System

Abstract

An object of a system according to an embodiment is to calculate the fault ratio of a traffic accident accurately and quickly.SOLUTION: A system according to an embodiment includes a drive recorder information reading unit, a driver operation log reading unit, a past case reading unit, a trial record reading unit, and a fault ratio calculation unit. The drive recorder information reading unit reads drive recorder information. The driver operation log reading unit reads a driver operation log. The past case reading unit reads a past case. A trial record reading part reads a trial record. The fault ratio calculation unit analyzes the drive recorder information, the driver operation log, the past cases, and the trial records in a comprehensive manner, and automatically calculates the fault ratio.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately and quickly calculate the fault ratio in a traffic accident.

[0005] The system according to the embodiment aims to accurately and quickly calculate the fault ratio in a traffic accident. [Means for solving the problem]

[0006] The system according to the embodiment includes a drive recorder information reading unit, a driver operation log reading unit, a past case reading unit, a court record reading unit, and a fault ratio calculation unit. The drive recorder information reading unit reads drive recorder information. The driver operation log reading unit reads the driver operation log. The past case reading unit reads past cases. The court record reading unit reads court records. The fault ratio calculation unit comprehensively analyzes the drive recorder information, driver operation log, past cases, and court records, and automatically calculates the fault ratio. [Effects of the Invention]

[0007] The system according to the embodiment can accurately and quickly calculate the fault ratio in a traffic accident. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The automatic fault calculation system according to an embodiment of the present invention is a system that automatically calculates the fault ratio by reading in information from a drive recorder, the driver's operation log, the fault ratios of past cases, and court records. As a result, the automatic fault calculation system can quickly and accurately calculate the fault ratio in a traffic accident.

[0029] An automatic fault ratio calculation system according to an embodiment includes a drive recorder information reading unit, a driver operation log reading unit, a past case reading unit, a court record reading unit, and a fault ratio calculation unit. The drive recorder information reading unit reads drive recorder information. For example, video data from the drive recorder is input to a generation AI, which analyzes the video data to understand the accident situation and vehicle movement. The drive recorder information reading unit can also analyze the video data in real time to recreate the physical situation at the moment of the accident. The driver operation log reading unit reads the driver operation log. For example, data such as brake pressure, steering wheel operation, and accelerator pressure is input to the generation AI, which analyzes the operation log to identify how the driver's operation affected the accident. The past case reading unit reads past cases. For example, data on fault ratios in past traffic accidents is input to the generation AI, which analyzes the past cases and identifies standards for fault ratios applicable to the current accident. The court record reading unit reads court records. For example, court records related to past traffic accidents are input into the generation AI, which then analyzes the court records to determine the judgments made in past trials. The fault ratio calculation unit comprehensively analyzes drive recorder information, driver operation logs, past cases, and court records to automatically calculate the fault ratio. For example, the generation AI recreates the accident situation based on video data and operation logs, and determines the fault ratio by referring to past cases and court records. This allows the automatic fault ratio calculation system according to the embodiment to quickly and accurately calculate the fault ratio in traffic accidents. For example, it can be used by insurance companies to quickly determine the fault ratio in accidents. It can also be used as reference material for courts when determining the fault ratio.

[0030] The drive recorder information reading unit analyzes the vehicle's speed and acceleration from the video data in real time, making it possible to recreate in detail the physical circumstances at the moment of the accident.The drive recorder information reading unit uses, for example, generative AI to analyze the vehicle's speed and acceleration from the video data in the drive recorder in real time.For example, it tracks the movement of the vehicle in the video and quantifies changes in speed and acceleration.This makes it possible to recreate in detail the physical circumstances at the moment of the accident.

[0031] The drive recorder information reading unit analyzes the audio information included in the video data and can use the audio data, such as horns and collision sounds, to determine the situation of the accident. The drive recorder information reading unit, for example, analyzes the audio information included in the video data and extracts audio data, such as horns and collision sounds. For example, it detects specific sounds using voice recognition technology. This allows the audio data to be used to determine the situation of the accident.

[0032] The drive recorder information reading unit converts the video data into a 3D model, allowing for a three-dimensional reconstruction of the accident scene.The drive recorder information reading unit, for example, converts the video data into a 3D model, allowing for a three-dimensional reconstruction of the accident scene.For example, it generates a 3D model based on the positional information of objects in the video.This makes it possible to recreate the accident scene in three dimensions.

[0033] The drive recorder information reading unit can integrate video data with drive recorder information from other vehicles and perform accident analysis from multiple perspectives. The drive recorder information reading unit, for example, integrates drive recorder information from multiple vehicles and performs accident analysis from multiple perspectives. For example, it synchronizes and analyzes videos from different perspectives. This makes it possible to analyze accidents from multiple perspectives.

[0034] The driver operation log reading unit uses a generation AI to learn the driver's driving style from the operation log and can detect abnormal operation patterns. The driver operation log reading unit uses, for example, a generation AI to learn the driver's driving style from the operation log. For example, it analyzes the operation patterns of the brake and accelerator and detects abnormal operations. This makes it possible to detect abnormal operation patterns of the driver.

[0035] The driver operation log reading unit integrates the operation log with the vehicle sensor data, and can perform a detailed analysis of the influence of the driver's operation on the vehicle behavior. The driver operation log reading unit, for example, integrates the operation log with the vehicle sensor data, and analyzes the influence of the driver's operation on the vehicle behavior. For example, it integrates and analyzes braking operation and vehicle deceleration data. This allows a detailed analysis of the influence of the driver's operation on the vehicle behavior.

[0036] The driver operation log reading unit can compare the operation log with data from other drivers to evaluate driving skills and identify areas for improvement. The driver operation log reading unit, for example, compares the operation log with data from other drivers to evaluate driving skills. For example, it compares operation patterns under the same conditions to identify differences in skills. This makes it possible to evaluate driving skills and identify areas for improvement.

[0037] The driver operation log reading unit evaluates the driver's driving skills based on the operation log and can utilize the evaluation for calculating insurance premiums. The driver operation log reading unit evaluates the driver's driving skills based on the operation log, for example. For example, it analyzes the frequency of sudden braking and sudden steering operations and calculates a skill score. This allows the evaluation of driving skills to be utilized for calculating insurance premiums.

[0038] The past case reading unit uses generation AI to extract common accident patterns from past case data, thereby improving the accuracy of fault ratio predictions. The past case reading unit, for example, uses generation AI to extract common accident patterns from past case data. For example, it analyzes the frequency of accidents under the same conditions and identifies common patterns. This improves the accuracy of fault ratio predictions.

[0039] The past case reading unit analyzes past case data in chronological order to understand trends and changes in traffic accidents. The past case reading unit, for example, analyzes past case data in chronological order to understand trends in traffic accidents. For example, it analyzes fluctuations in the number of accidents and the percentage of fault by year. This makes it possible to understand trends and changes in traffic accidents.

[0040] The past case reading unit classifies past case data by region and can clarify region-specific traffic accident trends. The past case reading unit, for example, classifies past case data by region and analyzes region-specific traffic accident trends. For example, it identifies differences in accident occurrence rates and fault ratios between urban and rural areas. This makes it possible to clarify region-specific traffic accident trends.

[0041] The past case reading unit can compare past case data with data from other countries and reevaluate the fault ratio standards from an international perspective. The past case reading unit, for example, compares past case data with data from other countries and reevaluates the fault ratio standards from an international perspective. For example, it analyzes traffic accident data from different countries and identifies differences in fault ratios. This makes it possible to reevaluate the fault ratio standards from an international perspective.

[0042] The court record reading unit uses generation AI to analyze trends in precedents from court records and clarify the criteria for determining fault ratios. The court record reading unit, for example, uses generation AI to analyze trends in precedents from court records. For example, it analyzes past judgment data and identifies the criteria for determining fault ratios. This makes it possible to clarify the criteria for determining fault ratios.

[0043] The court record reading unit can perform text mining on court records to extract keywords and phrases that affect the degree of fault. The court record reading unit, for example, performs text mining on court records to extract keywords and phrases that affect the degree of fault. For example, it extracts important keywords from the judgment. This makes it possible to extract keywords and phrases that affect the degree of fault.

[0044] The court record reading unit can integrate court records with other legal documents to build comprehensive legal decision criteria. The court record reading unit, for example, integrates court records with other legal documents to build comprehensive legal decision criteria. For example, it integrates and analyzes case law databases and legal documents. This makes it possible to build comprehensive legal decision criteria.

[0045] The court record reading unit can visualize court records to make it easier to visually understand the trends in court cases and the standards for determining fault ratios. The court record reading unit, for example, visualizes court records to visually display the trends in court cases and the standards for determining fault ratios. For example, it visualizes the court case data using graphs and charts. This makes it easier to visually understand the trends in court cases and the standards for determining fault ratios.

[0046] The fault percentage calculation unit can use generation AI to integrate multiple data sources and optimize the fault percentage calculation algorithm. The fault percentage calculation unit can, for example, use generation AI to integrate multiple data sources and optimize the fault percentage calculation algorithm. For example, it can integrate and analyze video data and operation logs from a drive recorder. This makes it possible to optimize the fault percentage calculation algorithm.

[0047] The fault proportion calculation unit can simulate the calculation results of the fault proportion and evaluate the fluctuation of the fault proportion in different scenarios. The fault proportion calculation unit, for example, simulates the calculation results of the fault proportion and evaluates the fluctuation of the fault proportion in different scenarios. For example, the simulation is performed taking into account changes in weather and road conditions. This makes it possible to evaluate the fluctuation of the fault proportion in different scenarios.

[0048] The fault percentage calculation unit can share the results of the fault percentage calculation with other insurance companies and legal institutions, thereby achieving standardization throughout the industry.The fault percentage calculation unit can, for example, share the results of the fault percentage calculation with other insurance companies and legal institutions, thereby achieving standardization throughout the industry.For example, a shared platform can be built to share data.This can achieve standardization throughout the industry.

[0049] The fault ratio calculation unit updates the calculation result of the fault ratio in real time, and can respond to changes in the situation after the accident. The fault ratio calculation unit, for example, updates the calculation result of the fault ratio in real time, and can respond to changes in the situation after the accident. For example, the calculation result is updated when new evidence or data is added. This makes it possible to respond to changes in the situation after the accident.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The automatic fault ratio calculation system can further include a weather data reading unit. The weather data reading unit obtains weather data at the time of the accident and provides it to the fault ratio calculation unit. For example, it obtains weather information at the time of the accident from the Japan Meteorological Agency's database and analyzes the impact of weather conditions such as rain and snow on the accident. This makes it possible to calculate the fault ratio taking weather conditions into account. Furthermore, the weather data reading unit can also analyze factors such as road slipperiness and poor visibility, supporting more accurate calculation of the fault ratio.

[0052] The automatic fault ratio calculation system can further include a vehicle condition monitoring unit. The vehicle condition monitoring unit acquires the vehicle's mechanical condition at the time of the accident and provides it to the fault ratio calculation unit. For example, it monitors the vehicle's brake system, tire condition, engine operating status, etc. in real time and analyzes this data. This makes it possible to calculate the fault ratio taking into account the impact of the vehicle's mechanical condition on the accident. Furthermore, the vehicle condition monitoring unit can analyze the vehicle's maintenance history and failure history to identify the cause of the accident in more detail.

[0053] The automatic fault calculation system can further include a driver's health condition monitoring unit. The driver's health condition monitoring unit acquires driver health data and provides it to the fault proportion calculation unit. For example, it monitors the driver's heart rate, blood pressure, stress level, etc. in real time and analyzes this data. This makes it possible to calculate the fault proportion taking into account the impact of the driver's health condition on the accident. Furthermore, the driver's health condition monitoring unit can also analyze past health examination results and medical history to identify the cause of the accident in more detail.

[0054] The automatic fault ratio calculation system can further include a traffic volume data reading unit. The traffic volume data reading unit acquires traffic volume data at the time of the accident and provides it to the fault ratio calculation unit. For example, it analyzes traffic volume data acquired from traffic sensors or cameras to understand the traffic conditions at the time of the accident. This makes it possible to calculate the fault ratio taking into account the impact of traffic volume on the accident. Furthermore, the traffic volume data reading unit can compare the data with past traffic volume data and identify the impact of abnormal traffic conditions on the accident.

[0055] The automatic fault ratio calculation system can further include a road condition data reading unit. The road condition data reading unit acquires road condition data at the time of the accident and provides it to the fault ratio calculation unit. For example, the road condition data reading unit analyzes the road pavement condition, construction information, presence or absence of traffic signs, etc. to understand the road conditions at the time of the accident. This makes it possible to calculate the fault ratio taking into account the impact of road conditions on the accident. Furthermore, the road condition data reading unit can compare the data with past road condition data to identify the impact of abnormal road conditions on the accident.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The drive recorder information reading unit reads the drive recorder information. For example, the drive recorder video data is input into the generation AI, which then analyzes the video data to understand the circumstances of the accident and the vehicle's movements. The drive recorder information reading unit can also analyze the video data in real time to recreate the physical situation at the moment of the accident. Step 2: The driver operation log reader reads the driver operation log. For example, data such as brake pressure, steering wheel operation, and accelerator pressure is input into the generation AI, which then analyzes the operation log to determine how the driver's operations contributed to the accident. Step 3: The past case reading unit reads past cases. For example, data on the percentage of fault in past traffic accidents is input into the generation AI, which then analyzes the past cases and identifies standards for the percentage of fault that can be applied to the current accident. Step 4: The court record reader reads court records. For example, court records related to past traffic accidents are input into the generation AI, which then analyzes the court records to determine what decisions were made in past trials. Step 5: The fault calculation unit comprehensively analyzes the drive recorder information, driver operation logs, past cases, and court records to automatically calculate the fault ratio. For example, the generation AI recreates the accident situation based on the video data and operation logs, and determines the fault ratio by referring to past cases and court records.

[0058] (Example 2) The automatic fault calculation system according to an embodiment of the present invention is a system that automatically calculates the fault ratio by reading in information from a drive recorder, the driver's operation log, the fault ratios of past cases, and court records. As a result, the automatic fault calculation system can quickly and accurately calculate the fault ratio in a traffic accident.

[0059] An automatic fault ratio calculation system according to an embodiment includes a drive recorder information reading unit, a driver operation log reading unit, a past case reading unit, a court record reading unit, and a fault ratio calculation unit. The drive recorder information reading unit reads drive recorder information. For example, video data from the drive recorder is input to a generation AI, which analyzes the video data to understand the accident situation and vehicle movement. The drive recorder information reading unit can also analyze the video data in real time to recreate the physical situation at the moment of the accident. The driver operation log reading unit reads the driver operation log. For example, data such as brake pressure, steering wheel operation, and accelerator pressure is input to the generation AI, which analyzes the operation log to identify how the driver's operation affected the accident. The past case reading unit reads past cases. For example, data on fault ratios in past traffic accidents is input to the generation AI, which analyzes the past cases and identifies standards for fault ratios applicable to the current accident. The court record reading unit reads court records. For example, court records related to past traffic accidents are input into the generation AI, which then analyzes the court records to determine the judgments made in past trials. The fault ratio calculation unit comprehensively analyzes drive recorder information, driver operation logs, past cases, and court records to automatically calculate the fault ratio. For example, the generation AI recreates the accident situation based on video data and operation logs, and determines the fault ratio by referring to past cases and court records. This allows the automatic fault ratio calculation system according to the embodiment to quickly and accurately calculate the fault ratio in traffic accidents. For example, it can be used by insurance companies to quickly determine the fault ratio in accidents. It can also be used as reference material for courts when determining the fault ratio.

[0060] The drive recorder information reading unit analyzes the vehicle's speed and acceleration from the video data in real time, making it possible to recreate in detail the physical circumstances at the moment of the accident.The drive recorder information reading unit uses, for example, generative AI to analyze the vehicle's speed and acceleration from the video data in the drive recorder in real time.For example, it tracks the movement of the vehicle in the video and quantifies changes in speed and acceleration.This makes it possible to recreate in detail the physical circumstances at the moment of the accident.

[0061] The drive recorder information reading unit analyzes the audio information included in the video data and can use the audio data, such as horns and collision sounds, to determine the situation of the accident. The drive recorder information reading unit, for example, analyzes the audio information included in the video data and extracts audio data, such as horns and collision sounds. For example, it detects specific sounds using voice recognition technology. This allows the audio data to be used to determine the situation of the accident.

[0062] The drive recorder information reading unit uses the emotion estimation function to analyze the facial expressions of the driver and pedestrians in the video and can grasp their emotional states before and after the accident. The drive recorder information reading unit, for example, uses the emotion estimation function to analyze the facial expressions of the driver and pedestrians in the video. For example, it uses face recognition technology to detect changes in facial expressions and estimate their emotional states. This makes it possible to grasp their emotional states before and after the accident.

[0063] The drive recorder information reading unit converts the video data into a 3D model, allowing for a three-dimensional reconstruction of the accident scene.The drive recorder information reading unit, for example, converts the video data into a 3D model, allowing for a three-dimensional reconstruction of the accident scene.For example, it generates a 3D model based on the positional information of objects in the video.This makes it possible to recreate the accident scene in three dimensions.

[0064] The drive recorder information reading unit can integrate video data with drive recorder information from other vehicles and perform accident analysis from multiple perspectives. The drive recorder information reading unit, for example, integrates drive recorder information from multiple vehicles and performs accident analysis from multiple perspectives. For example, it synchronizes and analyzes videos from different perspectives. This makes it possible to analyze accidents from multiple perspectives.

[0065] The drive recorder information reading unit uses the emotion estimation function to analyze changes in the driver's facial expression in the video in real time, and can detect signs of an accident. The drive recorder information reading unit, for example, uses the emotion estimation function to analyze changes in the driver's facial expression in the video in real time. For example, facial recognition technology is used to detect changes in facial expression and identify signs of an accident. This makes it possible to detect signs of an accident in real time.

[0066] The driver operation log reading unit uses a generation AI to learn the driver's driving style from the operation log and can detect abnormal operation patterns. The driver operation log reading unit uses, for example, a generation AI to learn the driver's driving style from the operation log. For example, it analyzes the operation patterns of the brake and accelerator and detects abnormal operations. This makes it possible to detect abnormal operation patterns of the driver.

[0067] The driver operation log reading unit integrates the operation log with the vehicle sensor data, and can perform a detailed analysis of the influence of the driver's operation on the vehicle behavior. The driver operation log reading unit, for example, integrates the operation log with the vehicle sensor data, and analyzes the influence of the driver's operation on the vehicle behavior. For example, it integrates and analyzes braking operation and vehicle deceleration data. This allows a detailed analysis of the influence of the driver's operation on the vehicle behavior.

[0068] The driver operation log reading unit can use the emotion estimation function to estimate the driver's stress level from the operation log and evaluate the risk of an accident. The driver operation log reading unit, for example, uses the emotion estimation function to estimate the driver's stress level from the operation log. For example, the frequency of sudden braking and sudden steering operations is analyzed to evaluate the stress level. This makes it possible to estimate the driver's stress level and evaluate the risk of an accident.

[0069] The driver operation log reading unit can compare the operation log with data from other drivers to evaluate driving skills and identify areas for improvement. The driver operation log reading unit, for example, compares the operation log with data from other drivers to evaluate driving skills. For example, it compares operation patterns under the same conditions to identify differences in skills. This makes it possible to evaluate driving skills and identify areas for improvement.

[0070] The driver operation log reading unit evaluates the driver's driving skills based on the operation log and can utilize the evaluation for calculating insurance premiums. The driver operation log reading unit evaluates the driver's driving skills based on the operation log, for example. For example, it analyzes the frequency of sudden braking and sudden steering operations and calculates a skill score. This allows the evaluation of driving skills to be utilized for calculating insurance premiums.

[0071] The driver operation log reading unit uses the emotion estimation function to monitor the driver's emotional changes from the operation log in real time and can propose accident prevention measures. The driver operation log reading unit uses, for example, the emotion estimation function to monitor the driver's emotional changes from the operation log in real time. For example, it analyzes the frequency of sudden braking and sudden steering operations to identify emotional changes. This makes it possible to monitor the driver's emotional changes in real time and propose accident prevention measures.

[0072] The past case reading unit uses generation AI to extract common accident patterns from past case data, thereby improving the accuracy of fault ratio predictions. The past case reading unit, for example, uses generation AI to extract common accident patterns from past case data. For example, it analyzes the frequency of accidents under the same conditions and identifies common patterns. This improves the accuracy of fault ratio predictions.

[0073] The past case reading unit analyzes past case data in chronological order to understand trends and changes in traffic accidents. The past case reading unit, for example, analyzes past case data in chronological order to understand trends in traffic accidents. For example, it analyzes fluctuations in the number of accidents and the percentage of fault by year. This makes it possible to understand trends and changes in traffic accidents.

[0074] The past case reading unit can use the emotion estimation function to analyze the emotional reactions of the parties involved in past cases and identify factors that influence the determination of fault ratio. The past case reading unit, for example, uses the emotion estimation function to analyze the emotional reactions of the parties involved in past cases. For example, it estimates emotional states from court records and testimony data. This makes it possible to identify factors that influence the determination of fault ratio.

[0075] The past case reading unit classifies past case data by region and can clarify region-specific traffic accident trends. The past case reading unit, for example, classifies past case data by region and analyzes region-specific traffic accident trends. For example, it identifies differences in accident occurrence rates and fault ratios between urban and rural areas. This makes it possible to clarify region-specific traffic accident trends.

[0076] The past case reading unit can compare past case data with data from other countries and reevaluate the fault ratio standards from an international perspective. The past case reading unit, for example, compares past case data with data from other countries and reevaluates the fault ratio standards from an international perspective. For example, it analyzes traffic accident data from different countries and identifies differences in fault ratios. This makes it possible to reevaluate the fault ratio standards from an international perspective.

[0077] The past case reading unit uses the emotion estimation function to analyze emotional data of the parties involved in past cases, and can set standards for fault ratios that are easy to empathize with emotionally. The past case reading unit, for example, uses the emotion estimation function to analyze emotional data of the parties involved in past cases. For example, it estimates emotional states from court records and testimony data. This makes it possible to set standards for fault ratios that are easy to empathize with emotionally.

[0078] The court record reading unit uses generation AI to analyze trends in precedents from court records and clarify the criteria for determining fault ratios. The court record reading unit, for example, uses generation AI to analyze trends in precedents from court records. For example, it analyzes past judgment data and identifies the criteria for determining fault ratios. This makes it possible to clarify the criteria for determining fault ratios.

[0079] The court record reading unit can perform text mining on court records to extract keywords and phrases that affect the degree of fault. The court record reading unit, for example, performs text mining on court records to extract keywords and phrases that affect the degree of fault. For example, it extracts important keywords from the judgment. This makes it possible to extract keywords and phrases that affect the degree of fault.

[0080] The court record reading unit can use the emotion estimation function to analyze the emotional expressions of the parties in the court record and identify the emotional factors that influenced the verdict. The court record reading unit, for example, uses the emotion estimation function to analyze the emotional expressions of the parties in the court record. For example, it estimates the emotional state from testimony and statements. This makes it possible to identify the emotional factors that influenced the verdict.

[0081] The court record reading unit can integrate court records with other legal documents to build comprehensive legal decision criteria. The court record reading unit, for example, integrates court records with other legal documents to build comprehensive legal decision criteria. For example, it integrates and analyzes case law databases and legal documents. This makes it possible to build comprehensive legal decision criteria.

[0082] The court record reading unit can visualize court records to make it easier to visually understand the trends in court cases and the standards for determining fault ratios. The court record reading unit, for example, visualizes court records to visually display the trends in court cases and the standards for determining fault ratios. For example, it visualizes the court case data using graphs and charts. This makes it easier to visually understand the trends in court cases and the standards for determining fault ratios.

[0083] The court record reading unit can use the emotion estimation function to analyze the emotion data of the parties in the court record and identify trends in judgments that are likely to be emotionally relatable. The court record reading unit, for example, uses the emotion estimation function to analyze the emotion data of the parties in the court record. For example, it estimates the emotional state from testimony and statements. This makes it possible to identify trends in judgments that are likely to be emotionally relatable.

[0084] The fault percentage calculation unit can use generation AI to integrate multiple data sources and optimize the fault percentage calculation algorithm. The fault percentage calculation unit can, for example, use generation AI to integrate multiple data sources and optimize the fault percentage calculation algorithm. For example, it can integrate and analyze video data and operation logs from a drive recorder. This makes it possible to optimize the fault percentage calculation algorithm.

[0085] The fault proportion calculation unit can simulate the calculation results of the fault proportion and evaluate the fluctuation of the fault proportion in different scenarios. The fault proportion calculation unit, for example, simulates the calculation results of the fault proportion and evaluates the fluctuation of the fault proportion in different scenarios. For example, the simulation is performed taking into account changes in weather and road conditions. This makes it possible to evaluate the fluctuation of the fault proportion in different scenarios.

[0086] The fault ratio calculation unit uses the emotion estimation function to analyze the emotional reactions of the parties involved to the fault ratio calculation result, and can provide a result that is emotionally convincing. The fault ratio calculation unit, for example, uses the emotion estimation function to analyze the emotional reactions of the parties involved to the fault ratio calculation result. For example, it calculates an emotion score for the calculation result and evaluates the degree of convincingness. This makes it possible to provide a result that is emotionally convincing.

[0087] The fault percentage calculation unit can share the results of the fault percentage calculation with other insurance companies and legal institutions, thereby achieving standardization throughout the industry.The fault percentage calculation unit can, for example, share the results of the fault percentage calculation with other insurance companies and legal institutions, thereby achieving standardization throughout the industry.For example, a shared platform can be built to share data.This can achieve standardization throughout the industry.

[0088] The fault ratio calculation unit updates the calculation result of the fault ratio in real time, and can respond to changes in the situation after the accident. The fault ratio calculation unit, for example, updates the calculation result of the fault ratio in real time, and can respond to changes in the situation after the accident. For example, the calculation result is updated when new evidence or data is added. This makes it possible to respond to changes in the situation after the accident.

[0089] The fault ratio calculation unit uses the emotion estimation function to monitor the emotional reactions of the parties involved to the fault ratio calculation results in real time, and can propose optimal countermeasures. The fault ratio calculation unit, for example, uses the emotion estimation function to monitor the emotional reactions of the parties involved to the fault ratio calculation results in real time. For example, it evaluates the parties' degree of satisfaction based on the emotion score. This makes it possible to propose optimal countermeasures.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The automatic fault ratio calculation system can further include a weather data reading unit. The weather data reading unit obtains weather data at the time of the accident and provides it to the fault ratio calculation unit. For example, it obtains weather information at the time of the accident from the Japan Meteorological Agency's database and analyzes the impact of weather conditions such as rain and snow on the accident. This makes it possible to calculate the fault ratio taking weather conditions into account. Furthermore, the weather data reading unit can also analyze factors such as road slipperiness and poor visibility, supporting more accurate calculation of the fault ratio.

[0092] The automatic fault ratio calculation system can further include a vehicle condition monitoring unit. The vehicle condition monitoring unit acquires the vehicle's mechanical condition at the time of the accident and provides it to the fault ratio calculation unit. For example, it monitors the vehicle's brake system, tire condition, engine operating status, etc. in real time and analyzes this data. This makes it possible to calculate the fault ratio taking into account the impact of the vehicle's mechanical condition on the accident. Furthermore, the vehicle condition monitoring unit can analyze the vehicle's maintenance history and failure history to identify the cause of the accident in more detail.

[0093] The automatic fault calculation system can further include a driver's health condition monitoring unit. The driver's health condition monitoring unit acquires driver health data and provides it to the fault proportion calculation unit. For example, it monitors the driver's heart rate, blood pressure, stress level, etc. in real time and analyzes this data. This makes it possible to calculate the fault proportion taking into account the impact of the driver's health condition on the accident. Furthermore, the driver's health condition monitoring unit can also analyze past health examination results and medical history to identify the cause of the accident in more detail.

[0094] The automatic fault ratio calculation system can further include a traffic volume data reading unit. The traffic volume data reading unit acquires traffic volume data at the time of the accident and provides it to the fault ratio calculation unit. For example, it analyzes traffic volume data acquired from traffic sensors or cameras to understand the traffic conditions at the time of the accident. This makes it possible to calculate the fault ratio taking into account the impact of traffic volume on the accident. Furthermore, the traffic volume data reading unit can compare the data with past traffic volume data and identify the impact of abnormal traffic conditions on the accident.

[0095] The automatic fault ratio calculation system can further include a road condition data reading unit. The road condition data reading unit acquires road condition data at the time of the accident and provides it to the fault ratio calculation unit. For example, the road condition data reading unit analyzes the road pavement condition, construction information, presence or absence of traffic signs, etc. to understand the road conditions at the time of the accident. This makes it possible to calculate the fault ratio taking into account the impact of road conditions on the accident. Furthermore, the road condition data reading unit can compare the data with past road condition data to identify the impact of abnormal road conditions on the accident.

[0096] The automatic fault calculation system can also use a driver emotion estimation function to monitor the driver's emotional state in real time and assess the risk of an accident. For example, it can estimate the driver's emotional state from their facial expressions and voice, and determine that if emotions such as stress or impatience are increasing, the risk of an accident is higher. This makes it possible to calculate the fault ratio taking the driver's emotional state into account. Furthermore, the driver emotion estimation function can compare it with past emotional data and identify the impact that abnormal emotional changes may have on an accident.

[0097] The automatic fault calculation system also uses an emotion estimation function to analyze the emotional responses of witnesses at the accident scene, enabling a more detailed understanding of the accident situation. For example, the system estimates the emotional state of a witness from their facial expressions and voice, and if emotions such as surprise or fear are strongly expressed, it determines that the impact of the accident was great. This makes it possible to calculate fault percentages that take the emotional responses of witnesses into account. Furthermore, the eyewitness emotion estimation function can integrate the emotional data of multiple witnesses to comprehensively analyze the accident situation.

[0098] The automatic fault percentage calculation system can also use an emotion estimation function to analyze the emotional reactions of those involved after the accident and assist in calculating a fault percentage that is emotionally convincing. For example, the emotional state of those involved after the accident can be estimated from their facial expressions and voices, and if strong emotions such as anger or sadness are expressed, the system can evaluate the degree of satisfaction with the calculated fault percentage. This makes it possible to calculate fault percentages that take into account the emotional reactions of those involved. Furthermore, the emotion estimation function for those involved can compare it with past emotional data and set standards for fault percentages that are emotionally convincing.

[0099] The automated fault calculation system can also use an emotion estimation function to analyze the emotional expressions of the parties in court records and identify the emotional factors that influenced the verdict. For example, it can estimate the emotional state from testimony and statements and identify parts that express strong emotions. This makes it possible to calculate the fault ratio while taking emotional factors into account. Furthermore, the court record emotion estimation function can compare it with past case data and identify trends in verdicts that are likely to resonate emotionally.

[0100] The automated fault calculation system can also use an emotion estimation function to monitor the emotional reactions of those involved in response to the fault calculation results in real time and propose optimal countermeasures. For example, it can calculate an emotion score for the calculation results and evaluate the degree of satisfaction of those involved. This makes it possible to propose countermeasures that are likely to be emotionally acceptable. Furthermore, the emotion estimation function can compare the results with past emotion data and identify countermeasures that are likely to be emotionally relatable.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The drive recorder information reading unit reads the drive recorder information. For example, the drive recorder video data is input into the generation AI, which then analyzes the video data to understand the circumstances of the accident and the vehicle's movements. The drive recorder information reading unit can also analyze the video data in real time to recreate the physical situation at the moment of the accident. Step 2: The driver operation log reader reads the driver operation log. For example, data such as brake pressure, steering wheel operation, and accelerator pressure is input into the generation AI, which then analyzes the operation log to determine how the driver's operations contributed to the accident. Step 3: The past case reading unit reads past cases. For example, data on the percentage of fault in past traffic accidents is input into the generation AI, which then analyzes the past cases and identifies standards for the percentage of fault that can be applied to the current accident. Step 4: The court record reader reads court records. For example, court records related to past traffic accidents are input into the generation AI, which then analyzes the court records to determine what decisions were made in past trials. Step 5: The fault calculation unit comprehensively analyzes the drive recorder information, driver operation logs, past cases, and court records to automatically calculate the fault ratio. For example, the generation AI recreates the accident situation based on the video data and operation logs, and determines the fault ratio by referring to past cases and court records.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a drive recorder information reading unit that reads drive recorder information; a driver operation log reading unit that reads a driver operation log; a past case reading unit that reads past cases; a court record reading unit that reads court records; and a fault ratio calculation unit that comprehensively analyzes drive recorder information, driver operation logs, past cases, and court records to automatically calculate the fault ratio. A system characterized by:

2. The drive recorder information reading section Analyzing vehicle speed and acceleration in real time from video data to recreate the physical conditions at the moment of the accident in detail 2. The system of claim 1.

3. The drive recorder information reading section Converting video data into a 3D model to create a three-dimensional reconstruction of the accident scene 2. The system of claim 1.

4. The driver operation log reading section is Using generative AI, the system learns the driver's driving style from operation logs and detects abnormal operation patterns.

2. The system of claim 1.

5. The past case loading section is Using generative AI to extract common accident patterns from past case data and improve the accuracy of fault ratio predictions 2. The system of claim 1.

6. The court record reading section Using generative AI to analyze trends in precedents from court records and clarify the criteria for determining fault ratios 2. The system of claim 1.

7. The fault ratio calculation section Analyze the emotional reactions of those involved to the calculation results of the fault ratio and provide results that are easy to understand emotionally.

2. The system of claim 1.

8. The drive recorder information reading section Analyzing the facial expressions of drivers and pedestrians in the video to understand their emotional state before and after an accident 2. The system of claim 1.

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