system

Abstract

An object of a system according to an embodiment is to accurately reproduce and visualize a situation of a traffic accident.SOLUTION: A system according to an embodiment includes a video analysis unit, an operation log analysis unit, an accident reproduction unit, and a visualization unit. The video analysis unit analyzes video data of the drive recorder. The operation log analysis unit analyzes an operation log of a driver. The accident reproducer reproduces the situation of the accident based on the data analyzed by the video analyzer and the operation log analyzer. The visualizer visualizes the situation of the accident reproduced by the accident reproducer as computer graphics (CG).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 recreate and visualize the circumstances of a traffic accident.

[0005] The system according to the embodiment aims to accurately recreate and visualize the situation of a traffic accident. [Means for solving the problem]

[0006] The system according to the embodiment includes a video analysis unit, an operation log analysis unit, an accident reconstruction unit, and a visualization unit. The video analysis unit analyzes video data from a drive recorder. The operation log analysis unit analyzes the driver's operation log. The accident reconstruction unit recreates the accident situation based on the data analyzed by the video analysis unit and the operation log analysis unit. The visualization unit visualizes the accident situation recreated by the accident reconstruction unit as computer graphics (CG). [Effects of the Invention]

[0007] The system according to the embodiment can accurately recreate and visualize the situation of 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 traffic accident reenactment system according to an embodiment of the present invention is a system that automatically recreates the situation of a traffic accident using information from a drive recorder and a driver's operation log, and visualizes it as CG. This makes it easier to visually understand the cause and situation of the accident.

[0029] A traffic accident reconstruction system according to an embodiment includes a video analysis unit, an operation log analysis unit, an accident reconstruction unit, and a visualization unit. The video analysis unit analyzes video data from a drive recorder. For example, the video analysis unit uses a generation AI to analyze the vehicle's position, speed, and surrounding conditions from the video data. The video analysis unit can also automatically recognize weather and road conditions from the video data. The operation log analysis unit analyzes the driver's operation log. For example, the operation log analysis unit uses a generation AI to analyze accelerator and brake operation, steering wheel movement, and turn signal usage. The operation log analysis unit can also analyze the driver's driving style and reaction time from the operation log. The accident reconstruction unit recreates the accident situation based on the data analyzed by the video analysis unit and the operation log analysis unit. For example, the accident reconstruction unit recreates the vehicle's movement and surrounding conditions as CG using a generation AI. The accident reconstruction unit can also analyze the vehicle's dynamic behavior at the time of the accident based on the video data. The visualization unit visualizes the accident situation recreated by the accident reconstruction unit as CG. For example, the visualization unit uses the accident situation recreated using the generation AI as evidence to clarify the cause of the accident and who is responsible. The visualization unit can also be used as a tool for relevant organizations, such as insurance companies and police, to quickly and accurately understand the accident situation based on the recreated accident situation. This allows the traffic accident reconstruction system according to the embodiment to recreate the traffic accident situation in detail and make it visually easy to understand. For example, this can be used as evidence to clarify the cause of the accident, allowing relevant organizations, such as insurance companies and police, to quickly and accurately understand the accident situation. Furthermore, the driver can review the accident situation and use it to improve their future driving.

[0030] The video analysis unit can automatically recognize weather or road conditions from video data and consider them as factors in an accident. For example, the video analysis unit can use generative AI to automatically recognize weather (sunny, rainy, snowy, etc.) from video data from a dashcam and consider them as factors in an accident. For example, it can analyze visibility conditions and road surface reflections in the video. The video analysis unit can also automatically recognize road conditions (e.g., icy roads, traffic jams, construction work, etc.) and consider them as factors in an accident. This allows for a more accurate understanding of the causes of accidents by taking weather and road conditions into account.

[0031] The video analysis unit can analyze audio information included in the video data and recreate the acoustic environment at the time of the accident. For example, the video analysis unit analyzes audio information included in the video data and identifies the horn sound at the time of the accident. For example, it analyzes the audio waveform and extracts the frequency and volume of the horn. The video analysis unit can also analyze audio information such as the collision sound and the driver's voice and recreate the acoustic environment at the time of the accident. For example, it analyzes the intensity and direction of the collision sound. By recreating the acoustic environment in this way, the circumstances of the accident can be understood in more detail.

[0032] The video analysis unit can use video data to analyze the vehicle's maintenance status before and after the accident and consider it as a factor in the accident. For example, the video analysis unit analyzes video data to automatically recognize the vehicle's external condition (e.g., tire wear, damage to the body). For example, it can measure tire tread depth using image analysis technology. The video analysis unit can also analyze maintenance conditions such as the condition of the brakes and the condition of the engine and consider them as factors in the accident. For example, it can analyze the wear of brake pads and abnormal engine noises. By taking the vehicle's maintenance status into consideration, the factors in the accident can be more accurately identified.

[0033] The video analysis unit generates a 3D model of the accident scene based on the video data, allowing detailed reconstruction of the accident situation. The video analysis unit, for example, analyzes the video data and generates a 3D model of the accident scene. For example, it integrates images from multiple camera angles to create a three-dimensional model. The video analysis unit can also generate a detailed 3D model of the accident scene using point cloud data and mesh data. For example, it recreates the shapes of buildings and roads based on point cloud data. By generating a 3D model, detailed reconstruction of the accident situation can be visually reproduced.

[0034] The operation log analysis unit can analyze the driver's driving style from the operation log and consider it as a factor in the accident. For example, the operation log analysis unit uses generative AI to analyze the frequency of the driver's sudden braking and sudden steering from the operation log. For example, it analyzes the speed at which the brake pedal is pressed and the angle at which the steering wheel is turned. The operation log analysis unit can also analyze the driver's acceleration and deceleration patterns to understand their driving style. For example, it analyzes the timing of acceleration and the method of deceleration. By taking driving style into consideration, it is possible to understand the factors behind the accident in more detail.

[0035] The operation log analysis unit can analyze the driver's reaction time based on the operation log and evaluate the appropriateness of the response when an accident occurs. The operation log analysis unit, for example, analyzes the driver's reaction time based on the operation log. For example, it measures the time from when the brake pedal is first pressed to when the vehicle actually decelerates. The operation log analysis unit can also analyze the reaction time of steering wheel operation and evaluate the appropriateness of the response. For example, it analyzes the time it takes to turn the steering wheel and the timing of evasive action. In this way, by evaluating the reaction time, it is possible to understand the appropriateness of the response when an accident occurs.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The traffic accident reconstruction system can further include an audio analysis unit. The audio analysis unit can analyze audio data inside and outside the vehicle when an accident occurs and consider it as a factor in the accident. For example, the audio analysis unit can analyze horn sounds, collision sounds, the driver's voice, etc. to understand the accident situation in more detail. The audio analysis unit can also analyze the tone and speed of the driver's voice to estimate the psychological state at the time of the accident. In this way, by taking audio data into consideration, the factors behind the accident can be more accurately understood.

[0038] The traffic accident reproduction system may further include an environmental data acquisition unit. The environmental data acquisition unit may acquire environmental data (e.g., temperature, humidity, wind speed) around the time of the accident and consider it as a factor in the accident. For example, the environmental data acquisition unit may analyze temperature fluctuations and humidity changes to estimate road surface conditions and deterioration of visibility. The environmental data acquisition unit may also analyze wind speed and wind direction to understand factors that affect vehicle behavior. In this way, by taking environmental data into consideration, the factors in the accident may be more accurately understood.

[0039] The traffic accident reconstruction system can further include a history analysis unit that analyzes the driver's past driving history. The history analysis unit can analyze the driver's past driving history and consider it as a factor in the accident. For example, the history analysis unit can analyze the driver's past traffic violations and accident history to understand the driver's driving habits and risk factors. The history analysis unit can also analyze the driver's driving style based on past driving data and estimate the factors in the accident. In this way, by considering the past driving history, the factors in the accident can be understood in more detail.

[0040] The traffic accident reconstruction system can further include an eye-tracking unit that tracks the driver's gaze. The eye-tracking unit can analyze the driver's gaze movements and consider them as factors in the accident. For example, the eye-tracking unit can analyze the driver's gaze direction and gaze duration to identify distractions and obstructions to the field of view. The eye-tracking unit can also analyze the driver's gaze movements in combination with the operation log to evaluate the appropriateness of the driver's responses. By taking eye-tracking movements into consideration, the factors in the accident can be understood in more detail.

[0041] The traffic accident reproduction system can further include a skill evaluation unit that evaluates the driver's driving skill. The skill evaluation unit can analyze the driver's driving skill and consider it as a factor in the accident. For example, the skill evaluation unit can analyze the accuracy of the driver's braking and steering operations to evaluate the driving skill. The skill evaluation unit can also analyze the driver's acceleration and deceleration patterns to understand the driving skill. In this way, by taking driving skill into consideration, the factors in the accident can be understood in more detail.

[0042] The traffic accident reproduction system can further include an advice unit that provides driving advice based on the driver's driving style. The advice unit can analyze the driver's driving style and provide appropriate driving advice. For example, the advice unit can analyze the frequency of the driver's sudden braking and sudden steering and provide advice on safe driving methods. The advice unit can also analyze the driver's acceleration and deceleration patterns and suggest fuel-efficient driving methods. In this way, by providing driving advice based on the driver's driving style, safe driving by the driver can be promoted.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The video analysis unit analyzes the video data from the drive recorder. For example, the video analysis unit uses generative AI to analyze the vehicle's position, speed, and surrounding conditions from the video data. The video analysis unit can also automatically recognize weather and road conditions from the video data. Step 2: The operation log analysis unit analyzes the driver's operation log. For example, the operation log analysis unit uses generation AI to analyze accelerator and brake operations, steering wheel movements, and turn signal usage. The operation log analysis unit can also analyze the driver's driving style and reaction time from the operation log. Step 3: The accident reconstruction unit recreates the accident situation based on the data analyzed by the video analysis unit and the operation log analysis unit. For example, the accident reconstruction unit uses generative AI to recreate the vehicle's movements and surrounding conditions as CG. The accident reconstruction unit can also analyze the dynamic behavior of the vehicle at the time of the accident based on the video data. Step 4: The visualization unit visualizes the accident situation recreated by the accident reconstruction unit as computer graphics. For example, the visualization unit can use the accident situation recreated using generative AI as evidence to clarify the cause of the accident and who is responsible. The visualization unit can also use the recreated accident situation as a tool for insurance companies, police, and other related organizations to quickly and accurately understand the accident situation.

[0045] (Example 2) The traffic accident reenactment system according to an embodiment of the present invention is a system that automatically recreates the situation of a traffic accident using information from a drive recorder and a driver's operation log, and visualizes it as CG. This makes it easier to visually understand the cause and situation of the accident.

[0046] A traffic accident reconstruction system according to an embodiment includes a video analysis unit, an operation log analysis unit, an accident reconstruction unit, and a visualization unit. The video analysis unit analyzes video data from a drive recorder. For example, the video analysis unit uses a generation AI to analyze the vehicle's position, speed, and surrounding conditions from the video data. The video analysis unit can also automatically recognize weather and road conditions from the video data. The operation log analysis unit analyzes the driver's operation log. For example, the operation log analysis unit uses a generation AI to analyze accelerator and brake operation, steering wheel movement, and turn signal usage. The operation log analysis unit can also analyze the driver's driving style and reaction time from the operation log. The accident reconstruction unit recreates the accident situation based on the data analyzed by the video analysis unit and the operation log analysis unit. For example, the accident reconstruction unit recreates the vehicle's movement and surrounding conditions as CG using a generation AI. The accident reconstruction unit can also analyze the vehicle's dynamic behavior at the time of the accident based on the video data. The visualization unit visualizes the accident situation recreated by the accident reconstruction unit as CG. For example, the visualization unit uses the accident situation recreated using the generation AI as evidence to clarify the cause of the accident and who is responsible. The visualization unit can also be used as a tool for relevant organizations, such as insurance companies and police, to quickly and accurately understand the accident situation based on the recreated accident situation. This allows the traffic accident reconstruction system according to the embodiment to recreate the traffic accident situation in detail and make it visually easy to understand. For example, this can be used as evidence to clarify the cause of the accident, allowing relevant organizations, such as insurance companies and police, to quickly and accurately understand the accident situation. Furthermore, the driver can review the accident situation and use it to improve their future driving.

[0047] The video analysis unit can automatically recognize weather or road conditions from video data and consider them as factors in an accident. For example, the video analysis unit can use generative AI to automatically recognize weather (sunny, rainy, snowy, etc.) from video data from a dashcam and consider them as factors in an accident. For example, it can analyze visibility conditions and road surface reflections in the video. The video analysis unit can also automatically recognize road conditions (e.g., icy roads, traffic jams, construction work, etc.) and consider them as factors in an accident. This allows for a more accurate understanding of the causes of accidents by taking weather and road conditions into account.

[0048] The video analysis unit can analyze audio information included in the video data and recreate the acoustic environment at the time of the accident. For example, the video analysis unit analyzes audio information included in the video data and identifies the horn sound at the time of the accident. For example, it analyzes the audio waveform and extracts the frequency and volume of the horn. The video analysis unit can also analyze audio information such as the collision sound and the driver's voice and recreate the acoustic environment at the time of the accident. For example, it analyzes the intensity and direction of the collision sound. By recreating the acoustic environment in this way, the circumstances of the accident can be understood in more detail.

[0049] The video analysis unit can use the emotion estimation function to analyze the facial expressions of the driver or pedestrian in the video data and understand their emotional state at the time of the accident. For example, the video analysis unit can use generative AI to analyze the driver's facial expression in the video and estimate their emotional state (e.g., surprise, fear) at the time of the accident. For example, it can analyze the movement of facial muscles. The video analysis unit can also analyze the facial expressions of pedestrians to understand their emotional state at the time of the accident. For example, it can analyze the movement of the pedestrian's facial muscles. By understanding their emotional state, it is possible to understand the causes of the accident in more detail.

[0050] The video analysis unit can use video data to analyze the vehicle's maintenance status before and after the accident and consider it as a factor in the accident. For example, the video analysis unit analyzes video data to automatically recognize the vehicle's external condition (e.g., tire wear, damage to the body). For example, it can measure tire tread depth using image analysis technology. The video analysis unit can also analyze maintenance conditions such as the condition of the brakes and the condition of the engine and consider them as factors in the accident. For example, it can analyze the wear of brake pads and abnormal engine noises. By taking the vehicle's maintenance status into consideration, the factors in the accident can be more accurately identified.

[0051] The video analysis unit generates a 3D model of the accident scene based on the video data, allowing detailed reconstruction of the accident situation. The video analysis unit, for example, analyzes the video data and generates a 3D model of the accident scene. For example, it integrates images from multiple camera angles to create a three-dimensional model. The video analysis unit can also generate a detailed 3D model of the accident scene using point cloud data and mesh data. For example, it recreates the shapes of buildings and roads based on point cloud data. By generating a 3D model, detailed reconstruction of the accident situation can be visually reproduced.

[0052] The video analysis unit can use emotion estimation functions to analyze the driver's stress level from video data and consider it as a factor in an accident. For example, the video analysis unit can use generative AI to analyze the driver's facial expressions from video data and estimate the stress level. For example, it can analyze the level of tension on the face and the amount of sweat. The video analysis unit can also analyze the driver's heart rate and electrodermal activity to determine the stress level. For example, it can analyze fluctuations in heart rate and changes in electrodermal activity. By taking the driver's stress level into consideration, the factors behind an accident can be understood in more detail.

[0053] The operation log analysis unit can analyze the driver's driving style from the operation log and consider it as a factor in the accident. For example, the operation log analysis unit uses generative AI to analyze the frequency of the driver's sudden braking and sudden steering from the operation log. For example, it analyzes the speed at which the brake pedal is pressed and the angle at which the steering wheel is turned. The operation log analysis unit can also analyze the driver's acceleration and deceleration patterns to understand their driving style. For example, it analyzes the timing of acceleration and the method of deceleration. By taking driving style into consideration, it is possible to understand the factors behind the accident in more detail.

[0054] The operation log analysis unit can analyze the driver's reaction time based on the operation log and evaluate the appropriateness of the response when an accident occurs. The operation log analysis unit, for example, analyzes the driver's reaction time based on the operation log. For example, it measures the time from when the brake pedal is first pressed to when the vehicle actually decelerates. The operation log analysis unit can also analyze the reaction time of steering wheel operation and evaluate the appropriateness of the response. For example, it analyzes the time it takes to turn the steering wheel and the timing of evasive action. In this way, by evaluating the reaction time, it is possible to understand the appropriateness of the response when an accident occurs.

[0055] The operation log analysis unit uses an emotion estimation function to analyze the driver's emotional state from the operation log, making it possible to understand their psychological state at the time of an accident. The operation log analysis unit, for example, uses a generative AI to analyze the driver's emotional state from the operation log. For example, it can estimate the driver's stress or impatience from the frequency of sudden braking or steering. The operation log analysis unit can also analyze the driver's operational errors and delayed reactions to understand their emotional state. For example, it can analyze the frequency of operational errors and delayed reactions. By understanding the driver's emotional state, it is possible to understand the causes of an accident in more detail.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The traffic accident reconstruction system can further include an audio analysis unit. The audio analysis unit can analyze audio data inside and outside the vehicle when an accident occurs and consider it as a factor in the accident. For example, the audio analysis unit can analyze horn sounds, collision sounds, the driver's voice, etc. to understand the accident situation in more detail. The audio analysis unit can also analyze the tone and speed of the driver's voice to estimate the psychological state at the time of the accident. In this way, by taking audio data into consideration, the factors behind the accident can be more accurately understood.

[0058] The traffic accident reconstruction system may further include a biometric information analysis unit. The biometric information analysis unit analyzes the driver's biometric information (e.g., heart rate, blood pressure, and electrodermal activity) to understand the physiological state at the time of the accident. For example, the biometric information analysis unit may analyze heart rate fluctuations and blood pressure increases to estimate the driver's state of tension and stress level. The biometric information analysis unit may also analyze electrodermal activity to understand the driver's emotional state. This allows for a more detailed understanding of the causes of the accident by taking biometric information into consideration.

[0059] The traffic accident reproduction system may further include an environmental data acquisition unit. The environmental data acquisition unit may acquire environmental data (e.g., temperature, humidity, wind speed) around the time of the accident and consider it as a factor in the accident. For example, the environmental data acquisition unit may analyze temperature fluctuations and humidity changes to estimate road surface conditions and deterioration of visibility. The environmental data acquisition unit may also analyze wind speed and wind direction to understand factors that affect vehicle behavior. In this way, by taking environmental data into consideration, the factors in the accident may be more accurately understood.

[0060] The traffic accident reconstruction system can further include a history analysis unit that analyzes the driver's past driving history. The history analysis unit can analyze the driver's past driving history and consider it as a factor in the accident. For example, the history analysis unit can analyze the driver's past traffic violations and accident history to understand the driver's driving habits and risk factors. The history analysis unit can also analyze the driver's driving style based on past driving data and estimate the factors in the accident. In this way, by considering the past driving history, the factors in the accident can be understood in more detail.

[0061] The traffic accident reconstruction system can further include an emotion monitoring unit that monitors the driver's emotional state in real time. The emotion monitoring unit can analyze the driver's emotional state while driving in real time and consider it as a factor in the accident. For example, the emotion monitoring unit can analyze the driver's facial expressions, voice, and operation log to grasp the driver's state of stress and impatience in real time. The emotion monitoring unit can also issue a warning depending on the driver's emotional state. In this way, by monitoring the driver's emotional state in real time, the factors that led to the accident can be understood in more detail.

[0062] The traffic accident reconstruction system can further include an eye-tracking unit that tracks the driver's gaze. The eye-tracking unit can analyze the driver's gaze movements and consider them as factors in the accident. For example, the eye-tracking unit can analyze the driver's gaze direction and gaze duration to identify distractions and obstructions to the field of view. The eye-tracking unit can also analyze the driver's gaze movements in combination with the operation log to evaluate the appropriateness of the driver's responses. By taking eye-tracking movements into consideration, the factors in the accident can be understood in more detail.

[0063] The traffic accident reproduction system can further include a driving assistance unit that provides driving assistance based on the emotional state of the driver. The driving assistance unit can analyze the emotional state of the driver and provide appropriate driving assistance. For example, the driving assistance unit can play relaxing music when the driver's stress level is high. The driving assistance unit can also automatically adjust the driving speed when it detects that the driver is impatient. In this way, the risk of an accident can be reduced by providing driving assistance based on the driver's emotional state.

[0064] The traffic accident reproduction system can further include a skill evaluation unit that evaluates the driver's driving skill. The skill evaluation unit can analyze the driver's driving skill and consider it as a factor in the accident. For example, the skill evaluation unit can analyze the accuracy of the driver's braking and steering operations to evaluate the driving skill. The skill evaluation unit can also analyze the driver's acceleration and deceleration patterns to understand the driving skill. In this way, by taking driving skill into consideration, the factors in the accident can be understood in more detail.

[0065] The traffic accident reproduction system can further include an education unit that provides driving education based on the driver's emotional state. The education unit can analyze the driver's emotional state and provide appropriate driving education. For example, if the driver's stress level is high, the education unit can teach the driver how to manage stress. Furthermore, if the education unit detects that the driver is impatient, it can provide educational content to encourage calm judgment. In this way, by providing driving education based on the driver's emotional state, it is possible to improve the driver's skills.

[0066] The traffic accident reproduction system can further include an advice unit that provides driving advice based on the driver's driving style. The advice unit can analyze the driver's driving style and provide appropriate driving advice. For example, the advice unit can analyze the frequency of the driver's sudden braking and sudden steering and provide advice on safe driving methods. The advice unit can also analyze the driver's acceleration and deceleration patterns and suggest fuel-efficient driving methods. In this way, by providing driving advice based on the driver's driving style, safe driving by the driver can be promoted.

[0067] The processing flow of the second embodiment will be briefly explained below.

[0068] Step 1: The video analysis unit analyzes the video data from the drive recorder. For example, the video analysis unit uses generative AI to analyze the vehicle's position, speed, and surrounding conditions from the video data. The video analysis unit can also automatically recognize weather and road conditions from the video data. Step 2: The operation log analysis unit analyzes the driver's operation log. For example, the operation log analysis unit uses generation AI to analyze accelerator and brake operations, steering wheel movements, and turn signal usage. The operation log analysis unit can also analyze the driver's driving style and reaction time from the operation log. Step 3: The accident reconstruction unit recreates the accident situation based on the data analyzed by the video analysis unit and the operation log analysis unit. For example, the accident reconstruction unit uses generative AI to recreate the vehicle's movements and surrounding conditions as CG. The accident reconstruction unit can also analyze the dynamic behavior of the vehicle at the time of the accident based on the video data. Step 4: The visualization unit visualizes the accident situation recreated by the accident reconstruction unit as computer graphics. For example, the visualization unit can use the accident situation recreated using generative AI as evidence to clarify the cause of the accident and who is responsible. The visualization unit can also use the recreated accident situation as a tool for insurance companies, police, and other related organizations to quickly and accurately understand the accident situation.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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).

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0103] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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."

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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, in order to avoid confusion and to 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.

[0135] 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]

[0136] 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 video analysis unit that analyzes video data from the drive recorder; an operation log analysis unit that analyzes the driver's operation log; an accident reconstruction unit that reconstructs the circumstances of the accident based on the data analyzed by the video analysis unit and the operation log analysis unit; a visualization unit that visualizes the accident situation recreated by the accident reconstruction unit as computer graphics (CG). A system characterized by:

2. The video analysis unit Weather or road conditions are automatically recognized from the video data and considered as factors in the accident.

2. The system of claim 1.

3. The video analysis unit Using the video data, the maintenance status of the vehicle before and after the accident is analyzed and considered as a factor in the accident.

2. The system of claim 1.

4. The operation log analysis unit Analyzing the driver's driving style from the operation log and considering it as a factor in the accident 2. The system of claim 1.

5. The accident reconstruction department Reconstructing the circumstances of the accident based on the video data and the operation log 2. The system of claim 1.

6. The video analysis unit Analyzing the facial expressions of the driver or pedestrian in the video data to understand their emotional state at the time of the accident 2. The system of claim 1.

Images