risk factor that occurred in the past, the unit can provide preventive measures and countermeasures based on historical data. In addition, in the case of a risk factor that occurred during a specific period, the unit can provide preventive measures and countermeasures specific to that period. This allows the information to be delivered to the user at the optimal time by adjusting the delivery method based on when the risk factor occurred. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the timing of the risk factor's occurrence into the AI ​​and have the AI ​​adjust the delivery method.

[0097] The information delivery unit can adjust the order of delivery based on the relevance of risk factors. For example, the delivery unit can prioritize the delivery of highly relevant risk factors to improve the overall efficiency of preventive measures and countermeasures. Alternatively, the delivery unit can postpone less relevant risk factors and prioritize preventive measures and countermeasures for important risk factors. Furthermore, the delivery unit can dynamically adjust the order of delivery based on the relevance of risk factors. This allows for the efficient delivery of information important to the user by adjusting the order of delivery based on the relevance of risk factors. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not. For example, the delivery unit can input the relevance of risk factors into AI and have the AI ​​perform the adjustment of the delivery order.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The traffic accident risk analysis system can also be equipped with a real-time data acquisition unit. The real-time data acquisition unit acquires data in real time from traffic sensors, cameras, weather data, etc., and provides it to the analysis unit. For example, the real-time data acquisition unit can acquire road congestion and weather changes in real time and transmit them to the analysis unit. This allows the analysis unit to analyze accident risks more accurately based on the latest data. In addition, the real-time data acquisition unit can immediately collect data when a traffic accident occurs and provide it to the analysis unit quickly. As a result, the traffic accident risk analysis system enables real-time identification and countermeasures for accident risks, further improving traffic safety.

[0100] The traffic accident risk analysis system may also include a user feedback unit. The user feedback unit collects feedback from drivers and provides it to the analysis unit. For example, the user feedback unit can collect information about dangerous situations perceived by drivers and factors believed to be the cause of accidents. This allows the analysis unit to incorporate data based on the drivers' actual experiences into its analysis. Furthermore, the user feedback unit can adjust the preventative measures and countermeasures provided by the analysis unit based on the feedback provided by the drivers. This enables the traffic accident risk analysis system to provide more effective preventative measures and countermeasures based on the drivers' actual experiences.

[0101] The traffic accident risk analysis system can also be equipped with a prediction unit. This unit predicts future accident risks based on historical and real-time data. For example, it can predict accident rates on specific roads and time periods, providing drivers with advance warnings. Furthermore, the prediction unit can predict fluctuations in accident risk by considering factors such as weather changes and increased traffic volume. This allows the traffic accident risk analysis system to anticipate future accident risks and provide drivers with appropriate countermeasures.

[0102] The traffic accident risk analysis system can also include an education section. This education section provides drivers with educational content on traffic safety. For example, it can provide simulations based on past accident data and information on risk factors. It can also provide training programs to improve drivers' driving skills. This allows the traffic accident risk analysis system to raise drivers' awareness of traffic safety and prevent accidents from occurring.

[0103] The traffic accident risk analysis system can also include a community collaboration unit. This unit collects information on local traffic safety and provides it to the analysis unit. For example, the community collaboration unit can collect opinions and suggestions on traffic safety from local residents. It can also provide information on local traffic safety events and campaigns. This allows the traffic accident risk analysis system to reflect local traffic safety information and provide more effective preventative measures and countermeasures.

[0104] The traffic accident risk analysis system can estimate the user's emotions and adjust the display method of the analysis results based on those emotions. For example, if the user is feeling anxious, the analysis results can be summarized concisely and presented in an easy-to-understand format. If the user is relaxed, detailed analysis results can be provided, including background information on the data. Furthermore, if the user is in a hurry, the system can perform a rapid analysis and provide results that get straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, the system can provide analysis results that are easy for the user to understand.

[0105] The traffic accident risk analysis system can estimate the user's emotions and determine the priority of preventative measures and countermeasures based on those estimated emotions. For example, if the user is feeling anxious, preventative measures against significant risk factors will be prioritized. If the user is relaxed, overall preventative measures and countermeasures may be provided. Furthermore, if the user is in a hurry, preventative measures and countermeasures that can be implemented quickly will be prioritized. By prioritizing preventative measures and countermeasures based on the user's emotions, the system can prioritize providing information that is important to the user.

[0106] The traffic accident risk analysis system can estimate the user's emotions and adjust the display method of the extracted risk factors based on those emotions. For example, if the user is feeling anxious, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the essentials can be provided. In this way, by adjusting the display method of risk factors based on the user's emotions, risk factors can be presented in a format that is easy for the user to understand.

[0107] The traffic accident risk analysis system uses an anonymization unit to estimate the user's emotions and adjust the level of anonymization based on those emotions. For example, if the user is feeling anxious, the system will thoroughly remove personal information and apply a higher level of anonymization. If the user is relaxed, the system can perform minimal anonymization while maximizing the usefulness of the data. Furthermore, if the user is in a hurry, the system can apply standard anonymization techniques to quickly anonymize the data. By adjusting the level of anonymization based on the user's emotions, the system can better protect user privacy.

[0108] The traffic accident risk analysis system can estimate the user's emotions and adjust the way preventative measures and countermeasures are presented based on those estimated emotions. For example, if the user is feeling anxious, it can provide concise and easy-to-understand information. If the user is relaxed, it can provide detailed information. Furthermore, if the user is in a hurry, it can provide concise and to-the-point information. By adjusting the way preventative measures and countermeasures are presented based on the user's emotions, the system can provide information in a format that is easy for the user to understand.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The anonymization unit anonymizes the traffic accident records. Specifically, it removes personal information and specific vehicle information to protect privacy. For example, it anonymizes the location and time of the accident, the type of accident, and information about the vehicles involved. Step 2: The analysis unit analyzes the traffic accident records anonymized by the anonymization unit. For example, it uses a large-scale language model to analyze a large amount of data and extract accident patterns and associated risk factors. Step 3: The extraction unit extracts accident patterns and risk factors from the records analyzed by the analysis unit. For example, if the accident rate is high under certain road conditions or weather conditions, the risk factors are identified. Step 4: The provisioning unit provides accident prevention measures and countermeasures based on the risk factors identified by the extraction unit. For example, if the risk is high under specific road conditions or weather conditions, the provisioning unit will inform the driver of that risk through a car navigation system or location-based app.

[0111] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0114] Each of the multiple elements described above, including the anonymization unit, analysis unit, extraction unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the anonymization unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The extraction unit is implemented by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the anonymization unit, analysis unit, extraction unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the anonymization unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The extraction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the anonymization unit, analysis unit, extraction unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the anonymization unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The extraction unit is implemented by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the anonymization unit, analysis unit, extraction unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the anonymization unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The extraction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0164] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) An anonymization unit that anonymizes traffic accident records, An analysis unit analyzes the traffic accident records anonymized by the anonymization unit, An extraction unit extracts accident patterns and risk factors from the records analyzed by the aforementioned analysis unit, The system includes a providing unit that provides accident prevention measures and countermeasures based on the risk factors extracted by the extraction unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, We provide risk assessments that take into account road conditions, traffic congestion, weather, etc., through car navigation and location-based apps. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Providing information for transportation industry professionals. The system described in Appendix 1, characterized by the features described herein. (Note 4) The anonymization unit is, Personal information and specific vehicle information are deleted to protect privacy. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze large amounts of data to extract accident patterns and risk factors. The system described in Appendix 1, characterized by the features described herein. (Note 6) The extraction unit is When accident rates are high under specific road conditions or weather conditions, identify the risk factors. The system described in Appendix 1, characterized by the features described herein. (Note 7) The anonymization unit is, The system estimates the user's sentiment and adjusts the level of anonymization based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The anonymization unit is, Apply different anonymization algorithms based on the location and time of the accident. The system described in Appendix 1, characterized by the features described herein. (Note 9) The anonymization unit is, Select a different anonymization method depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The anonymization unit is, The system estimates the user's emotions and determines the priority of anonymization based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The anonymization unit is, Apply different anonymization methods based on the data source. The system described in Appendix 1, characterized by the features described herein. (Note 12) The anonymization unit is, Set different anonymization levels depending on the purpose of the data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Apply different analysis algorithms depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Improve the accuracy of the analysis by referring to past analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The analysis method is adjusted based on when the data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The extraction unit is We estimate the user's emotions and determine the priority of risk factors to extract based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The extraction unit is Improve extraction accuracy by considering the interrelationships between data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The extraction unit is Apply different extraction algorithms depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The extraction unit is We estimate user sentiment and adjust how risk factors extracted based on that estimated sentiment are displayed. The system described in Appendix 1, characterized by the features described herein. (Note 23) The extraction unit is Extraction should be performed considering the geographical distribution of the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The extraction unit is Referencing relevant literature for data improves extraction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way preventative measures and countermeasures are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, We adjust the level of detail provided based on the importance of the risk factors. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, Different delivery algorithms are applied depending on the category of risk factors. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of preventative measures and countermeasures to provide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The method of delivery will be adjusted based on when the risk factors occur. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, The order of offerings will be adjusted based on the relevance of risk factors. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An anonymization unit that anonymizes traffic accident records, An analysis unit analyzes the traffic accident records anonymized by the anonymization unit, An extraction unit extracts accident patterns and risk factors from the records analyzed by the aforementioned analysis unit, The system includes a providing unit that provides accident prevention measures and countermeasures based on the risk factors extracted by the extraction unit. A system characterized by the following features.

2. The aforementioned supply unit is, We provide risk assessments that take into account road conditions, traffic congestion, weather, etc., through car navigation and location-based apps. The system according to feature 1.

3. The aforementioned supply unit is, Providing information for transportation industry professionals. The system according to feature 1.

4. The anonymization unit is, Personal information and specific vehicle information are deleted to protect privacy. The system according to feature 1.

5. The aforementioned analysis unit, Analyze large amounts of data to extract accident patterns and risk factors. The system according to feature 1.

6. The extraction unit is When accident rates are high under specific road conditions or weather conditions, identify the risk factors. The system according to feature 1.

7. The anonymization unit is, The system estimates the user's sentiment and adjusts the level of anonymization based on the estimated sentiment. The system according to feature 1.

8. The anonymization unit is, Apply different anonymization algorithms based on the location and time of the accident. The system according to feature 1.

9. The anonymization unit is, Select a different anonymization method depending on the type of data. The system according to feature 1.

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