System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving inputted accident information; means for standardizing and preprocessing the received accident information; means for reading and analyzing relevant legal case information from a past legal case database by using the standardized and preprocessed accident information; means for calculating a fault ratio based on the legal case information; means for calculating a damage claim amount according to a standard; and means for presenting the calculated fault ratio and damage claim amount to a user.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] Determining compensation for damages in traffic accidents is extremely complex, and a wealth of specialized knowledge is required to accurately calculate the degree of fault and the amount of compensation. This increases the cost and time required for settlement negotiations and the hiring of lawyers. As the number of car owners increases, there is a need for quick and accurate compensation determinations, but there is a lack of means to meet this need. In addition, there is a need to improve the operational efficiency of insurance companies, and there is a growing need for automated, simplified determination tools. [Means for solving the problem]

[0005] The system includes a means for receiving input accident information, a means for standardizing and preprocessing the received accident information, a means for reading and analyzing relevant case information from a database of past legal precedents using the standardized and preprocessed accident information, a means for calculating the degree of fault based on the legal precedent information, a means for calculating the amount of compensation in accordance with a standard, and a means for presenting the calculated degree of fault and compensation amount to the user. This invention enables the degree of fault and compensation amount in traffic accidents to be calculated quickly and accurately, significantly reducing the effort required for settlement negotiations and the hiring of lawyers. Furthermore, by structuring the legal precedent data using natural language processing technology, highly accurate judgment results can be obtained.

[0006] "Accident information" is detailed data about traffic accidents, including the date and time, location, information about the people involved, the circumstances of the accident, and details of the damage.

[0007] "Means for receiving" refers to a device or software that has the function of taking information input by a user into the system and storing that information in a processable format.

[0008] "Standardization and preprocessing measures" are operations or processes for standardizing the format and content of received data and correcting errors and omissions.

[0009] "Case law information" refers to court results and judgment data from past traffic accidents, and is used to calculate the degree of fault and the amount of compensation.

[0010] The "means for calculating the degree of fault" refers to a device or software that has the function of calculating the degree of fault of each party involved in an accident based on input accident information and case law information.

[0011] A "means for calculating damages" is a device or software that has the function of calculating the amount of damages to be paid by each party based on the specific details of the damage.

[0012] The "means for presenting to the user" refers to a device or software that has the function of visually displaying the calculated fault ratio and compensation amount to the user.

[0013] "Natural language processing technology" is a series of technologies for analyzing text data and understanding, generating, and manipulating human language on a computer.

[0014] A "database" is a system that stores information in an organized manner and manages it in a format that allows for efficient search and retrieval. [Brief explanation of the drawings]

[0015] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The system of the present invention is designed to quickly and accurately automate compensation for damages in automobile accidents, and includes a process for inputting details of a traffic accident and calculating the percentage of fault and the amount of compensation. Specific embodiments and the flow of program processing are described below.

[0037] System configuration

[0038] The system mainly consists of the following components:

[0039] 1. User input terminal: A device with an interface for entering details of a traffic accident. This can be a computer, smartphone, tablet, etc.

[0040] 2. Server: This is the core device that receives data sent by users and performs various processes, such as standardizing data, reading legal precedent information, and calculating the degree of fault and the amount of compensation.

[0041] 3. Database: A system that stores past case data and compensation standards.

[0042] System Operation

[0043] Below we explain the process from entering accident information to presenting the final amount of compensation.

[0044] User Input Phase

[0045] User: Enters detailed information about the traffic accident into the input terminal, such as the date and time of the accident, location, parties involved (victim, perpetrator), accident circumstances (rear-end collision, side collision, etc.), and specific details of the damage (vehicle damage, need for medical treatment, etc.).

[0046] Terminal: Checks the input and sends the data to the server based on the format.

[0047] Data reception and preprocessing phase

[0048] Server: Receives the accident information sent by the user.

[0049] Server: Standardizes incoming data and ensures consistency in format and content, for example, standardizing date and time formats and address notation.

[0050] Server: Data preprocessing includes imputing missing values ​​and detecting and correcting input errors.

[0051] Case law information loading phase

[0052] Server: Loads relevant information from case law databases, including damage standards such as the "Red Book" and "Blue Book."

[0053] Server: Uses natural language processing techniques to structure the text data and convert it into a format that can be used by the AI ​​model.

[0054] Fault calculation phase

[0055] Server: Inputs standardized and preprocessed accident information and structured case law information into the AI ​​model.

[0056] Server: The AI ​​model calculates the percentage of fault based on past legal precedent data. For example, by looking at past legal precedents for rear-end collisions, the model may determine that the assailant is 80% at fault and the victim is 20% at fault.

[0057] Damages calculation phase

[0058] Server: Calculates the amount of compensation based on the determined percentage of fault and the damage data entered. Damages such as medical expenses, repair costs, and compensation are calculated according to standards.

[0059] Server: For example, if medical expenses are 500,000 yen, repair costs are 300,000 yen, and compensation is 200,000 yen, the total damages are 1,000,000 yen. The amount of compensation that the perpetrator must pay is 80% of 1,000,000 yen, or 800,000 yen.

[0060] Results presentation phase

[0061] Server: Organizes the calculated percentage of fault and amount of damages and converts them into a format that can be notified to the user.

[0062] Terminal: Displays the results received from the server to the user. For example, the final payment amount of 800,000 yen and its breakdown (medical expenses 400,000 yen, repair expenses 240,000 yen, and compensation 160,000 yen) is displayed to the user.

[0063] Specific examples

[0064] For example, consider a rear-end collision that occurred in Shibuya Ward, Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator), resulting in damage to the rear of Person A's vehicle and injuries requiring medical treatment. User Person A enters the accident information, and the device sends that information to the server. The server processes the received data, loads appropriate case law information, and runs it through an AI model. Person B is determined to be 80% at fault, and the amount of compensation to Person A is calculated to be 800,000 yen. This result is presented to the user.

[0065] This system allows users to automatically calculate the amount of damages quickly and accurately, significantly reducing the effort required for settlement negotiations.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] User: Enter detailed information about the traffic accident (e.g., date and time of the accident, location, people involved, accident situation, damage details, etc.).

[0069] Terminal: Converts the input information into the appropriate format and sends it to the server.

[0070] Step 2:

[0071] Server: Receives the accident information sent by the user.

[0072] Server: Checks whether the format and content of the received data are correct.

[0073] Step 3:

[0074] Server: Standardizes the received accident information. For example, standardize the date and time to the "YYYY-MM-DD HH:MM" format and standardize address notation.

[0075] Server: Preprocesses the data, completes missing data, and corrects input errors.

[0076] Step 4:

[0077] Server: Reads case information from the "Red Book" and "Blue Book."

[0078] Server: The read text data is structured using natural language processing technology and converted into a format that can be used by the AI ​​model.

[0079] Step 5:

[0080] Server: Structured case law information and standardized accident information are input into the AI ​​model.

[0081] Server: The AI ​​model refers to past case data and calculates the fault ratio based on the input accident information. For example, in the case of a rear-end collision, the fault ratio is usually 80:20.

[0082] Step 6:

[0083] Server: Calculate the amount of compensation based on the percentage of fault. For example, if the victim's medical expenses are 500,000 yen, repair costs are 300,000 yen, and compensation is 200,000 yen, the total damages will be 1 million yen.

[0084] Server: Calculate the amount of compensation that the at-fault party must pay based on their percentage of fault. In the example above, the amount that must be paid is 800,000 yen, which is 80% of the total damages of 1 million yen, and is the at-fault party's percentage of the total damages.

[0085] Step 7:

[0086] Server: Organizes the calculated percentage of fault and compensation amount and summarizes them in a format that is easy for users to understand.

[0087] Terminal: Displays the results received from the server to the user. For example, the final payment amount of 800,000 yen and its breakdown (medical expenses 400,000 yen, repair expenses 240,000 yen, and compensation 160,000 yen) is displayed.

[0088] Step 8:

[0089] User: Check the displayed results and, if necessary, correct the accident information again or proceed with settlement negotiations.

[0090] Example 1

[0091] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0092] When a traffic accident occurs, it is important to calculate compensation for damages quickly and accurately. However, conventional methods require manual input, calculations, and analysis of legal precedents, which is time-consuming and prone to errors. To solve these problems, a system is needed that automates the process from inputting accident information to calculating compensation amounts, thereby improving accuracy.

[0093] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0094] In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a database of past case law using the standardized and preprocessed accident information, means for calculating the degree of fault based on the case law information, means for calculating the amount of compensation in accordance with a standard, means for presenting the calculated degree of fault and amount of compensation to the user, means for checking the input accident information and transmitting the data in an appropriate format, and means for using natural language processing technology to structure text data and convert it into a format for input to an AI model. This automates the process from inputting accident information to calculating the amount of compensation, making it possible to perform the process quickly and accurately.

[0095] "Accident information" refers to detailed data including the date and time of the traffic accident, the location of the accident, information about those involved (such as the names and contact information of the victim and perpetrator), the circumstances of the accident (such as a rear-end collision or a side collision), and the damages (such as damage to the vehicle and the need for medical treatment).

[0096] The "receiving means" refers to a hardware or software element that has the function of accurately acquiring input from a user or data from an external system.

[0097] "Standardization and preprocessing means" refers to processes that standardize the format of received data, fill in inconsistencies and missing values, and prepare the data for analysis.

[0098] "Case law information" refers to data on the results of past trials and settlements regarding similar traffic accidents, as well as the percentage of fault and amount of compensation calculated at those times.

[0099] "Natural language processing technology" is a technology that allows computers to understand and analyze the language that humans use on a daily basis, and is a technology that structures text data and performs semantic analysis.

[0100] "Fault ratio" is a number that indicates the percentage of responsibility that each party should bear for damages caused in a traffic accident.

[0101] "Compensation" refers to the amount that the at-fault party must pay for the damages suffered by the victim in a traffic accident, including medical expenses, repair costs, and compensation.

[0102] The "presentation means" refers to hardware or software elements that have the function of displaying or notifying the calculated results to users and other related parties in an easy-to-understand manner.

[0103] An "AI model" is a collection of algorithms that use machine learning and deep learning to analyze data and automatically perform tasks such as prediction and classification.

[0104] "Structuring technology" is a technology that analyzes unstructured data and converts it into a format that can be used in databases and analysis systems.

[0105] The present invention provides a system for automatically calculating compensation for damages after a traffic accident, quickly and accurately. Specific embodiments of this system are described below.

[0106] System configuration

[0107] This system consists of a user input terminal, a server, and a database.

[0108] User Input Device: The device used to enter details of a traffic accident, such as a computer, smartphone, or tablet.

[0109] Server: This is the core device that analyzes the received data and calculates the percentage of fault and the amount of compensation. It also performs data standardization, preprocessing, reading case law information, and various calculations.

[0110] Database: A system that stores past case data and damages standards. The case information database includes damages standards such as the "Red Book" and the "Blue Book."

[0111] System Operation

[0112] The system processes traffic accident data using the following hardware and software:

[0113] 1. When a traffic accident occurs, the user uses a smartphone or computer to input accident information, including the date and time of the accident, the location of the accident, information about the people involved, the accident situation, and details of the damage.

[0114] 2. The terminal checks the entered accident information, converts it into the appropriate format, and sends it to the server. For example, it standardizes the date and time format and address notation, and checks for and corrects any inconsistencies or missing entries.

[0115] 3. The server standardizes and preprocesses the received accident information. Standardization includes standardizing date and time formats and address notation, and preprocessing includes filling in missing values ​​and detecting and correcting input errors.

[0116] 4. The server reads the relevant case information from the case information database, analyzes and structures the text data using natural language processing technology, and then segments and tags the text data before storing it in the database.

[0117] 5. The server uses the standardized and preprocessed accident information and structured case law information to calculate the percentage of fault using an AI model. It references past case law data and calculates based on the percentage of fault in similar accidents.

[0118] 6. The server calculates the amount of compensation based on the percentage of fault and the damage data entered by the user. The damages include medical expenses, repair costs, compensation, etc. Based on these, the total amount of damages is calculated and the amount of compensation according to the percentage of fault is determined.

[0119] 7. The server organizes the calculated percentage of fault and compensation amount and converts it into a format that can be notified to the user. The results include a breakdown of the compensation amount (medical expenses, repair costs, compensation, etc.).

[0120] 8. The terminal displays the results received from the server to the user. For example, it may present the results in the form of "Final payment amount: 800,000 yen (medical expenses 400,000 yen, repair expenses 240,000 yen, compensation 160,000 yen)."

[0121] Specific examples

[0122] For example, consider a case involving a rear-end collision that occurred somewhere in Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator). When Person A's vehicle rear end was damaged and he suffered injuries requiring medical treatment, User A inputs the accident information. The input information is sent to the server, which processes the information. It reads relevant case law information and uses an AI model to determine the fault ratio as 80:20, calculating the amount of damages as 800,000 yen. The results are then presented to the user.

[0123] Prompt Sentence Examples

[0124] "Information on victim A and assailant B has been entered for a rear-end collision that occurred in a certain location in Tokyo on October 1, 2023. The damage involved damage to the rear of the vehicle and the need for medical treatment. Please calculate the percentage of fault and the amount of compensation based on past legal precedents."

[0125] This system makes it possible to quickly and accurately calculate compensation for damages caused by traffic accidents, significantly reducing the burden on users.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] System processing flow

[0128] Step 1: Enter accident information

[0129] The user inputs details of the traffic accident using a smartphone or computer, including the date and time of the accident, the location of the accident, information about the parties involved (such as the names and contact information of the victim and at-fault parties), the circumstances of the accident (such as rear-end collision or side-impact collision), and details of damage (such as vehicle damage and medical needs).The system records the accident circumstances based on the input data and proceeds to the next step.

[0130] Step 2: Send data

[0131] The terminal checks the information entered by the user and converts it into the appropriate format. Specifically, it standardizes the date and time format and address notation. The converted data is then sent to the server. By converting the entered data into a format and sending it to the server, data consistency is maintained.

[0132] Step 3: Receiving and Preprocessing Data

[0133] The server receives the accident information sent from the terminal. After receiving it, it standardizes and preprocesses the data. Specifically, it standardizes the date and time format and address notation, fills in missing values, and detects and corrects input errors. This prepares the data in an analyzable format and allows it to proceed to the next step.

[0134] Step 4: Loading case law information

[0135] The server reads relevant case information from a case information database. For example, it extracts cases related to compensation for damages from databases such as the "Red Book" and "Blue Book." It then uses natural language processing technology to analyze and structure the text data, converting it into a format that can be input into the AI ​​model. This converts the case data into a format that can be used for analysis.

[0136] Step 5: Calculating the percentage of fault

[0137] The server uses the standardized and preprocessed accident information and structured case law information to calculate the percentage of fault using an AI model. Specifically, it calculates the percentage of fault corresponding to the input accident information based on past case law data. For example, it refers to past cases related to rear-end collisions to determine the percentage of fault between the assailant and the victim. It then proceeds to the next step based on the calculated percentage of fault.

[0138] Step 6: Calculating damages

[0139] The server calculates the amount of compensation based on the determined fault ratio and the damage data entered by the user. Specifically, it calculates the total amount of damages using data such as medical expenses, repair costs, and compensation, and then calculates the amount of compensation based on the fault ratio. For example, if the total amount of damages is 1 million yen and the fault ratio is 80:20, the amount of compensation that the perpetrator must pay is 800,000 yen. The process proceeds to the next step based on the calculated amount of compensation.

[0140] Step 7: Organize and communicate results

[0141] The server organizes the calculated percentage of fault and compensation amount and converts them into a format that can be notified to the user. Specifically, it formats the results, including a breakdown of the compensation amount (e.g., medical expenses, repair costs, compensation, etc.). The formatted data is sent to proceed to the next step.

[0142] Step 8: Viewing the results

[0143] The terminal displays the results received from the server to the user. Specifically, it presents the results in the form of "Final payment amount: 800,000 yen (medical expenses 400,000 yen, repair expenses 240,000 yen, compensation 160,000 yen)." This allows the user to quickly and accurately check the amount of compensation.

[0144] (Application example 1)

[0145] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0146] Conventional methods for calculating compensation for traffic accidents require human intervention, which requires a significant amount of time and effort. Furthermore, there is a problem in that accident analysis and compensation calculation for autonomous vehicles cannot be performed in real time. This can lead to delays in settlement negotiations and in some cases to an inability to present accurate compensation amounts. Furthermore, the time required for rapid accident analysis and notification of results increases the mental and financial burden on both the victim and the at-fault party.

[0147] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0148] In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a past case database using the standardized and preprocessed accident information, means for calculating a fault ratio based on the case information, means for calculating a compensation amount according to a standard, means for presenting the calculated fault ratio and compensation amount to a user, means for recording sensor information from the autonomous vehicle and collecting accident data, and means for transmitting the accident data to the server via the vehicle's communication module. This makes it possible, when an autonomous vehicle is involved in a traffic accident, to quickly record the accident situation and quickly and accurately calculate a fault ratio and compensation amount.

[0149] "Accident information" is detailed data about traffic accidents, including the date and time of the accident, location, people involved, accident circumstances, and damage details.

[0150] The "receiving means" refers to a method and device for capturing the input accident information into the server.

[0151] "Standardization and preprocessing means" refers to methods and devices for standardizing received incident information into a consistent format and correcting missing or incorrect data.

[0152] The "Case Law Database" is a data store that compiles case law and compensation standards related to past traffic accidents.

[0153] The "means for reading and analyzing" refers to a method and apparatus for extracting and analyzing relevant case information from a case database based on the standardized and preprocessed accident information.

[0154] The "means for calculating the degree of fault" refers to a method and device for calculating the degree of fault of each party based on legal precedent information.

[0155] The "means for calculating the amount of compensation" refers to a method and device for calculating the amount of compensation for each party based on the determined degree of fault and damage data.

[0156] The "means for presenting to the user" refers to a method and device for displaying the calculated fault ratio and compensation amount to the user.

[0157] "Means for recording sensor information" refers to methods and apparatus for storing accident-related data obtained from sensors installed on an autonomous vehicle.

[0158] A "communication module" is a communication device used to transfer data from a vehicle to a server.

[0159] This invention is a system that quickly and accurately collects accident information when an autonomous vehicle is involved in a traffic accident, and automatically calculates the percentage of fault and the amount of compensation. Specific embodiments of this system are described below.

[0160] System Overview

[0161] This system consists of a user input terminal, a server, a database, various sensors in the autonomous vehicle, and a communication module. These components work together as follows:

[0162] 1. User Input Terminal

[0163] This is a device that allows users to input detailed information about traffic accidents. The device can be a smartphone, tablet, or vehicle display system.

[0164] 2. Server

[0165] The server is the main device responsible for receiving accident information, standardizing and preprocessing it, reading case law information, calculating the degree of fault and compensation amounts, and presenting the results. In particular, it uses natural language processing technologies (such as NLTK and spaCy) and AI models (TENSORFLOW (registered trademark) and PyTorch) to standardize and preprocess the received accident information and extract relevant information from the case law database.

[0166] 3. Database

[0167] The database holds information on past legal precedents and damage compensation standards, from which the server extracts the necessary information and uses it for analytical processing.

[0168] 4. Sensors and communication modules for autonomous vehicles

[0169] Autonomous vehicles are equipped with front and rear cameras and various sensors that record information in real time when an accident occurs. This data is sent to a server via the vehicle's communication module, using 5G communication technology.

[0170] System operation flow

[0171] The operation of the system consists of the following major steps:

[0172] 1. Collecting accident information

[0173] Sensors in autonomous vehicles record accident information in real time, acquiring information such as the location and time of the accident, and information on those involved. This information is then sent to a server via the vehicle's communication module.

[0174] 2. Receiving and preprocessing accident information

[0175] The server standardizes the received accident information, filling in missing values ​​and checking for format consistency, for example, standardizing date and time formats and address notation.

[0176] 3. Extraction and structuring of case law information

[0177] The server reads the relevant case information from the case database and structures it using natural language processing technology, converting it into a format that can be used by the AI ​​model.

[0178] 4. Calculation of the percentage of fault and the amount of compensation

[0179] Standardized and preprocessed accident information and structured case law information are input into the AI ​​model to calculate the degree of fault and the amount of compensation.

[0180] 5. Presentation of results

[0181] The calculated percentage of fault and amount of compensation for damages are sent from the server to the user input terminal in a format that can be confirmed by the user, and are displayed.

[0182] Specific examples

[0183] For example, in the case of a rear-end collision that occurred in Tokyo on October 1, 2023, the victim was Person A and the assailant was Person B, and Person A's rear end was damaged, resulting in injuries requiring medical treatment. Accident information acquired by the autonomous vehicle is sent to a server in real time, where it is standardized and preprocessed. The server refers to a database of past legal precedents, extracts appropriate data, and inputs it into an AI model to calculate the percentage of fault between the victim and the assailant, and calculates the amount of compensation. The results are displayed on the autonomous vehicle's display system and on the user's smartphone.

[0184] Prompt Sentence Examples

[0185] "Please enter the details of a rear-end collision that occurred in Tokyo on October 1, 2023. The victim is Person A and the perpetrator is Person B. The rear of Person A's vehicle is damaged and requires medical treatment."

[0186] This system allows for quick and accurate calculation of compensation in traffic accidents, significantly reducing the effort required for settlement negotiations.

[0187] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0188] Step 1:

[0189] Sensors in autonomous vehicles record accident information in real time.

[0190] Input: Real-time data from sensors and cameras (location of the accident, speed, impact angle, etc.).

[0191] Output: Recorded accident data.

[0192] Specific operation: Various sensors and cameras installed in the autonomous vehicle detect the moment of an accident and store the data in internal memory. These sensors include acceleration sensors, gyro sensors, and video cameras.

[0193] Step 2:

[0194] The accident data is transmitted to a server via the vehicle's communication module.

[0195] Input: Accident data recorded in step 1.

[0196] Output: The incident data sent to the server.

[0197] How it works: Recorded accident data is sent to a server in real time via a 5G communication module installed in the vehicle, which requires high data transfer speeds and low latency.

[0198] Step 3:

[0199] The server normalizes and pre-processes the received accident data.

[0200] Input: Accident data sent to the server.

[0201] Output: Standardized and preprocessed accident data.

[0202] Specific operation: The server standardizes the date and time format and address notation of the received accident data, and performs missing data completion and correction of input errors to maintain consistency. This process is often performed using the Python library Pandas.

[0203] Step 4:

[0204] The server reads the relevant case information from the case database and structures it using natural language processing technology.

[0205] Input: Standardized and preprocessed accident data.

[0206] Output: Structured case law information.

[0207] Specific operation: The server searches and extracts relevant information from case law databases such as the "Red Book" and "Blue Book," and structures the text data using natural language processing technology (such as NLTK or spaCy).

[0208] Step 5:

[0209] The server inputs standardized accident data and structured case law information into an AI model to calculate the degree of fault.

[0210] Input: Standardized accident data and structured case law information.

[0211] Output: Calculated fault percentage.

[0212] Specific operation: Standardized accident data and structured case law information are input into an AI model (using TensorFlow and PyTorch), and the fault ratio of each party is calculated based on past case law data.

[0213] Step 6:

[0214] The server calculates the amount of compensation based on the determined fault percentage and damage data.

[0215] Input: Calculated fault percentage and damage data.

[0216] Output: Calculated damages.

[0217] Specific operation: The server calculates the total amount of damages based on the percentage of fault and the input damage data (medical expenses, repair costs, compensation, etc.), and then calculates the amount of compensation that the at-fault party must pay. Specifically, it multiplies and adds the amounts.

[0218] Step 7:

[0219] The server presents the calculated percentage of fault and the amount of compensation to the user.

[0220] Input: Calculated percentage of fault and damages.

[0221] Output: Percent fault and damages in a user-viewable format.

[0222] Specific operation: The server converts the calculation results into an easy-to-understand format and sends them to the user's input device (smartphone or vehicle display system), allowing both the victim and the perpetrator to check the results in real time.

[0223] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0224] The system of the present invention quickly and accurately determines compensation for damages in automobile accidents and further improves the user experience by recognizing the user's emotions. Specific embodiments of the present invention and the flow of program processing are described below.

[0225] System configuration

[0226] The system mainly consists of the following components:

[0227] 1. User input terminal: A device with an interface for entering details of a traffic accident. This can be a computer, smartphone, tablet, etc.

[0228] 2. Server: This is the core device that receives data sent by users and performs various processes, such as standardizing data, reading legal precedent information, and calculating the degree of fault and the amount of compensation.

[0229] 3. Database: A system that stores past case data and compensation standards.

[0230] 4. Emotion engine: A device or software that recognizes emotions based on user input and reactions and adjusts the system's response.

[0231] System Operation

[0232] Below we explain the process from inputting accident information to presenting the final compensation amount and optimizing it through emotion recognition.

[0233] User Input Phase

[0234] User: Enters detailed information about the traffic accident into the input terminal, such as the date and time of the accident, location, parties involved (victim, perpetrator), accident circumstances (rear-end collision, side collision, etc.), and specific details of the damage (vehicle damage, need for medical treatment, etc.).

[0235] Terminal: Checks the input and sends the data to the server based on the format.

[0236] Data reception and preprocessing phase

[0237] Server: Receives the accident information sent by the user.

[0238] Server: Checks whether the format and content of the received data are correct.

[0239] Server: Standardizes the received accident information and checks for consistency in format and content, for example, standardizing date and time formats and address notation.

[0240] Case law information loading phase

[0241] Server: Reads case information from the "Red Book" and "Blue Book."

[0242] Server: The read text data is structured using natural language processing technology and converted into a format that can be used by the AI ​​model.

[0243] Fault calculation phase

[0244] Server: Structured case law information and standardized accident information are input into the AI ​​model.

[0245] Server: The AI ​​model refers to past case data and calculates the fault ratio based on the input accident information. For example, in the case of a rear-end collision, the fault ratio is usually 80:20.

[0246] Damages calculation phase

[0247] Server: Calculate the amount of compensation based on the percentage of fault. For example, if the victim's medical expenses are 500,000 yen, repair costs are 300,000 yen, and compensation is 200,000 yen, the total damages will be 1 million yen.

[0248] Server: Calculate the amount of compensation that the at-fault party must pay based on their percentage of fault. In the example above, the amount that must be paid is 800,000 yen, which is 80% of the total damages of 1 million yen, and is the at-fault party's percentage of the total damages.

[0249] Emotion Recognition Phase

[0250] Terminal: The emotion engine analyzes the user's reactions when inputting information, facial expressions, tone of voice, etc.

[0251] Emotion engine: The emotion engine recognizes emotions based on the user's input information and sends the emotional state to the server.

[0252] Server: Adjusts the content, expression, and presentation of responses based on the user's recognized emotions. For example, if the system recognizes that the user is feeling stressed or anxious, it will provide a more polite and reassuring response.

[0253] Results presentation phase

[0254] Server: Organizes the calculated percentage of fault and compensation amount and summarizes them in a format that is easy for users to understand.

[0255] Terminal: Displays the results received from the server to the user. For example, it displays the final payment amount of 800,000 yen and its breakdown (400,000 yen for medical expenses, 240,000 yen for repairs, and 160,000 yen for compensation). In addition, it can display a leaflet or a link to an FAQ page that reflects the user's emotions.

[0256] Specific examples

[0257] For example, consider a rear-end collision that occurred in Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator), resulting in damage to the rear of Person A's vehicle and injuries requiring medical treatment. User A enters accident information, and the device sends that information to the server. The server processes the received data, loads appropriate case law information, and runs it through an AI model. Person B is determined to be 80% at fault, and the amount of compensation to Person A is calculated to be 800,000 yen. This result is presented to the user by the emotion engine, taking into account the user's emotions.

[0258] In this way, users can quickly and accurately calculate the amount of compensation automatically, and the system takes the user's feelings into consideration when presenting optimal information, significantly reducing the effort and stress involved in settlement negotiations.

[0259] The processing flow will be explained below.

[0260] Step 1:

[0261] User: Enter detailed information about the traffic accident, such as the date and time of the accident (e.g., 14:30, October 1, 2023), the location of the accident (an intersection in Tokyo), the parties involved (victim A, assailant B), the accident circumstances (rear-end collision while waiting at a traffic light), and the specific details of the damage (damage to the rear of the vehicle, injuries requiring medical treatment).

[0262] Step 2:

[0263] Terminal: Converts the input information into the appropriate format and sends it to the server, including all the details of the accident.

[0264] Step 3:

[0265] Server: Receives the accident information sent by the user. Immediately after receiving the information, the server checks whether there are any errors in the data format or content. For example, it checks whether the date and time format is standardized to "YYYY-MM-DD HH:MM" and whether the address is written correctly.

[0266] Step 4:

[0267] Server: Standardizes the received accident information. Standardization includes standardizing date and time formats, address notation, and name notation. This ensures data consistency.

[0268] Step 5:

[0269] Server: Reads case law information from the "Red Book" and "Blue Book" databases. Structures the text data using natural language processing technology to analyze past case law data and compensation standards.

[0270] Step 6:

[0271] Server: Structured case law information and standardized accident information are input into the AI ​​model. The AI ​​model references past case law data and calculates the fault ratio based on the input accident information. For example, it automatically determines the fault ratio for a rear-end collision as 80:20.

[0272] Step 7:

[0273] Server: Calculates the amount of compensation based on the percentage of fault. Specifically, the damages such as medical expenses (e.g., 500,000 yen), repair costs (e.g., 300,000 yen), and compensation (e.g., 200,000 yen) are evaluated according to a standard, and the total amount of damages (e.g., 1 million yen) is calculated.

[0274] Server: Calculate the amount of compensation that the at-fault party must pay based on the percentage of fault. For example, if the total damages are 1 million yen, the amount that the at-fault party must pay is 800,000 yen, which is 80% of the total damages.

[0275] Step 8:

[0276] Device: The emotion engine analyzes the user's reactions, facial expressions, and tone of voice in real time when inputting information.

[0277] Emotion Engine: The emotion engine recognizes the user's emotional data (e.g., text typing speed, changes in tone of voice, subtle changes in facial expressions) and determines their emotional state.

[0278] Step 9:

[0279] Server: The emotion engine takes into account the emotional data it recognizes and adjusts the content and expression of the response. For example, if it senses that the user is feeling stressed, it will provide a more polite and reassuring response. If the user is feeling anxious, it will provide concise and prompt information.

[0280] Step 10:

[0281] Server: Converts the organized results of fault ratio and compensation amount into data to be presented to the user along with the emotion recognition results.

[0282] Step 11:

[0283] Terminal: Receives the final result data from the server and displays it to the user. The information presented to the user includes the final payment amount (e.g., 800,000 yen) and its breakdown (400,000 yen for medical expenses, 240,000 yen for repairs, and 160,000 yen for compensation), as well as recommendations for settlement negotiations. The display content and format are also adjusted according to the user's emotions, as recognized by the emotion engine. For example, if a user is feeling stressed, it will also display relief measures and links to specialist consultations.

[0284] Specific examples

[0285] For example, in the case of a rear-end collision that occurred at an intersection in Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator), the rear of Person A's vehicle was damaged and he suffered injuries requiring medical treatment. User A enters the details of the accident, and the device sends the information to the server. The server standardizes and preprocesses the data, reads case law information, and calculates the degree of fault and the amount of compensation using an AI model. At this time, the emotion engine recognizes Person A's emotions in real time, and the server responds accordingly. The amount of compensation ultimately presented to Person A is 800,000 yen, and advice that takes emotions into consideration is also provided along with a detailed breakdown of the amount.

[0286] This system allows users to quickly and accurately calculate compensation amounts automatically, and emotion recognition enables user-friendly information presentation, significantly reducing the effort required for settlement negotiations and reducing stress for users.

[0287] Example 2

[0288] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0289] When a traffic accident occurs, it is necessary to quickly and accurately determine compensation for damages, but it often takes time to refer to past case law information. Furthermore, if there is an error in the information entered by the user, the processing may be further delayed. Furthermore, a system that ignores the user's emotional state may impair the user experience and increase stress. It is necessary to solve these issues, realize fast and accurate calculation of compensation amounts, and improve the user experience.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0291] In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a database of past cases using the standardized and preprocessed accident information, means for calculating a fault ratio based on the case information, means for calculating a compensation amount according to a standard, means for recognizing the user's emotional state and adjusting the response content, and means for presenting the calculated fault ratio and compensation amount to the user. This enables a quick and accurate calculation of compensation amount, and also enables the user experience to be improved by providing a response that takes the user's emotional state into consideration.

[0292] (Definitions of important words)

[0293] "Accident information" is detailed data related to a traffic accident, including the date and time of the accident, the location, the people involved, the accident situation, and specific details of the damage.

[0294] "Standardization" is the process of standardizing the format and content of received data to ensure consistency.

[0295] "Preprocessing" is the process of checking data for errors or omissions and converting it into an appropriate format.

[0296] The "case law database" is a database that stores case law information related to past traffic accidents.

[0297] "Analysis" is the process of analyzing data and extracting and utilizing useful information from it.

[0298] "Fault ratio" is a number that indicates the percentage of fault of each party involved in a traffic accident.

[0299] "Amount of damages" refers to the amount to be paid for damages caused by a traffic accident.

[0300] "Natural language processing technology" is a technology that analyzes text data and converts it into a format that machines can understand.

[0301] "Emotional state" refers to the user's mental and emotional state.

[0302] "Adjusting the response content" refers to the process of changing the system's response and display content according to the user's emotional state.

[0303] The above are definitions of important terms contained in the claims.

[0304] MODE FOR CARRYING OUT THE INVENTION

[0305] This system quickly and accurately determines compensation for damages in traffic accidents, and provides an optimal user experience while recognizing the user's emotions. This system consists of the following main components:

[0306] User Input Terminal

[0307] Terminal: A device with an interface for inputting detailed information about a traffic accident. Specifically, it includes a computer, smartphone, tablet, etc. The user uses this terminal to input accident information.

[0308] Example: A user may use a smartphone to input the date and time of an accident, its location, the people involved, the circumstances of the accident, and specific details of the damage.

[0309] server

[0310] Server: A core device that receives data sent by users and performs various processes. The server performs multiple processes such as data standardization, reading past case law information, and calculating the degree of fault and compensation amount.

[0311] Example: A server receives user input and standardizes date and time formats or address notation to ensure uniform formatting.

[0312] Database

[0313] Database: A system that stores past case data and damage compensation standards, allowing the server to load and analyze case information.

[0314] Example: The server reads case law information on rear-end collisions from the "Red Book," then uses natural language processing technology to structure the data and convert it into a format that can be used by an AI model.

[0315] Emotion Engine

[0316] Emotion engine: A device or software that recognizes emotions based on user input and reactions and adjusts the system's response.

[0317] Example: When a user types something, the camera and microphone on the smartphone are used to analyze emotions from facial expressions and tone of voice.

[0318] Specific procedures for determining damages

[0319] The server receives the input accident information, standardizes and preprocesses it, and then reads case law information from the database. It then uses this information to calculate the degree of fault and calculate the amount of compensation. It also recognizes the user's emotional state and adjusts the response accordingly.

[0320] Prompt Sentence Examples

[0321] When entering details of a traffic accident, specific prompts might include:

[0322] "Please enter details about a rear-end collision that occurred in Tokyo on October 1, 2023. For example, please enter the date and time of the accident, location, parties involved (victim, perpetrator), accident circumstances (rear-end collision, side collision, etc.), and specific details of the damage (vehicle damage, need for medical treatment, etc.)."

[0323] This system enables quick and accurate damage compensation determination and provides optimal responses that take the user's feelings into consideration, thereby reducing the burden on the user and providing a more comfortable user experience.

[0324] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0325] Step 1: User Input Phase

[0326] User: Enters detailed information about the traffic accident into the input terminal. Specifically, the user enters the date and time of the accident, the location, the parties involved (victim, perpetrator), the accident situation (rear-end collision, side collision, etc.), and the specific details of the damage. This is done using the input form displayed on the terminal.

[0327] Input: Accident details (date, time, location, people involved, situation, damage details)

[0328] Output: Initial data entered into the terminal

[0329] Specific actions: The user enters details into the smartphone interface.

[0330] Step 2: Data transmission phase

[0331] Terminal: Validates input and sends data to the server based on format. Format validation includes consistency of date and time formats and address notation.

[0332] Input: Initial data entered into the terminal

[0333] Output: Standardized data sent to the server

[0334] Specific operation: The smartphone sends the entered data to the server.

[0335] Step 3: Data reception and preprocessing phase

[0336] Server: Receives accident information sent by users, checks for errors in format and content, and then standardizes the received data to ensure consistency in format and content.

[0337] Input: Data sent to the server

[0338] Output: Standardized and preprocessed data

[0339] Specific operation: The server checks the format and content of the received data and standardizes it, for example, standardizing the date and time and address notation.

[0340] Step 4: Case law information loading phase

[0341] Server: Reads case information from the "Red Book" and "Blue Book" and structures it using natural language processing techniques, including converting it into a format that can be used by the AI ​​model.

[0342] Input: Standardized accident information

[0343] Output: Structured case law information

[0344] Specific operation: The server extracts text data from the database and analyzes and structures it using natural language processing.

[0345] Step 5: Calculation of fault

[0346] Server: Structured case law information and standardized accident information are input into the AI ​​model, and the fault ratio is calculated based on past case law data.

[0347] Input: Structured case law information, standardized accident information

[0348] Output: Calculated fault ratio

[0349] How it works: The server inputs data into the AI ​​model and calculates the fault ratio. For example, in the case of a rear-end collision, an 80:20 fault ratio is applied.

[0350] Step 6: Calculation of damages

[0351] Server: Calculates the amount of compensation based on the calculated percentage of fault and clearly indicates the breakdown (medical expenses, repair costs, compensation, etc.).

[0352] Input: Fault ratio, detailed damage information (medical expenses, repair costs, compensation, etc.)

[0353] Output: Calculated damages amount and breakdown

[0354] Specific operation: The server calculates the total damage amount and then applies the fault ratio to calculate the amount that the at-fault party must pay. For example, if the total damage amount is 1 million yen and the at-fault party is responsible for 80% of the damage, the amount of compensation will be 800,000 yen.

[0355] Step 7: Emotion Recognition Phase

[0356] Terminal: The emotion engine analyzes the user's reactions when inputting information, facial expressions, tone of voice, etc.

[0357] Input: User reaction data (facial expressions, tone of voice, etc.)

[0358] Output: Recognized emotional state of the user

[0359] Specific operation: The smartphone's camera and microphone capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[0360] Step 8: Presentation of results

[0361] Server: Organizes the calculated percentage of fault and compensation amount and sends them to the user in an easy-to-understand format.

[0362] Input: Calculated percentage of fault and amount of damages

[0363] Output: Organized result data

[0364] What it does: The server organizes the results and formats them appropriately for presentation to the user.

[0365] Terminal: Displays the results from the server to the user, providing any necessary information or links to additional resources (such as an FAQ page).

[0366] Input: Organized result data

[0367] Output: The results displayed to the user and links to related information

[0368] Specific operation: The smartphone displays the results and their breakdown, as well as a response based on the user's emotions (such as a leaflet or a link to an FAQ page).

[0369] The above are the specific processing steps and operations of this system. This process has the effect of improving the accuracy of input information, quickly calculating the percentage of fault, and taking user emotions into consideration.

[0370] (Application example 2)

[0371] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0372] When a car accident occurs, both the victim and the at-fault party want a quick and accurate judgment on compensation, but the process is complicated and often causes great stress for users. In addition, there is a lack of means to provide appropriate emotional support to the driver and passengers during the accident process, which can easily increase the psychological burden.

[0373] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a past case database using the standardized and preprocessed accident information, means for calculating a fault ratio based on the case information, means for calculating a compensation amount according to a standard, means for presenting the calculated fault ratio and compensation amount to the user, means for collecting accident data using an in-vehicle sensor, means for standardizing and analyzing the collected data in real time, and means for recognizing the user's emotions and adjusting the response. This enables a quick and accurate determination of compensation and reduces the psychological burden by providing support that takes the user's emotions into consideration.

[0374] The "means for receiving input accident information" refers to a device or system that receives information about accidents sent from a user or an autonomous vehicle.

[0375] "Means for standardizing and preprocessing received accident information" refers to devices or systems that organize received accident information into a unified format and process it into a form suitable for analysis.

[0376] "Means for reading and analyzing relevant case information from a database of past case law" refers to a device or system that searches stored past case law data, obtains case law information corresponding to the input accident information, and analyzes it.

[0377] The "means for calculating the degree of fault" is a device or system that calculates the degree of fault in an accident based on the acquired case law information.

[0378] A "means for calculating the amount of compensation according to a standard" is a device or system that calculates the amount of compensation to be paid by the victim and the perpetrator based on the calculated degree of fault.

[0379] The "means for presenting the calculated fault ratio and compensation amount to the user" refers to a device or system that presents the calculation results to the user visually or audibly in an easy-to-understand manner.

[0380] "Means for collecting accident data using in-vehicle sensors" refers to devices and systems that use various sensors installed in autonomous vehicles to collect situational data when an accident occurs.

[0381] "Means for standardizing and analyzing collected data in real time" refers to devices and systems that instantly standardize data obtained from sensors inside the vehicle and quickly perform analysis work.

[0382] The "means for recognizing the user's emotions and adjusting the response" refers to a device or system that recognizes the user's emotional state from their facial expressions and voice, and provides an appropriate response accordingly.

[0383] The system of the present invention is intended to be installed in an autonomous vehicle. When a traffic accident occurs, the system analyzes the details of the accident quickly and accurately, determines compensation for damages, and provides emotional support to the user.

[0384] System configuration

[0385] Hardware Configuration

[0386] 1. Sensor equipment: Vehicle sensors such as cameras, LiDAR, and GPS.

[0387] 2. User input terminals: displays and voice recognition systems inside autonomous vehicles.

[0388] 3. Server: A central processing unit that analyzes data.

[0389] 4. Database: External storage for storing past case data and damage compensation standards.

[0390] Software Configuration

[0391] 1. Data standardization and preprocessing module: Software that standardizes received accident data and prepares it in a form suitable for analysis.

[0392] 2. Natural Language Processing Module: NLP library for parsing and structuring text data.

[0393] 3. AI analysis module: A generative AI model that calculates the degree of fault and compensation amount based on accident data and case law data.

[0394] 4. Emotion recognition module: Software that analyzes the user's facial expressions and voice to recognize emotions.

[0395] Program processing explanation

[0396] When an accident occurs, the server processes data in the following manner.

[0397] Receiving and standardizing accident data

[0398] The server receives real-time accident data collected from autonomous vehicles through sensors. The data is then converted into a standardized format by the data standardization and preprocessing module, making it suitable for analysis. This standardization ensures consistency in date and time formats and address notation, improving analysis accuracy.

[0399] Analysis of case law information

[0400] The server inputs the standardized accident data into a natural language processing module, which then structures the data from past legal cases. The structured data is then analyzed by an AI analysis module, which calculates the percentage of fault and the amount of compensation. Specifically, the standard percentage of fault (80:20) in a rear-end collision, for example, is applied.

[0401] Emotion Recognition and Response

[0402] The user's emotional state is captured in real time using the in-car camera and microphone and analyzed by the emotion recognition module. The analyzed emotional data is sent to the server, and the system responds taking the user's psychological state into consideration. For example, if the user is feeling stressed or anxious, the system may respond by displaying a reassuring message on the display or playing relaxing music through the in-car speakers.

[0403] Presentation of results

[0404] The server then sends the analyzed fault percentage and compensation amount to the user's input terminal and presents it to the user in an easy-to-understand format, allowing the user to quickly and accurately determine the amount of compensation, which is useful for dealing with the situation after the accident.

[0405] Specific examples

[0406] For example, if a user has an accident and shows an anxious expression, the generative AI model will generate the following prompt sentence and present it to the user.

[0407] Example prompt sentence:

[0408] "An accident has occurred. Please remain calm. We will now analyze the situation and inform you of the details of compensation."

[0409] "I'll play some relaxing music so you don't have to worry."

[0410] In this way, the system is expected to improve the overall user experience by enabling faster and more accurate accident response in autonomous vehicles and providing support that takes into account the user's emotions.

[0411] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0412] Step 1:

[0413] The server receives accident data from the vehicle's sensor devices, including camera footage, LiDAR data, GPS location information, etc. It collects these input data in real time for further processing.

[0414] Step 2:

[0415] The server converts the received accident data into a unified format using the data standardization and preprocessing module. For example, it standardizes date and time formats and address notations, and corrects data inconsistencies. The standardized data is then formatted for analysis and moves on to the next step.

[0416] Step 3:

[0417] The server inputs the standardized accident data into a natural language processing module, reads relevant case information from a database of past cases, and structures it. Specifically, it analyzes the text data and extracts information on the accident situation and the degree of fault. This structured data is then passed to the AI ​​analysis module.

[0418] Step 4:

[0419] The server uses an AI analysis module to compare structured case data with accident data and calculate the fault ratio and compensation amount. For example, in the case of a rear-end collision, the fault ratio is calculated as 80:20. This calculation provides a basis for compensation amount.

[0420] Step 5:

[0421] The server sends the calculated percentage of fault and compensation amount to the user's input terminal for presentation to the user. The user can check this information through the in-car display or voice guidance system. The presented information includes a breakdown of the compensation amount and the legal precedents that serve as the basis.

[0422] Step 6:

[0423] The emotion recognition module uses the in-car camera and microphone to analyze the user's facial expressions and tone of voice to recognize their emotional state. For example, if the user has an anxious expression, the module analyzes the emotional data and sends it to the server.

[0424] Step 7:

[0425] The server adjusts the response and display method based on the user's emotional state. For example, if the server recognizes that the user is feeling stressed or anxious, it will display a reassuring message on the display and play relaxing music through the car speakers.

[0426] Step 8:

[0427] The user can check the final amount of compensation and the percentage of fault and decide on the next steps. Based on this information, it is expected that contacting insurance companies and legal procedures will proceed smoothly.

[0428] Through the above steps, the system of the present invention realizes quick and accurate accident analysis and damage compensation determination, and furthermore, it reduces the psychological burden by supporting the user's emotions.

[0429] 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.

[0430] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0431] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0432] [Second embodiment]

[0433] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0434] 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.

[0435] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0436] 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.

[0437] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0438] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0439] 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.

[0440] 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.

[0441] 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 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.

[0442] 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.

[0443] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0444] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0445] The system of the present invention is designed to quickly and accurately automate compensation for damages in automobile accidents, and includes a process for inputting details of a traffic accident and calculating the percentage of fault and the amount of compensation. Specific embodiments and the flow of program processing are described below.

[0446] System configuration

[0447] The system mainly consists of the following components:

[0448] 1. User input terminal: A device with an interface for entering details of a traffic accident. This can be a computer, smartphone, tablet, etc.

[0449] 2. Server: This is the core device that receives data sent by users and performs various processes, such as standardizing data, reading legal precedent information, and calculating the degree of fault and the amount of compensation.

[0450] 3. Database: A system that stores past case data and compensation standards.

[0451] System Operation

[0452] Below we explain the process from entering accident information to presenting the final amount of compensation.

[0453] User Input Phase

[0454] User: Enters detailed information about the traffic accident into the input terminal, such as the date and time of the accident, location, parties involved (victim, perpetrator), accident circumstances (rear-end collision, side collision, etc.), and specific details of the damage (vehicle damage, need for medical treatment, etc.).

[0455] Terminal: Checks the input and sends the data to the server based on the format.

[0456] Data reception and preprocessing phase

[0457] Server: Receives the accident information sent by the user.

[0458] Server: Standardizes incoming data and ensures consistency in format and content, for example, standardizing date and time formats and address notation.

[0459] Server: Data preprocessing includes imputing missing values ​​and detecting and correcting input errors.

[0460] Case law information loading phase

[0461] Server: Loads relevant information from case law databases, including damage standards such as the "Red Book" and "Blue Book."

[0462] Server: Uses natural language processing techniques to structure the text data and convert it into a format that can be used by the AI ​​model.

[0463] Fault calculation phase

[0464] Server: Inputs standardized and preprocessed accident information and structured case law information into the AI ​​model.

[0465] Server: The AI ​​model calculates the percentage of fault based on past legal precedent data. For example, by looking at past legal precedents for rear-end collisions, the model may determine that the assailant is 80% at fault and the victim is 20% at fault.

[0466] Damages calculation phase

[0467] Server: Calculates the amount of compensation based on the determined percentage of fault and the damage data entered. Damages such as medical expenses, repair costs, and compensation are calculated according to standards.

[0468] Server: For example, if medical expenses are 500,000 yen, repair costs are 300,000 yen, and compensation is 200,000 yen, the total damages are 1,000,000 yen. The amount of compensation that the perpetrator must pay is 80% of 1,000,000 yen, or 800,000 yen.

[0469] Results presentation phase

[0470] Server: Organizes the calculated percentage of fault and amount of damages and converts them into a format that can be notified to the user.

[0471] Terminal: Displays the results received from the server to the user. For example, the final payment amount of 800,000 yen and its breakdown (medical expenses 400,000 yen, repair expenses 240,000 yen, and compensation 160,000 yen) is displayed to the user.

[0472] Specific examples

[0473] For example, consider a rear-end collision that occurred in Shibuya Ward, Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator), resulting in damage to the rear of Person A's vehicle and injuries requiring medical treatment. User Person A enters the accident information, and the device sends that information to the server. The server processes the received data, loads appropriate case law information, and runs it through an AI model. Person B is determined to be 80% at fault, and the amount of compensation to Person A is calculated to be 800,000 yen. This result is presented to the user.

[0474] This system allows users to automatically calculate the amount of damages quickly and accurately, significantly reducing the effort required for settlement negotiations.

[0475] The processing flow will be explained below.

[0476] Step 1:

[0477] User: Enter detailed information about the traffic accident (e.g., date and time of the accident, location, people involved, accident situation, damage details, etc.).

[0478] Terminal: Converts the input information into the appropriate format and sends it to the server.

[0479] Step 2:

[0480] Server: Receives the accident information sent by the user.

[0481] Server: Checks whether the format and content of the received data are correct.

[0482] Step 3:

[0483] Server: Standardizes the received accident information. For example, standardize the date and time to the "YYYY-MM-DD HH:MM" format and standardize address notation.

[0484] Server: Preprocesses the data, completes missing data, and corrects input errors.

[0485] Step 4:

[0486] Server: Reads case information from the "Red Book" and "Blue Book."

[0487] Server: The read text data is structured using natural language processing technology and converted into a format that can be used by the AI ​​model.

[0488] Step 5:

[0489] Server: Structured case law information and standardized accident information are input into the AI ​​model.

[0490] Server: The AI ​​model refers to past case data and calculates the fault ratio based on the input accident information. For example, in the case of a rear-end collision, the fault ratio is usually 80:20.

[0491] Step 6:

[0492] Server: Calculate the amount of compensation based on the percentage of fault. For example, if the victim's medical expenses are 500,000 yen, repair costs are 300,000 yen, and compensation is 200,000 yen, the total damages will be 1 million yen.

[0493] Server: Calculate the amount of compensation that the at-fault party must pay based on their percentage of fault. In the example above, the amount that must be paid is 800,000 yen, which is 80% of the total damages of 1 million yen, and is the at-fault party's percentage of the total damages.

[0494] Step 7:

[0495] Server: Organizes the calculated percentage of fault and compensation amount and summarizes them in a format that is easy for users to understand.

[0496] Terminal: Displays the results received from the server to the user. For example, the final payment amount of 800,000 yen and its breakdown (medical expenses 400,000 yen, repair expenses 240,000 yen, and compensation 160,000 yen) is displayed.

[0497] Step 8:

[0498] User: Check the displayed results and, if necessary, correct the accident information again or proceed with settlement negotiations.

[0499] Example 1

[0500] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0501] When a traffic accident occurs, it is important to calculate compensation for damages quickly and accurately. However, conventional methods require manual input, calculations, and analysis of legal precedents, which is time-consuming and prone to errors. To solve these problems, a system is needed that automates the process from inputting accident information to calculating compensation amounts, thereby improving accuracy.

[0502] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0503] In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a database of past case law using the standardized and preprocessed accident information, means for calculating the degree of fault based on the case law information, means for calculating the amount of compensation in accordance with a standard, means for presenting the calculated degree of fault and amount of compensation to the user, means for checking the input accident information and transmitting the data in an appropriate format, and means for using natural language processing technology to structure text data and convert it into a format for input to an AI model. This automates the process from inputting accident information to calculating the amount of compensation, making it possible to perform the process quickly and accurately.

[0504] "Accident information" refers to detailed data including the date and time of the traffic accident, the location of the accident, information about those involved (such as the names and contact information of the victim and perpetrator), the circumstances of the accident (such as a rear-end collision or a side collision), and the damages (such as damage to the vehicle and the need for medical treatment).

[0505] The "receiving means" refers to a hardware or software element that has the function of accurately acquiring input from a user or data from an external system.

[0506] "Standardization and preprocessing means" refers to processes that standardize the format of received data, fill in inconsistencies and missing values, and prepare the data for analysis.

[0507] "Case law information" refers to data on the results of past trials and settlements regarding similar traffic accidents, as well as the percentage of fault and amount of compensation calculated at those times.

[0508] "Natural language processing technology" is a technology that allows computers to understand and analyze the language that humans use on a daily basis, and is a technology that structures text data and performs semantic analysis.

[0509] "Fault ratio" is a number that indicates the percentage of responsibility that each party should bear for damages caused in a traffic accident.

[0510] "Compensation" refers to the amount that the at-fault party must pay for the damages suffered by the victim in a traffic accident, including medical expenses, repair costs, and compensation.

[0511] The "presentation means" refers to hardware or software elements that have the function of displaying or notifying the calculated results to users and other related parties in an easy-to-understand manner.

[0512] An "AI model" is a collection of algorithms that use machine learning and deep learning to analyze data and automatically perform tasks such as prediction and classification.

[0513] "Structuring technology" is a technology that analyzes unstructured data and converts it into a format that can be used in databases and analysis systems.

[0514] The present invention provides a system for automatically calculating compensation for damages after a traffic accident, quickly and accurately. Specific embodiments of this system are described below.

[0515] System configuration

[0516] This system consists of a user input terminal, a server, and a database.

[0517] User Input Device: The device used to enter details of a traffic accident, such as a computer, smartphone, or tablet.

[0518] Server: This is the core device that analyzes the received data and calculates the percentage of fault and the amount of compensation. It also performs data standardization, preprocessing, reading case law information, and various calculations.

[0519] Database: A system that stores past case data and damages standards. The case information database includes damages standards such as the "Red Book" and the "Blue Book."

[0520] System Operation

[0521] The system processes traffic accident data using the following hardware and software:

[0522] 1. When a traffic accident occurs, the user uses a smartphone or computer to input accident information, including the date and time of the accident, the location of the accident, information about the people involved, the accident situation, and details of the damage.

[0523] 2. The terminal checks the entered accident information, converts it into the appropriate format, and sends it to the server. For example, it standardizes the date and time format and address notation, and checks for and corrects any inconsistencies or missing entries.

[0524] 3. The server standardizes and preprocesses the received accident information. Standardization includes standardizing date and time formats and address notation, and preprocessing includes filling in missing values ​​and detecting and correcting input errors.

[0525] 4. The server reads the relevant case information from the case information database, analyzes and structures the text data using natural language processing technology, and then segments and tags the text data before storing it in the database.

[0526] 5. The server uses the standardized and preprocessed accident information and structured case law information to calculate the percentage of fault using an AI model. It references past case law data and calculates based on the percentage of fault in similar accidents.

[0527] 6. The server calculates the amount of compensation based on the percentage of fault and the damage data entered by the user. The damages include medical expenses, repair costs, compensation, etc. Based on these, the total amount of damages is calculated and the amount of compensation according to the percentage of fault is determined.

[0528] 7. The server organizes the calculated percentage of fault and compensation amount and converts it into a format that can be notified to the user. The results include a breakdown of the compensation amount (medical expenses, repair costs, compensation, etc.).

[0529] 8. The terminal displays the results received from the server to the user. For example, it may present the results in the form of "Final payment amount: 800,000 yen (medical expenses 400,000 yen, repair expenses 240,000 yen, compensation 160,000 yen)."

[0530] Specific examples

[0531] For example, consider a case involving a rear-end collision that occurred somewhere in Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator). When Person A's vehicle rear end was damaged and he suffered injuries requiring medical treatment, User A inputs the accident information. The input information is sent to the server, which processes the information. It reads relevant case law information and uses an AI model to determine the fault ratio as 80:20, calculating the amount of damages as 800,000 yen. The results are then presented to the user.

[0532] Prompt Sentence Examples

[0533] "Information on victim A and assailant B has been entered for a rear-end collision that occurred in a certain location in Tokyo on October 1, 2023. The damage involved damage to the rear of the vehicle and the need for medical treatment. Please calculate the percentage of fault and the amount of compensation based on past legal precedents."

[0534] This system makes it possible to quickly and accurately calculate compensation for damages caused by traffic accidents, significantly reducing the burden on users.

[0535] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0536] System processing flow

[0537] Step 1: Enter accident information

[0538] The user inputs details of the traffic accident using a smartphone or computer, including the date and time of the accident, the location of the accident, information about the parties involved (such as the names and contact information of the victim and at-fault parties), the circumstances of the accident (such as rear-end collision or side-impact collision), and details of damage (such as vehicle damage and medical needs).The system records the accident circumstances based on the input data and proceeds to the next step.

[0539] Step 2: Send data

[0540] The terminal checks the information entered by the user and converts it into the appropriate format. Specifically, it standardizes the date and time format and address notation. The converted data is then sent to the server. By converting the entered data into a format and sending it to the server, data consistency is maintained.

[0541] Step 3: Receiving and Preprocessing Data

[0542] The server receives the accident information sent from the terminal. After receiving it, it standardizes and preprocesses the data. Specifically, it standardizes the date and time format and address notation, fills in missing values, and detects and corrects input errors. This prepares the data in an analyzable format and allows it to proceed to the next step.

[0543] Step 4: Loading case law information

[0544] The server reads relevant case information from a case information database. For example, it extracts cases related to compensation for damages from databases such as the "Red Book" and "Blue Book." It then uses natural language processing technology to analyze and structure the text data, converting it into a format that can be input into the AI ​​model. This converts the case data into a format that can be used for analysis.

[0545] Step 5: Calculating the percentage of fault

[0546] The server uses the standardized and preprocessed accident information and structured case law information to calculate the percentage of fault using an AI model. Specifically, it calculates the percentage of fault corresponding to the input accident information based on past case law data. For example, it refers to past cases related to rear-end collisions to determine the percentage of fault between the assailant and the victim. It then proceeds to the next step based on the calculated percentage of fault.

[0547] Step 6: Calculating damages

[0548] The server calculates the amount of compensation based on the determined fault ratio and the damage data entered by the user. Specifically, it calculates the total amount of damages using data such as medical expenses, repair costs, and compensation, and then calculates the amount of compensation based on the fault ratio. For example, if the total amount of damages is 1 million yen and the fault ratio is 80:20, the amount of compensation that the perpetrator must pay is 800,000 yen. The process proceeds to the next step based on the calculated amount of compensation.

[0549] Step 7: Organize and communicate results

[0550] The server organizes the calculated percentage of fault and compensation amount and converts them into a format that can be notified to the user. Specifically, it formats the results, including a breakdown of the compensation amount (e.g., medical expenses, repair costs, compensation, etc.). The formatted data is sent to proceed to the next step.

[0551] Step 8: Viewing the results

[0552] The terminal displays the results received from the server to the user. Specifically, it presents the results in the form of "Final payment amount: 800,000 yen (medical expenses 400,000 yen, repair expenses 240,000 yen, compensation 160,000 yen)." This allows the user to quickly and accurately check the amount of compensation.

[0553] (Application example 1)

[0554] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0555] Conventional methods for calculating compensation for traffic accidents require human intervention, which requires a significant amount of time and effort. Furthermore, there is a problem in that accident analysis and compensation calculation for autonomous vehicles cannot be performed in real time. This can lead to delays in settlement negotiations and in some cases to an inability to present accurate compensation amounts. Furthermore, the time required for rapid accident analysis and notification of results increases the mental and financial burden on both the victim and the at-fault party.

[0556] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0557] In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a past case database using the standardized and preprocessed accident information, means for calculating a fault ratio based on the case information, means for calculating a compensation amount according to a standard, means for presenting the calculated fault ratio and compensation amount to a user, means for recording sensor information from the autonomous vehicle and collecting accident data, and means for transmitting the accident data to the server via the vehicle's communication module. This makes it possible, when an autonomous vehicle is involved in a traffic accident, to quickly record the accident situation and quickly and accurately calculate a fault ratio and compensation amount.

[0558] "Accident information" is detailed data about traffic accidents, including the date and time of the accident, location, people involved, accident circumstances, and damage details.

[0559] The "receiving means" refers to a method and device for capturing the input accident information into the server.

[0560] "Standardization and preprocessing means" refers to methods and devices for standardizing received incident information into a consistent format and correcting missing or incorrect data.

[0561] The "Case Law Database" is a data store that compiles case law and compensation standards related to past traffic accidents.

[0562] The "means for reading and analyzing" refers to a method and apparatus for extracting and analyzing relevant case information from a case database based on the standardized and preprocessed accident information.

[0563] The "means for calculating the degree of fault" refers to a method and device for calculating the degree of fault of each party based on legal precedent information.

[0564] The "means for calculating the amount of compensation" refers to a method and device for calculating the amount of compensation for each party based on the determined degree of fault and damage data.

[0565] The "means for presenting to the user" refers to a method and device for displaying the calculated fault ratio and compensation amount to the user.

[0566] "Means for recording sensor information" refers to methods and apparatus for storing accident-related data obtained from sensors installed on an autonomous vehicle.

[0567] A "communication module" is a communication device used to transfer data from a vehicle to a server.

[0568] This invention is a system that quickly and accurately collects accident information when an autonomous vehicle is involved in a traffic accident, and automatically calculates the percentage of fault and the amount of compensation. Specific embodiments of this system are described below.

[0569] System Overview

[0570] This system consists of a user input terminal, a server, a database, various sensors in the autonomous vehicle, and a communication module. These components work together as follows:

[0571] 1. User Input Terminal

[0572] This is a device that allows users to input detailed information about traffic accidents. The device can be a smartphone, tablet, or vehicle display system.

[0573] 2. Server

[0574] The server is the main device responsible for receiving accident information, standardizing and preprocessing it, reading case law information, calculating the degree of fault and compensation amounts, and presenting the results. In particular, it uses natural language processing technologies (such as NLTK and spaCy) and AI models (such as TensorFlow and PyTorch) to standardize and preprocess the received accident information and extract relevant information from the case law database.

[0575] 3. Database

[0576] The database holds information on past legal precedents and damage compensation standards, from which the server extracts the necessary information and uses it for analytical processing.

[0577] 4. Sensors and communication modules for autonomous vehicles

[0578] Autonomous vehicles are equipped with front and rear cameras and various sensors that record information in real time when an accident occurs. This data is sent to a server via the vehicle's communication module, using 5G communication technology.

[0579] System operation flow

[0580] The operation of the system consists of the following major steps:

[0581] 1. Collecting accident information

[0582] Sensors in autonomous vehicles record accident information in real time, acquiring information such as the location and time of the accident, and information on those involved. This information is then sent to a server via the vehicle's communication module.

[0583] 2. Receiving and preprocessing accident information

[0584] The server standardizes the received accident information, filling in missing values ​​and checking for format consistency, for example, standardizing date and time formats and address notation.

[0585] 3. Extraction and structuring of case law information

[0586] The server reads the relevant case information from the case database and structures it using natural language processing technology, converting it into a format that can be used by the AI ​​model.

[0587] 4. Calculation of the percentage of fault and the amount of compensation

[0588] Standardized and preprocessed accident information and structured case law information are input into the AI ​​model to calculate the degree of fault and the amount of compensation.

[0589] 5. Presentation of results

[0590] The calculated percentage of fault and amount of compensation for damages are sent from the server to the user input terminal in a format that can be confirmed by the user, and are displayed.

[0591] Specific examples

[0592] For example, in the case of a rear-end collision that occurred in Tokyo on October 1, 2023, the victim was Person A and the assailant was Person B, and Person A's rear end was damaged, resulting in injuries requiring medical treatment. Accident information acquired by the autonomous vehicle is sent to a server in real time, where it is standardized and preprocessed. The server refers to a database of past legal precedents, extracts appropriate data, and inputs it into an AI model to calculate the percentage of fault between the victim and the assailant, and calculates the amount of compensation. The results are displayed on the autonomous vehicle's display system and on the user's smartphone.

[0593] Prompt Sentence Examples

[0594] "Please enter the details of a rear-end collision that occurred in Tokyo on October 1, 2023. The victim is Person A and the perpetrator is Person B. The rear of Person A's vehicle is damaged and requires medical treatment."

[0595] This system allows for quick and accurate calculation of compensation in traffic accidents, significantly reducing the effort required for settlement negotiations.

[0596] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0597] Step 1:

[0598] Sensors in autonomous vehicles record accident information in real time.

[0599] Input: Real-time data from sensors and cameras (location of the accident, speed, impact angle, etc.).

[0600] Output: Recorded accident data.

[0601] Specific operation: Various sensors and cameras installed in the autonomous vehicle detect the moment of an accident and store the data in internal memory. These sensors include acceleration sensors, gyro sensors, and video cameras.

[0602] Step 2:

[0603] The accident data is transmitted to a server via the vehicle's communication module.

[0604] Input: Accident data recorded in step 1.

[0605] Output: The incident data sent to the server.

[0606] How it works: Recorded accident data is sent to a server in real time via a 5G communication module installed in the vehicle, which requires high data transfer speeds and low latency.

[0607] Step 3:

[0608] The server normalizes and pre-processes the received accident data.

[0609] Input: Accident data sent to the server.

[0610] Output: Standardized and preprocessed accident data.

[0611] Specific operation: The server standardizes the date and time format and address notation of the received accident data, and performs missing data completion and correction of input errors to maintain consistency. This process is often performed using the Python library Pandas.

[0612] Step 4:

[0613] The server reads the relevant case information from the case database and structures it using natural language processing technology.

[0614] Input: Standardized and preprocessed accident data.

[0615] Output: Structured case law information.

[0616] Specific operation: The server searches and extracts relevant information from case law databases such as the "Red Book" and "Blue Book," and structures the text data using natural language processing technology (such as NLTK or spaCy).

[0617] Step 5:

[0618] The server inputs standardized accident data and structured case law information into an AI model to calculate the degree of fault.

[0619] Input: Standardized accident data and structured case law information.

[0620] Output: Calculated fault percentage.

[0621] Specific operation: Standardized accident data and structured case law information are input into an AI model (using TensorFlow and PyTorch), and the fault ratio of each party is calculated based on past case law data.

[0622] Step 6:

[0623] The server calculates the amount of compensation based on the determined fault percentage and damage data.

[0624] Input: Calculated fault percentage and damage data.

[0625] Output: Calculated damages.

[0626] Specific operation: The server calculates the total amount of damages based on the percentage of fault and the input damage data (medical expenses, repair costs, compensation, etc.), and then calculates the amount of compensation that the at-fault party must pay. Specifically, it multiplies and adds the amounts.

[0627] Step 7:

[0628] The server presents the calculated percentage of fault and the amount of compensation to the user.

[0629] Input: Calculated percentage of fault and damages.

[0630] Output: Percent fault and damages in a user-viewable format.

[0631] Specific operation: The server converts the calculation results into an easy-to-understand format and sends them to the user's input device (smartphone or vehicle display system), allowing both the victim and the perpetrator to check the results in real time.

[0632] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0633] The system of the present invention quickly and accurately determines compensation for damages in automobile accidents and further improves the user experience by recognizing the user's emotions. Specific embodiments of the present invention and the flow of program processing are described below.

[0634] System configuration

[0635] The system mainly consists of the following components:

[0636] 1. User input terminal: A device with an interface for entering details of a traffic accident. This can be a computer, smartphone, tablet, etc.

[0637] 2. Server: This is the core device that receives data sent by users and performs various processes, such as standardizing data, reading legal precedent information, and calculating the degree of fault and the amount of compensation.

[0638] 3. Database: A system that stores past case data and compensation standards.

[0639] 4. Emotion engine: A device or software that recognizes emotions based on user input and reactions and adjusts the system's response.

[0640] System Operation

[0641] Below we explain the process from inputting accident information to presenting the final compensation amount and optimizing it through emotion recognition.

[0642] User Input Phase

[0643] User: Enters detailed information about the traffic accident into the input terminal, such as the date and time of the accident, location, parties involved (victim, perpetrator), accident circumstances (rear-end collision, side collision, etc.), and specific details of the damage (vehicle damage, need for medical treatment, etc.).

[0644] Terminal: Checks the input and sends the data to the server based on the format.

[0645] Data reception and preprocessing phase

[0646] Server: Receives the accident information sent by the user.

[0647] Server: Checks whether the format and content of the received data are correct.

[0648] Server: Standardizes the received accident information and checks for consistency in format and content, for example, standardizing date and time formats and address notation.

[0649] Case law information loading phase

[0650] Server: Reads case information from the "Red Book" and "Blue Book."

[0651] Server: The read text data is structured using natural language processing technology and converted into a format that can be used by the AI ​​model.

[0652] Fault calculation phase

[0653] Server: Structured case law information and standardized accident information are input into the AI ​​model.

[0654] Server: The AI ​​model refers to past case data and calculates the fault ratio based on the input accident information. For example, in the case of a rear-end collision, the fault ratio is usually 80:20.

[0655] Damages calculation phase

[0656] Server: Calculate the amount of compensation based on the percentage of fault. For example, if the victim's medical expenses are 500,000 yen, repair costs are 300,000 yen, and compensation is 200,000 yen, the total damages will be 1 million yen.

[0657] Server: Calculate the amount of compensation that the at-fault party must pay based on their percentage of fault. In the example above, the amount that must be paid is 800,000 yen, which is 80% of the total damages of 1 million yen, and is the at-fault party's percentage of the total damages.

[0658] Emotion Recognition Phase

[0659] Terminal: The emotion engine analyzes the user's reactions when inputting information, facial expressions, tone of voice, etc.

[0660] Emotion engine: The emotion engine recognizes emotions based on the user's input information and sends the emotional state to the server.

[0661] Server: Adjusts the content, expression, and presentation of responses based on the user's recognized emotions. For example, if the system recognizes that the user is feeling stressed or anxious, it will provide a more polite and reassuring response.

[0662] Results presentation phase

[0663] Server: Organizes the calculated percentage of fault and compensation amount and summarizes them in a format that is easy for users to understand.

[0664] Terminal: Displays the results received from the server to the user. For example, it displays the final payment amount of 800,000 yen and its breakdown (400,000 yen for medical expenses, 240,000 yen for repairs, and 160,000 yen for compensation). In addition, it can display a leaflet or a link to an FAQ page that reflects the user's emotions.

[0665] Specific examples

[0666] For example, consider a rear-end collision that occurred in Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator), resulting in damage to the rear of Person A's vehicle and injuries requiring medical treatment. User A enters accident information, and the device sends that information to the server. The server processes the received data, loads appropriate case law information, and runs it through an AI model. Person B is determined to be 80% at fault, and the amount of compensation to Person A is calculated to be 800,000 yen. This result is presented to the user by the emotion engine, taking into account the user's emotions.

[0667] In this way, users can quickly and accurately calculate the amount of compensation automatically, and the system takes the user's feelings into consideration when presenting optimal information, significantly reducing the effort and stress involved in settlement negotiations.

[0668] The processing flow will be explained below.

[0669] Step 1:

[0670] User: Enter detailed information about the traffic accident, such as the date and time of the accident (e.g., 14:30, October 1, 2023), the location of the accident (an intersection in Tokyo), the parties involved (victim A, assailant B), the accident circumstances (rear-end collision while waiting at a traffic light), and the specific details of the damage (damage to the rear of the vehicle, injuries requiring medical treatment).

[0671] Step 2:

[0672] Terminal: Converts the input information into the appropriate format and sends it to the server, including all the details of the accident.

[0673] Step 3:

[0674] Server: Receives the accident information sent by the user. Immediately after receiving the information, the server checks whether there are any errors in the data format or content. For example, it checks whether the date and time format is standardized to "YYYY-MM-DD HH:MM" and whether the address is written correctly.

[0675] Step 4:

[0676] Server: Standardizes the received accident information. Standardization includes standardizing date and time formats, address notation, and name notation. This ensures data consistency.

[0677] Step 5:

[0678] Server: Reads case law information from the "Red Book" and "Blue Book" databases. Structures the text data using natural language processing technology to analyze past case law data and compensation standards.

[0679] Step 6:

[0680] Server: Structured case law information and standardized accident information are input into the AI ​​model. The AI ​​model references past case law data and calculates the fault ratio based on the input accident information. For example, it automatically determines the fault ratio for a rear-end collision as 80:20.

[0681] Step 7:

[0682] Server: Calculates the amount of compensation based on the percentage of fault. Specifically, the damages such as medical expenses (e.g., 500,000 yen), repair costs (e.g., 300,000 yen), and compensation (e.g., 200,000 yen) are evaluated according to a standard, and the total amount of damages (e.g., 1 million yen) is calculated.

[0683] Server: Calculate the amount of compensation that the at-fault party must pay based on the percentage of fault. For example, if the total damages are 1 million yen, the amount that the at-fault party must pay is 800,000 yen, which is 80% of the total damages.

[0684] Step 8:

[0685] Device: The emotion engine analyzes the user's reactions, facial expressions, and tone of voice in real time when inputting information.

[0686] Emotion Engine: The emotion engine recognizes the user's emotional data (e.g., text typing speed, changes in tone of voice, subtle changes in facial expressions) and determines their emotional state.

[0687] Step 9:

[0688] Server: The emotion engine takes into account the emotional data it recognizes and adjusts the content and expression of the response. For example, if it senses that the user is feeling stressed, it will provide a more polite and reassuring response. If the user is feeling anxious, it will provide concise and prompt information.

[0689] Step 10:

[0690] Server: Converts the organized results of fault ratio and compensation amount into data to be presented to the user along with the emotion recognition results.

[0691] Step 11:

[0692] Terminal: Receives the final result data from the server and displays it to the user. The information presented to the user includes the final payment amount (e.g., 800,000 yen) and its breakdown (400,000 yen for medical expenses, 240,000 yen for repairs, and 160,000 yen for compensation), as well as recommendations for settlement negotiations. The display content and format are also adjusted according to the user's emotions, as recognized by the emotion engine. For example, if a user is feeling stressed, it will also display relief measures and links to specialist consultations.

[0693] Specific examples

[0694] For example, in the case of a rear-end collision that occurred at an intersection in Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator), the rear of Person A's vehicle was damaged and he suffered injuries requiring medical treatment. User A enters the details of the accident, and the device sends the information to the server. The server standardizes and preprocesses the data, reads case law information, and calculates the degree of fault and the amount of compensation using an AI model. At this time, the emotion engine recognizes Person A's emotions in real time, and the server responds accordingly. The amount of compensation ultimately presented to Person A is 800,000 yen, and advice that takes emotions into consideration is also provided along with a detailed breakdown of the amount.

[0695] This system allows users to quickly and accurately calculate compensation amounts automatically, and emotion recognition enables user-friendly information presentation, significantly reducing the effort required for settlement negotiations and reducing stress for users.

[0696] Example 2

[0697] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0698] When a traffic accident occurs, it is necessary to quickly and accurately determine compensation for damages, but it often takes time to refer to past case law information. Furthermore, if there is an error in the information entered by the user, the processing may be further delayed. Furthermore, a system that ignores the user's emotional state may impair the user experience and increase stress. It is necessary to solve these issues, realize fast and accurate calculation of compensation amounts, and improve the user experience.

[0699] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0700] In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a database of past cases using the standardized and preprocessed accident information, means for calculating a fault ratio based on the case information, means for calculating a compensation amount according to a standard, means for recognizing the user's emotional state and adjusting the response content, and means for presenting the calculated fault ratio and compensation amount to the user. This enables a quick and accurate calculation of compensation amount, and also enables the user experience to be improved by providing a response that takes the user's emotional state into consideration.

[0701] (Definitions of important words)

[0702] "Accident information" is detailed data related to a traffic accident, including the date and time of the accident, the location, the people involved, the accident situation, and specific details of the damage.

[0703] "Standardization" is the process of standardizing the format and content of received data to ensure consistency.

[0704] "Preprocessing" is the process of checking data for errors or omissions and converting it into an appropriate format.

[0705] The "case law database" is a database that stores case law information related to past traffic accidents.

[0706] "Analysis" is the process of analyzing data and extracting and utilizing useful information from it.

[0707] "Fault ratio" is a number that indicates the percentage of fault of each party involved in a traffic accident.

[0708] "Amount of damages" refers to the amount to be paid for damages caused by a traffic accident.

[0709] "Natural language processing technology" is a technology that analyzes text data and converts it into a format that machines can understand.

[0710] "Emotional state" refers to the user's mental and emotional state.

[0711] "Adjusting the response content" refers to the process of changing the system's response and display content according to the user's emotional state.

[0712] The above are definitions of important terms contained in the claims.

[0713] MODE FOR CARRYING OUT THE INVENTION

[0714] This system quickly and accurately determines compensation for damages in traffic accidents, and provides an optimal user experience while recognizing the user's emotions. This system consists of the following main components:

[0715] User Input Terminal

[0716] Terminal: A device with an interface for inputting detailed information about a traffic accident. Specifically, it includes a computer, smartphone, tablet, etc. The user uses this terminal to input accident information.

[0717] Example: A user may use a smartphone to input the date and time of an accident, its location, the people involved, the circumstances of the accident, and specific details of the damage.

[0718] server

[0719] Server: A core device that receives data sent by users and performs various processes. The server performs multiple processes such as data standardization, reading past case law information, and calculating the degree of fault and compensation amount.

[0720] Example: A server receives user input and standardizes date and time formats or address notation to ensure uniform formatting.

[0721] Database

[0722] Database: A system that stores past case data and damage compensation standards, allowing the server to load and analyze case information.

[0723] Example: The server reads case law information on rear-end collisions from the "Red Book," then uses natural language processing technology to structure the data and convert it into a format that can be used by an AI model.

[0724] Emotion Engine

[0725] Emotion engine: A device or software that recognizes emotions based on user input and reactions and adjusts the system's response.

[0726] Example: When a user types something, the camera and microphone on the smartphone are used to analyze emotions from facial expressions and tone of voice.

[0727] Specific procedures for determining damages

[0728] The server receives the input accident information, standardizes and preprocesses it, and then reads case law information from the database. It then uses this information to calculate the degree of fault and calculate the amount of compensation. It also recognizes the user's emotional state and adjusts the response accordingly.

[0729] Prompt Sentence Examples

[0730] When entering details of a traffic accident, specific prompts might include:

[0731] "Please enter details about a rear-end collision that occurred in Tokyo on October 1, 2023. For example, please enter the date and time of the accident, location, parties involved (victim, perpetrator), accident circumstances (rear-end collision, side collision, etc.), and specific details of the damage (vehicle damage, need for medical treatment, etc.)."

[0732] This system enables quick and accurate damage compensation determination and provides optimal responses that take the user's feelings into consideration, thereby reducing the burden on the user and providing a more comfortable user experience.

[0733] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0734] Step 1: User Input Phase

[0735] User: Enters detailed information about the traffic accident into the input terminal. Specifically, the user enters the date and time of the accident, the location, the parties involved (victim, perpetrator), the accident situation (rear-end collision, side collision, etc.), and the specific details of the damage. This is done using the input form displayed on the terminal.

[0736] Input: Accident details (date, time, location, people involved, situation, damage details)

[0737] Output: Initial data entered into the terminal

[0738] Specific actions: The user enters details into the smartphone interface.

[0739] Step 2: Data transmission phase

[0740] Terminal: Validates input and sends data to the server based on format. Format validation includes consistency of date and time formats and address notation.

[0741] Input: Initial data entered into the terminal

[0742] Output: Standardized data sent to the server

[0743] Specific operation: The smartphone sends the entered data to the server.

[0744] Step 3: Data reception and preprocessing phase

[0745] Server: Receives accident information sent by users, checks for errors in format and content, and then standardizes the received data to ensure consistency in format and content.

[0746] Input: Data sent to the server

[0747] Output: Standardized and preprocessed data

[0748] Specific operation: The server checks the format and content of the received data and standardizes it, for example, standardizing the date and time and address notation.

[0749] Step 4: Case law information loading phase

[0750] Server: Reads case information from the "Red Book" and "Blue Book" and structures it using natural language processing techniques, including converting it into a format that can be used by the AI ​​model.

[0751] Input: Standardized accident information

[0752] Output: Structured case law information

[0753] Specific operation: The server extracts text data from the database and analyzes and structures it using natural language processing.

[0754] Step 5: Calculation of fault

[0755] Server: Structured case law information and standardized accident information are input into the AI ​​model, and the fault ratio is calculated based on past case law data.

[0756] Input: Structured case law information, standardized accident information

[0757] Output: Calculated fault ratio

[0758] How it works: The server inputs data into the AI ​​model and calculates the fault ratio. For example, in the case of a rear-end collision, an 80:20 fault ratio is applied.

[0759] Step 6: Calculation of damages

[0760] Server: Calculates the amount of compensation based on the calculated percentage of fault and clearly indicates the breakdown (medical expenses, repair costs, compensation, etc.).

[0761] Input: Fault ratio, detailed damage information (medical expenses, repair costs, compensation, etc.)

[0762] Output: Calculated damages amount and breakdown

[0763] Specific operation: The server calculates the total damage amount and then applies the fault ratio to calculate the amount that the at-fault party must pay. For example, if the total damage amount is 1 million yen and the at-fault party is responsible for 80% of the damage, the amount of compensation will be 800,000 yen.

[0764] Step 7: Emotion Recognition Phase

[0765] Terminal: The emotion engine analyzes the user's reactions when inputting information, facial expressions, tone of voice, etc.

[0766] Input: User reaction data (facial expressions, tone of voice, etc.)

[0767] Output: Recognized emotional state of the user

[0768] Specific operation: The smartphone's camera and microphone capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[0769] Step 8: Presentation of results

[0770] Server: Organizes the calculated percentage of fault and compensation amount and sends them to the user in an easy-to-understand format.

[0771] Input: Calculated percentage of fault and amount of damages

[0772] Output: Organized result data

[0773] What it does: The server organizes the results and formats them appropriately for presentation to the user.

[0774] Terminal: Displays the results from the server to the user, providing any necessary information or links to additional resources (such as an FAQ page).

[0775] Input: Organized result data

[0776] Output: The results displayed to the user and links to related information

[0777] Specific operation: The smartphone displays the results and their breakdown, as well as a response based on the user's emotions (such as a leaflet or a link to an FAQ page).

[0778] The above are the specific processing steps and operations of this system. This process has the effect of improving the accuracy of input information, quickly calculating the percentage of fault, and taking user emotions into consideration.

[0779] (Application example 2)

[0780] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0781] When a car accident occurs, both the victim and the at-fault party want a quick and accurate judgment on compensation, but the process is complicated and often causes great stress for users. In addition, there is a lack of means to provide appropriate emotional support to the driver and passengers during the accident process, which can easily increase the psychological burden.

[0782] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a past case database using the standardized and preprocessed accident information, means for calculating a fault ratio based on the case information, means for calculating a compensation amount according to a standard, means for presenting the calculated fault ratio and compensation amount to the user, means for collecting accident data using an in-vehicle sensor, means for standardizing and analyzing the collected data in real time, and means for recognizing the user's emotions and adjusting the response. This enables a quick and accurate determination of compensation and reduces the psychological burden by providing support that takes the user's emotions into consideration.

[0783] The "means for receiving input accident information" refers to a device or system that receives information about accidents sent from a user or an autonomous vehicle.

[0784] "Means for standardizing and preprocessing received accident information" refers to devices or systems that organize received accident information into a unified format and process it into a form suitable for analysis.

[0785] "Means for reading and analyzing relevant case information from a database of past case law" refers to a device or system that searches stored past case law data, obtains case law information corresponding to the input accident information, and analyzes it.

[0786] The "means for calculating the degree of fault" is a device or system that calculates the degree of fault in an accident based on the acquired case law information.

[0787] A "means for calculating the amount of compensation according to a standard" is a device or system that calculates the amount of compensation to be paid by the victim and the perpetrator based on the calculated degree of fault.

[0788] The "means for presenting the calculated fault ratio and compensation amount to the user" refers to a device or system that presents the calculation results to the user visually or audibly in an easy-to-understand manner.

[0789] "Means for collecting accident data using in-vehicle sensors" refers to devices and systems that use various sensors installed in autonomous vehicles to collect situational data when an accident occurs.

[0790] "Means for standardizing and analyzing collected data in real time" refers to devices and systems that instantly standardize data obtained from sensors inside the vehicle and quickly perform analysis work.

[0791] The "means for recognizing the user's emotions and adjusting the response" refers to a device or system that recognizes the user's emotional state from their facial expressions and voice, and provides an appropriate response accordingly.

[0792] The system of the present invention is intended to be installed in an autonomous vehicle. When a traffic accident occurs, the system analyzes the details of the accident quickly and accurately, determines compensation for damages, and provides emotional support to the user.

[0793] System configuration

[0794] Hardware Configuration

[0795] 1. Sensor equipment: Vehicle sensors such as cameras, LiDAR, and GPS.

[0796] 2. User input terminals: displays and voice recognition systems inside autonomous vehicles.

[0797] 3. Server: A central processing unit that analyzes data.

[0798] 4. Database: External storage for storing past case data and damage compensation standards.

[0799] Software Configuration

[0800] 1. Data standardization and preprocessing module: Software that standardizes received accident data and prepares it in a form suitable for analysis.

[0801] 2. Natural Language Processing Module: NLP library for parsing and structuring text data.

[0802] 3. AI analysis module: A generative AI model that calculates the degree of fault and compensation amount based on accident data and case law data.

[0803] 4. Emotion recognition module: Software that analyzes the user's facial expressions and voice to recognize emotions.

[0804] Program processing explanation

[0805] When an accident occurs, the server processes data in the following manner.

[0806] Receiving and standardizing accident data

[0807] The server receives real-time accident data collected from autonomous vehicles through sensors. The data is then converted into a standardized format by the data standardization and preprocessing module, making it suitable for analysis. This standardization ensures consistency in date and time formats and address notation, improving analysis accuracy.

[0808] Analysis of case law information

[0809] The server inputs the standardized accident data into a natural language processing module, which then structures the data from past legal cases. The structured data is then analyzed by an AI analysis module, which calculates the percentage of fault and the amount of compensation. Specifically, the standard percentage of fault (80:20) in a rear-end collision, for example, is applied.

[0810] Emotion Recognition and Response

[0811] The user's emotional state is captured in real time using the in-car camera and microphone and analyzed by the emotion recognition module. The analyzed emotional data is sent to the server, and the system responds taking the user's psychological state into consideration. For example, if the user is feeling stressed or anxious, the system may respond by displaying a reassuring message on the display or playing relaxing music through the in-car speakers.

[0812] Presentation of results

[0813] The server then sends the analyzed fault percentage and compensation amount to the user's input terminal and presents it to the user in an easy-to-understand format, allowing the user to quickly and accurately determine the amount of compensation, which is useful for dealing with the situation after the accident.

[0814] Specific examples

[0815] For example, if a user has an accident and shows an anxious expression, the generative AI model will generate the following prompt sentence and present it to the user.

[0816] Example prompt sentence:

[0817] "An accident has occurred. Please remain calm. We will now analyze the situation and inform you of the details of compensation."

[0818] "I'll play some relaxing music so you don't have to worry."

[0819] In this way, the system is expected to improve the overall user experience by enabling faster and more accurate accident response in autonomous vehicles and providing support that takes into account the user's emotions.

[0820] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0821] Step 1:

[0822] The server receives accident data from the vehicle's sensor devices, including camera footage, LiDAR data, GPS location information, etc. It collects these input data in real time for further processing.

[0823] Step 2:

[0824] The server converts the received accident data into a unified format using the data standardization and preprocessing module. For example, it standardizes date and time formats and address notations, and corrects data inconsistencies. The standardized data is then formatted for analysis and moves on to the next step.

[0825] Step 3:

[0826] The server inputs the standardized accident data into a natural language processing module, reads relevant case information from a database of past cases, and structures it. Specifically, it analyzes the text data and extracts information on the accident situation and the degree of fault. This structured data is then passed to the AI ​​analysis module.

[0827] Step 4:

[0828] The server uses an AI analysis module to compare structured case data with accident data and calculate the fault ratio and compensation amount. For example, in the case of a rear-end collision, the fault ratio is calculated as 80:20. This calculation provides a basis for compensation amount.

[0829] Step 5:

[0830] The server sends the calculated percentage of fault and compensation amount to the user's input terminal for presentation to the user. The user can check this information through the in-car display or voice guidance system. The presented information includes a breakdown of the compensation amount and the legal precedents that serve as the basis.

[0831] Step 6:

[0832] The emotion recognition module uses the in-car camera and microphone to analyze the user's facial expressions and tone of voice to recognize their emotional state. For example, if the user has an anxious expression, the module analyzes the emotional data and sends it to the server.

[0833] Step 7:

[0834] The server adjusts the response and display method based on the user's emotional state. For example, if the server recognizes that the user is feeling stressed or anxious, it will display a reassuring message on the display and play relaxing music through the car speakers.

[0835] Step 8:

[0836] The user can check the final amount of compensation and the percentage of fault and decide on the next steps. Based on this information, it is expected that contacting insurance companies and legal procedures will proceed smoothly.

[0837] Through the above steps, the system of the present invention realizes quick and accurate accident analysis and damage compensation determination, and furthermore, it reduces the psychological burden by supporting the user's emotions.

[0838] 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.

[0839] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0840] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0841] [Third embodiment]

[0842] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0843] 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.

[0844] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0845] 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.

[0846] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0847] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0848] 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.

[0849] 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.

[0850] 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 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.

[0851] 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.

[0852] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0853] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0854] The system of the present invention is designed to quickly and accurately automate compensation for damages in automobile accidents, and includes a process for inputting details of a traffic accident and calculating the percentage of fault and the amount of compensation. Specific embodiments and the flow of program processing are described below.

[0855] System configuration

[0856] The system mainly consists of the following components:

[0857] 1. User input terminal: A device with an interface for entering details of a traffic accident. This can be a computer, smartphone, tablet, etc.

[0858] 2. Server: This is the core device that receives data sent by users and performs various processes, such as standardizing data, reading legal precedent information, and calculating the degree of fault and the amount of compensation.

[0859] 3. Database: A system that stores past case data and compensation standards.

[0860] System Operation

[0861] Below we explain the process from entering accident information to presenting the final amount of compensation.

[0862] User Input Phase

[0863] User: Enters detailed information about the traffic accident into the input terminal, such as the date and time of the accident, location, parties involved (victim, perpetrator), accident circumstances (rear-end collision, side collision, etc.), and specific details of the damage (vehicle damage, need for medical treatment, etc.).

[0864] Terminal: Checks the input and sends the data to the server based on the format.

[0865] Data reception and preprocessing phase

[0866] Server: Receives the accident information sent by the user.

[0867] Server: Standardizes incoming data and ensures consistency in format and content, for example, standardizing date and time formats and address notation.

[0868] Server: Data preprocessing includes imputing missing values ​​and detecting and correcting input errors.

[0869] Case law information loading phase

[0870] Server: Loads relevant information from case law databases, including damage standards such as the "Red Book" and "Blue Book."

[0871] Server: Uses natural language processing techniques to structure the text data and convert it into a format that can be used by the AI ​​model.

[0872] Fault calculation phase

[0873] Server: Inputs standardized and preprocessed accident information and structured case law information into the AI ​​model.

[0874] Server: The AI ​​model calculates the percentage of fault based on past legal precedent data. For example, by looking at past legal precedents for rear-end collisions, the model may determine that the assailant is 80% at fault and the victim is 20% at fault.

[0875] Damages calculation phase

[0876] Server: Calculates the amount of compensation based on the determined percentage of fault and the damage data entered. Damages such as medical expenses, repair costs, and compensation are calculated according to standards.

[0877] Server: For example, if medical expenses are 500,000 yen, repair costs are 300,000 yen, and compensation is 200,000 yen, the total damages are 1,000,000 yen. The amount of compensation that the perpetrator must pay is 80% of 1,000,000 yen, or 800,000 yen.

[0878] Results presentation phase

[0879] Server: Organizes the calculated percentage of fault and amount of damages and converts them into a format that can be notified to the user.

[0880] Terminal: Displays the results received from the server to the user. For example, the final payment amount of 800,000 yen and its breakdown (medical expenses 400,000 yen, repair expenses 240,000 yen, and compensation 160,000 yen) is displayed to the user.

[0881] Specific examples

[0882] For example, consider a rear-end collision that occurred in Shibuya Ward, Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator), resulting in damage to the rear of Person A's vehicle and injuries requiring medical treatment. User Person A enters the accident information, and the device sends that information to the server. The server processes the received data, loads appropriate case law information, and runs it through an AI model. Person B is determined to be 80% at fault, and the amount of compensation to Person A is calculated to be 800,000 yen. This result is presented to the user.

[0883] This system allows users to automatically calculate the amount of damages quickly and accurately, significantly reducing the effort required for settlement negotiations.

[0884] The processing flow will be explained below.

[0885] Step 1:

[0886] User: Enter detailed information about the traffic accident (e.g., date and time of the accident, location, people involved, accident situation, damage details, etc.).

[0887] Terminal: Converts the input information into the appropriate format and sends it to the server.

[0888] Step 2:

[0889] Server: Receives the accident information sent by the user.

[0890] Server: Checks whether the format and content of the received data are correct.

[0891] Step 3:

[0892] Server: Standardizes the received accident information. For example, standardize the date and time to the "YYYY-MM-DD HH:MM" format and standardize address notation.

[0893] Server: Preprocesses the data, completes missing data, and corrects input errors.

[0894] Step 4:

[0895] Server: Reads case information from the "Red Book" and "Blue Book."

[0896] Server: The read text data is structured using natural language processing technology and converted into a format that can be used by the AI ​​model.

[0897] Step 5:

[0898] Server: Structured case law information and standardized accident information are input into the AI ​​model.

[0899] Server: The AI ​​model refers to past case data and calculates the fault ratio based on the input accident information. For example, in the case of a rear-end collision, the fault ratio is usually 80:20.

[0900] Step 6:

[0901] Server: Calculate the amount of compensation based on the percentage of fault. For example, if the victim's medical expenses are 500,000 yen, repair costs are 300,000 yen, and compensation is 200,000 yen, the total damages will be 1 million yen.

[0902] Server: Calculate the amount of compensation that the at-fault party must pay based on their percentage of fault. In the example above, the amount that must be paid is 800,000 yen, which is 80% of the total damages of 1 million yen, and is the at-fault party's percentage of the total damages.

[0903] Step 7:

[0904] Server: Organizes the calculated percentage of fault and compensation amount and summarizes them in a format that is easy for users to understand.

[0905] Terminal: Displays the results received from the server to the user. For example, the final payment amount of 800,000 yen and its breakdown (medical expenses 400,000 yen, repair expenses 240,000 yen, and compensation 160,000 yen) is displayed.

[0906] Step 8:

[0907] User: Check the displayed results and, if necessary, correct the accident information again or proceed with settlement negotiations.

[0908] Example 1

[0909] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0910] When a traffic accident occurs, it is important to calculate compensation for damages quickly and accurately. However, conventional methods require manual input, calculations, and analysis of legal precedents, which is time-consuming and prone to errors. To solve these problems, a system is needed that automates the process from inputting accident information to calculating compensation amounts, thereby improving accuracy.

[0911] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0912] In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a database of past case law using the standardized and preprocessed accident information, means for calculating the degree of fault based on the case law information, means for calculating the amount of compensation in accordance with a standard, means for presenting the calculated degree of fault and amount of compensation to the user, means for checking the input accident information and transmitting the data in an appropriate format, and means for using natural language processing technology to structure text data and convert it into a format for input to an AI model. This automates the process from inputting accident information to calculating the amount of compensation, making it possible to perform the process quickly and accurately.

[0913] "Accident information" refers to detailed data including the date and time of the traffic accident, the location of the accident, information about those involved (such as the names and contact information of the victim and perpetrator), the circumstances of the accident (such as a rear-end collision or a side collision), and the damages (such as damage to the vehicle and the need for medical treatment).

[0914] The "receiving means" refers to a hardware or software element that has the function of accurately acquiring input from a user or data from an external system.

[0915] "Standardization and preprocessing means" refers to processes that standardize the format of received data, fill in inconsistencies and missing values, and prepare the data for analysis.

[0916] "Case law information" refers to data on the results of past trials and settlements regarding similar traffic accidents, as well as the percentage of fault and amount of compensation calculated at those times.

[0917] "Natural language processing technology" is a technology that allows computers to understand and analyze the language that humans use on a daily basis, and is a technology that structures text data and performs semantic analysis.

[0918] "Fault ratio" is a number that indicates the percentage of responsibility that each party should bear for damages caused in a traffic accident.

[0919] "Compensation" refers to the amount that the at-fault party must pay for the damages suffered by the victim in a traffic accident, including medical expenses, repair costs, and compensation.

[0920] The "presentation means" refers to hardware or software elements that have the function of displaying or notifying the calculated results to users and other related parties in an easy-to-understand manner.

[0921] An "AI model" is a collection of algorithms that use machine learning and deep learning to analyze data and automatically perform tasks such as prediction and classification.

[0922] "Structuring technology" is a technology that analyzes unstructured data and converts it into a format that can be used in databases and analysis systems.

[0923] The present invention provides a system for automatically calculating compensation for damages after a traffic accident, quickly and accurately. Specific embodiments of this system are described below.

[0924] System configuration

[0925] This system consists of a user input terminal, a server, and a database.

[0926] User Input Device: The device used to enter details of a traffic accident, such as a computer, smartphone, or tablet.

[0927] Server: This is the core device that analyzes the received data and calculates the percentage of fault and the amount of compensation. It also performs data standardization, preprocessing, reading case law information, and various calculations.

[0928] Database: A system that stores past case data and damages standards. The case information database includes damages standards such as the "Red Book" and the "Blue Book."

[0929] System Operation

[0930] The system processes traffic accident data using the following hardware and software:

[0931] 1. When a traffic accident occurs, the user uses a smartphone or computer to input accident information, including the date and time of the accident, the location of the accident, information about the people involved, the accident situation, and details of the damage.

[0932] 2. The terminal checks the entered accident information, converts it into the appropriate format, and sends it to the server. For example, it standardizes the date and time format and address notation, and checks for and corrects any inconsistencies or missing entries.

[0933] 3. The server standardizes and preprocesses the received accident information. Standardization includes standardizing date and time formats and address notation, and preprocessing includes filling in missing values ​​and detecting and correcting input errors.

[0934] 4. The server reads the relevant case information from the case information database, analyzes and structures the text data using natural language processing technology, and then segments and tags the text data before storing it in the database.

[0935] 5. The server uses the standardized and preprocessed accident information and structured case law information to calculate the percentage of fault using an AI model. It references past case law data and calculates based on the percentage of fault in similar accidents.

[0936] 6. The server calculates the amount of compensation based on the percentage of fault and the damage data entered by the user. The damages include medical expenses, repair costs, compensation, etc. Based on these, the total amount of damages is calculated and the amount of compensation according to the percentage of fault is determined.

[0937] 7. The server organizes the calculated percentage of fault and compensation amount and converts it into a format that can be notified to the user. The results include a breakdown of the compensation amount (medical expenses, repair costs, compensation, etc.).

[0938] 8. The terminal displays the results received from the server to the user. For example, it may present the results in the form of "Final payment amount: 800,000 yen (medical expenses 400,000 yen, repair expenses 240,000 yen, compensation 160,000 yen)."

[0939] Specific examples

[0940] For example, consider a case involving a rear-end collision that occurred somewhere in Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator). When Person A's vehicle rear end was damaged and he suffered injuries requiring medical treatment, User A inputs the accident information. The input information is sent to the server, which processes the information. It reads relevant case law information and uses an AI model to determine the fault ratio as 80:20, calculating the amount of damages as 800,000 yen. The results are then presented to the user.

[0941] Prompt Sentence Examples

[0942] "Information on victim A and assailant B has been entered for a rear-end collision that occurred in a certain location in Tokyo on October 1, 2023. The damage involved damage to the rear of the vehicle and the need for medical treatment. Please calculate the percentage of fault and the amount of compensation based on past legal precedents."

[0943] This system makes it possible to quickly and accurately calculate compensation for damages caused by traffic accidents, significantly reducing the burden on users.

[0944] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0945] System processing flow

[0946] Step 1: Enter accident information

[0947] The user inputs details of the traffic accident using a smartphone or computer, including the date and time of the accident, the location of the accident, information about the parties involved (such as the names and contact information of the victim and at-fault parties), the circumstances of the accident (such as rear-end collision or side-impact collision), and details of damage (such as vehicle damage and medical needs).The system records the accident circumstances based on the input data and proceeds to the next step.

[0948] Step 2: Send data

[0949] The terminal checks the information entered by the user and converts it into the appropriate format. Specifically, it standardizes the date and time format and address notation. The converted data is then sent to the server. By converting the entered data into a format and sending it to the server, data consistency is maintained.

[0950] Step 3: Receiving and Preprocessing Data

[0951] The server receives the accident information sent from the terminal. After receiving it, it standardizes and preprocesses the data. Specifically, it standardizes the date and time format and address notation, fills in missing values, and detects and corrects input errors. This prepares the data in an analyzable format and allows it to proceed to the next step.

[0952] Step 4: Loading case law information

[0953] The server reads relevant case information from a case information database. For example, it extracts cases related to compensation for damages from databases such as the "Red Book" and "Blue Book." It then uses natural language processing technology to analyze and structure the text data, converting it into a format that can be input into the AI ​​model. This converts the case data into a format that can be used for analysis.

[0954] Step 5: Calculating the percentage of fault

[0955] The server uses the standardized and preprocessed accident information and structured case law information to calculate the percentage of fault using an AI model. Specifically, it calculates the percentage of fault corresponding to the input accident information based on past case law data. For example, it refers to past cases related to rear-end collisions to determine the percentage of fault between the assailant and the victim. It then proceeds to the next step based on the calculated percentage of fault.

[0956] Step 6: Calculating damages

[0957] The server calculates the amount of compensation based on the determined fault ratio and the damage data entered by the user. Specifically, it calculates the total amount of damages using data such as medical expenses, repair costs, and compensation, and then calculates the amount of compensation based on the fault ratio. For example, if the total amount of damages is 1 million yen and the fault ratio is 80:20, the amount of compensation that the perpetrator must pay is 800,000 yen. The process proceeds to the next step based on the calculated amount of compensation.

[0958] Step 7: Organize and communicate results

[0959] The server organizes the calculated percentage of fault and compensation amount and converts them into a format that can be notified to the user. Specifically, it formats the results, including a breakdown of the compensation amount (e.g., medical expenses, repair costs, compensation, etc.). The formatted data is sent to proceed to the next step.

[0960] Step 8: Viewing the results

[0961] The terminal displays the results received from the server to the user. Specifically, it presents the results in the form of "Final payment amount: 800,000 yen (medical expenses 400,000 yen, repair expenses 240,000 yen, compensation 160,000 yen)." This allows the user to quickly and accurately check the amount of compensation.

[0962] (Application example 1)

[0963] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0964] Conventional methods for calculating compensation for traffic accidents require human intervention, which requires a significant amount of time and effort. Furthermore, there is a problem in that accident analysis and compensation calculation for autonomous vehicles cannot be performed in real time. This can lead to delays in settlement negotiations and in some cases to an inability to present accurate compensation amounts. Furthermore, the time required for rapid accident analysis and notification of results increases the mental and financial burden on both the victim and the at-fault party.

[0965] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0966] In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a past case database using the standardized and preprocessed accident information, means for calculating a fault ratio based on the case information, means for calculating a compensation amount according to a standard, means for presenting the calculated fault ratio and compensation amount to a user, means for recording sensor information from the autonomous vehicle and collecting accident data, and means for transmitting the accident data to the server via the vehicle's communication module. This makes it possible, when an autonomous vehicle is involved in a traffic accident, to quickly record the accident situation and quickly and accurately calculate a fault ratio and compensation amount.

[0967] "Accident information" is detailed data about traffic accidents, including the date and time of the accident, location, people involved, accident circumstances, and damage details.

[0968] The "receiving means" refers to a method and device for capturing the input accident information into the server.

[0969] "Standardization and preprocessing means" refers to methods and devices for standardizing received incident information into a consistent format and correcting missing or incorrect data.

[0970] The "Case Law Database" is a data store that compiles case law and compensation standards related to past traffic accidents.

[0971] The "means for reading and analyzing" refers to a method and apparatus for extracting and analyzing relevant case information from a case database based on the standardized and preprocessed accident information.

[0972] The "means for calculating the degree of fault" refers to a method and device for calculating the degree of fault of each party based on legal precedent information.

[0973] The "means for calculating the amount of compensation" refers to a method and device for calculating the amount of compensation for each party based on the determined degree of fault and damage data.

[0974] The "means for presenting to the user" refers to a method and device for displaying the calculated fault ratio and compensation amount to the user.

[0975] "Means for recording sensor information" refers to methods and apparatus for storing accident-related data obtained from sensors installed on an autonomous vehicle.

[0976] A "communication module" is a communication device used to transfer data from a vehicle to a server.

[0977] This invention is a system that quickly and accurately collects accident information when an autonomous vehicle is involved in a traffic accident, and automatically calculates the percentage of fault and the amount of compensation. Specific embodiments of this system are described below.

[0978] System Overview

[0979] This system consists of a user input terminal, a server, a database, various sensors in the autonomous vehicle, and a communication module. These components work together as follows:

[0980] 1. User Input Terminal

[0981] This is a device that allows users to input detailed information about traffic accidents. The device can be a smartphone, tablet, or vehicle display system.

[0982] 2. Server

[0983] The server is the main device responsible for receiving accident information, standardizing and preprocessing it, reading case law information, calculating the degree of fault and compensation amounts, and presenting the results. In particular, it uses natural language processing technologies (such as NLTK and spaCy) and AI models (such as TensorFlow and PyTorch) to standardize and preprocess the received accident information and extract relevant information from the case law database.

[0984] 3. Database

[0985] The database holds information on past legal precedents and damage compensation standards, from which the server extracts the necessary information and uses it for analytical processing.

[0986] 4. Sensors and communication modules for autonomous vehicles

[0987] Autonomous vehicles are equipped with front and rear cameras and various sensors that record information in real time when an accident occurs. This data is sent to a server via the vehicle's communication module, using 5G communication technology.

[0988] System operation flow

[0989] The operation of the system consists of the following major steps:

[0990] 1. Collecting accident information

[0991] Sensors in autonomous vehicles record accident information in real time, acquiring information such as the location and time of the accident, and information on those involved. This information is then sent to a server via the vehicle's communication module.

[0992] 2. Receiving and preprocessing accident information

[0993] The server standardizes the received accident information, filling in missing values ​​and checking for format consistency, for example, standardizing date and time formats and address notation.

[0994] 3. Extraction and structuring of case law information

[0995] The server reads the relevant case information from the case database and structures it using natural language processing technology, converting it into a format that can be used by the AI ​​model.

[0996] 4. Calculation of the percentage of fault and the amount of compensation

[0997] Standardized and preprocessed accident information and structured case law information are input into the AI ​​model to calculate the degree of fault and the amount of compensation.

[0998] 5. Presentation of results

[0999] The calculated percentage of fault and amount of compensation for damages are sent from the server to the user input terminal in a format that can be confirmed by the user, and are displayed.

[1000] Specific examples

[1001] For example, in the case of a rear-end collision that occurred in Tokyo on October 1, 2023, the victim was Person A and the assailant was Person B, and Person A's rear end was damaged, resulting in injuries requiring medical treatment. Accident information acquired by the autonomous vehicle is sent to a server in real time, where it is standardized and preprocessed. The server refers to a database of past legal precedents, extracts appropriate data, and inputs it into an AI model to calculate the percentage of fault between the victim and the assailant, and calculates the amount of compensation. The results are displayed on the autonomous vehicle's display system and on the user's smartphone.

[1002] Prompt Sentence Examples

[1003] "Please enter the details of a rear-end collision that occurred in Tokyo on October 1, 2023. The victim is Person A and the perpetrator is Person B. The rear of Person A's vehicle is damaged and requires medical treatment."

[1004] This system allows for quick and accurate calculation of compensation in traffic accidents, significantly reducing the effort required for settlement negotiations.

[1005] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1006] Step 1:

[1007] Sensors in autonomous vehicles record accident information in real time.

[1008] Input: Real-time data from sensors and cameras (location of the accident, speed, impact angle, etc.).

[1009] Output: Recorded accident data.

[1010] Specific operation: Various sensors and cameras installed in the autonomous vehicle detect the moment of an accident and store the data in internal memory. These sensors include acceleration sensors, gyro sensors, and video cameras.

[1011] Step 2:

[1012] The accident data is transmitted to a server via the vehicle's communication module.

[1013] Input: Accident data recorded in step 1.

[1014] Output: The incident data sent to the server.

[1015] How it works: Recorded accident data is sent to a server in real time via a 5G communication module installed in the vehicle, which requires high data transfer speeds and low latency.

[1016] Step 3:

[1017] The server normalizes and pre-processes the received accident data.

[1018] Input: Accident data sent to the server.

[1019] Output: Standardized and preprocessed accident data.

[1020] Specific operation: The server standardizes the date and time format and address notation of the received accident data, and performs missing data completion and correction of input errors to maintain consistency. This process is often performed using the Python library Pandas.

[1021] Step 4:

[1022] The server reads the relevant case information from the case database and structures it using natural language processing technology.

[1023] Input: Standardized and preprocessed accident data.

[1024] Output: Structured case law information.

[1025] Specific operation: The server searches and extracts relevant information from case law databases such as the "Red Book" and "Blue Book," and structures the text data using natural language processing technology (such as NLTK or spaCy).

[1026] Step 5:

[1027] The server inputs standardized accident data and structured case law information into an AI model to calculate the degree of fault.

[1028] Input: Standardized accident data and structured case law information.

[1029] Output: Calculated fault percentage.

[1030] Specific operation: Standardized accident data and structured case law information are input into an AI model (using TensorFlow and PyTorch), and the fault ratio of each party is calculated based on past case law data.

[1031] Step 6:

[1032] The server calculates the amount of compensation based on the determined fault percentage and damage data.

[1033] Input: Calculated fault percentage and damage data.

[1034] Output: Calculated damages.

[1035] Specific operation: The server calculates the total amount of damages based on the percentage of fault and the input damage data (medical expenses, repair costs, compensation, etc.), and then calculates the amount of compensation that the at-fault party must pay. Specifically, it multiplies and adds the amounts.

[1036] Step 7:

[1037] The server presents the calculated percentage of fault and the amount of compensation to the user.

[1038] Input: Calculated percentage of fault and damages.

[1039] Output: Percent fault and damages in a user-viewable format.

[1040] Specific operation: The server converts the calculation results into an easy-to-understand format and sends them to the user's input device (smartphone or vehicle display system), allowing both the victim and the perpetrator to check the results in real time.

[1041] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1042] The system of the present invention quickly and accurately determines compensation for damages in automobile accidents and further improves the user experience by recognizing the user's emotions. Specific embodiments of the present invention and the flow of program processing are described below.

[1043] System configuration

[1044] The system mainly consists of the following components:

[1045] 1. User input terminal: A device with an interface for entering details of a traffic accident. This can be a computer, smartphone, tablet, etc.

[1046] 2. Server: This is the core device that receives data sent by users and performs various processes, such as standardizing data, reading legal precedent information, and calculating the degree of fault and the amount of compensation.

[1047] 3. Database: A system that stores past case data and compensation standards.

[1048] 4. Emotion engine: A device or software that recognizes emotions based on user input and reactions and adjusts the system's response.

[1049] System Operation

[1050] Below we explain the process from inputting accident information to presenting the final compensation amount and optimizing it through emotion recognition.

[1051] User Input Phase

[1052] User: Enters detailed information about the traffic accident into the input terminal, such as the date and time of the accident, location, parties involved (victim, perpetrator), accident circumstances (rear-end collision, side collision, etc.), and specific details of the damage (vehicle damage, need for medical treatment, etc.).

[1053] Terminal: Checks the input and sends the data to the server based on the format.

[1054] Data reception and preprocessing phase

[1055] Server: Receives the accident information sent by the user.

[1056] Server: Checks whether the format and content of the received data are correct.

[1057] Server: Standardizes the received accident information and checks for consistency in format and content, for example, standardizing date and time formats and address notation.

[1058] Case law information loading phase

[1059] Server: Reads case information from the "Red Book" and "Blue Book."

[1060] Server: The read text data is structured using natural language processing technology and converted into a format that can be used by the AI ​​model.

[1061] Fault calculation phase

[1062] Server: Structured case law information and standardized accident information are input into the AI ​​model.

[1063] Server: The AI ​​model refers to past case data and calculates the fault ratio based on the input accident information. For example, in the case of a rear-end collision, the fault ratio is usually 80:20.

[1064] Damages calculation phase

[1065] Server: Calculate the amount of compensation based on the percentage of fault. For example, if the victim's medical expenses are 500,000 yen, repair costs are 300,000 yen, and compensation is 200,000 yen, the total damages will be 1 million yen.

[1066] Server: Calculate the amount of compensation that the at-fault party must pay based on their percentage of fault. In the example above, the amount that must be paid is 800,000 yen, which is 80% of the total damages of 1 million yen, and is the at-fault party's percentage of the total damages.

[1067] Emotion Recognition Phase

[1068] Terminal: The emotion engine analyzes the user's reactions when inputting information, facial expressions, tone of voice, etc.

[1069] Emotion engine: The emotion engine recognizes emotions based on the user's input information and sends the emotional state to the server.

[1070] Server: Adjusts the content, expression, and presentation of responses based on the user's recognized emotions. For example, if the system recognizes that the user is feeling stressed or anxious, it will provide a more polite and reassuring response.

[1071] Results presentation phase

[1072] Server: Organizes the calculated percentage of fault and compensation amount and summarizes them in a format that is easy for users to understand.

[1073] Terminal: Displays the results received from the server to the user. For example, it displays the final payment amount of 800,000 yen and its breakdown (400,000 yen for medical expenses, 240,000 yen for repairs, and 160,000 yen for compensation). In addition, it can display a leaflet or a link to an FAQ page that reflects the user's emotions.

[1074] Specific examples

[1075] For example, consider a rear-end collision that occurred in Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator), resulting in damage to the rear of Person A's vehicle and injuries requiring medical treatment. User A enters accident information, and the device sends that information to the server. The server processes the received data, loads appropriate case law information, and runs it through an AI model. Person B is determined to be 80% at fault, and the amount of compensation to Person A is calculated to be 800,000 yen. This result is presented to the user by the emotion engine, taking into account the user's emotions.

[1076] In this way, users can quickly and accurately calculate the amount of compensation automatically, and the system takes the user's feelings into consideration when presenting optimal information, significantly reducing the effort and stress involved in settlement negotiations.

[1077] The processing flow will be explained below.

[1078] Step 1:

[1079] User: Enter detailed information about the traffic accident, such as the date and time of the accident (e.g., 14:30, October 1, 2023), the location of the accident (an intersection in Tokyo), the parties involved (victim A, assailant B), the accident circumstances (rear-end collision while waiting at a traffic light), and the specific details of the damage (damage to the rear of the vehicle, injuries requiring medical treatment).

[1080] Step 2:

[1081] Terminal: Converts the input information into the appropriate format and sends it to the server, including all the details of the accident.

[1082] Step 3:

[1083] Server: Receives the accident information sent by the user. Immediately after receiving the information, the server checks whether there are any errors in the data format or content. For example, it checks whether the date and time format is standardized to "YYYY-MM-DD HH:MM" and whether the address is written correctly.

[1084] Step 4:

[1085] Server: Standardizes the received accident information. Standardization includes standardizing date and time formats, address notation, and name notation. This ensures data consistency.

[1086] Step 5:

[1087] Server: Reads case law information from the "Red Book" and "Blue Book" databases. Structures the text data using natural language processing technology to analyze past case law data and compensation standards.

[1088] Step 6:

[1089] Server: Structured case law information and standardized accident information are input into the AI ​​model. The AI ​​model references past case law data and calculates the fault ratio based on the input accident information. For example, it automatically determines the fault ratio for a rear-end collision as 80:20.

[1090] Step 7:

[1091] Server: Calculates the amount of compensation based on the percentage of fault. Specifically, the damages such as medical expenses (e.g., 500,000 yen), repair costs (e.g., 300,000 yen), and compensation (e.g., 200,000 yen) are evaluated according to a standard, and the total amount of damages (e.g., 1 million yen) is calculated.

[1092] Server: Calculate the amount of compensation that the at-fault party must pay based on the percentage of fault. For example, if the total damages are 1 million yen, the amount that the at-fault party must pay is 800,000 yen, which is 80% of the total damages.

[1093] Step 8:

[1094] Device: The emotion engine analyzes the user's reactions, facial expressions, and tone of voice in real time when inputting information.

[1095] Emotion Engine: The emotion engine recognizes the user's emotional data (e.g., text typing speed, changes in tone of voice, subtle changes in facial expressions) and determines their emotional state.

[1096] Step 9:

[1097] Server: The emotion engine takes into account the emotional data it recognizes and adjusts the content and expression of the response. For example, if it senses that the user is feeling stressed, it will provide a more polite and reassuring response. If the user is feeling anxious, it will provide concise and prompt information.

[1098] Step 10:

[1099] Server: Converts the organized results of fault ratio and compensation amount into data to be presented to the user along with the emotion recognition results.

[1100] Step 11:

[1101] Terminal: Receives the final result data from the server and displays it to the user. The information presented to the user includes the final payment amount (e.g., 800,000 yen) and its breakdown (400,000 yen for medical expenses, 240,000 yen for repairs, and 160,000 yen for compensation), as well as recommendations for settlement negotiations. The display content and format are also adjusted according to the user's emotions, as recognized by the emotion engine. For example, if a user is feeling stressed, it will also display relief measures and links to specialist consultations.

[1102] Specific examples

[1103] For example, in the case of a rear-end collision that occurred at an intersection in Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator), the rear of Person A's vehicle was damaged and he suffered injuries requiring medical treatment. User A enters the details of the accident, and the device sends the information to the server. The server standardizes and preprocesses the data, reads case law information, and calculates the degree of fault and the amount of compensation using an AI model. At this time, the emotion engine recognizes Person A's emotions in real time, and the server responds accordingly. The amount of compensation ultimately presented to Person A is 800,000 yen, and advice that takes emotions into consideration is also provided along with a detailed breakdown of the amount.

[1104] This system allows users to quickly and accurately calculate compensation amounts automatically, and emotion recognition enables user-friendly information presentation, significantly reducing the effort required for settlement negotiations and reducing stress for users.

[1105] Example 2

[1106] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1107] When a traffic accident occurs, it is necessary to quickly and accurately determine compensation for damages, but it often takes time to refer to past case law information. Furthermore, if there is an error in the information entered by the user, the processing may be further delayed. Furthermore, a system that ignores the user's emotional state may impair the user experience and increase stress. It is necessary to solve these issues, realize fast and accurate calculation of compensation amounts, and improve the user experience.

[1108] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1109] In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a database of past cases using the standardized and preprocessed accident information, means for calculating a fault ratio based on the case information, means for calculating a compensation amount according to a standard, means for recognizing the user's emotional state and adjusting the response content, and means for presenting the calculated fault ratio and compensation amount to the user. This enables a quick and accurate calculation of compensation amount, and also enables the user experience to be improved by providing a response that takes the user's emotional state into consideration.

[1110] (Definitions of important words)

[1111] "Accident information" is detailed data related to a traffic accident, including the date and time of the accident, the location, the people involved, the accident situation, and specific details of the damage.

[1112] "Standardization" is the process of standardizing the format and content of received data to ensure consistency.

[1113] "Preprocessing" is the process of checking data for errors or omissions and converting it into an appropriate format.

[1114] The "case law database" is a database that stores case law information related to past traffic accidents.

[1115] "Analysis" is the process of analyzing data and extracting and utilizing useful information from it.

[1116] "Fault ratio" is a number that indicates the percentage of fault of each party involved in a traffic accident.

[1117] "Amount of damages" refers to the amount to be paid for damages caused by a traffic accident.

[1118] "Natural language processing technology" is a technology that analyzes text data and converts it into a format that machines can understand.

[1119] "Emotional state" refers to the user's mental and emotional state.

[1120] "Adjusting the response content" refers to the process of changing the system's response and display content according to the user's emotional state.

[1121] The above are definitions of important terms contained in the claims.

[1122] MODE FOR CARRYING OUT THE INVENTION

[1123] This system quickly and accurately determines compensation for damages in traffic accidents, and provides an optimal user experience while recognizing the user's emotions. This system consists of the following main components:

[1124] User Input Terminal

[1125] Terminal: A device with an interface for inputting detailed information about a traffic accident. Specifically, it includes a computer, smartphone, tablet, etc. The user uses this terminal to input accident information.

[1126] Example: A user may use a smartphone to input the date and time of an accident, its location, the people involved, the circumstances of the accident, and specific details of the damage.

[1127] server

[1128] Server: A core device that receives data sent by users and performs various processes. The server performs multiple processes such as data standardization, reading past case law information, and calculating the degree of fault and compensation amount.

[1129] Example: A server receives user input and standardizes date and time formats or address notation to ensure uniform formatting.

[1130] Database

[1131] Database: A system that stores past case data and damage compensation standards, allowing the server to load and analyze case information.

[1132] Example: The server reads case law information on rear-end collisions from the "Red Book," then uses natural language processing technology to structure the data and convert it into a format that can be used by an AI model.

[1133] Emotion Engine

[1134] Emotion engine: A device or software that recognizes emotions based on user input and reactions and adjusts the system's response.

[1135] Example: When a user types something, the camera and microphone on the smartphone are used to analyze emotions from facial expressions and tone of voice.

[1136] Specific procedures for determining damages

[1137] The server receives the input accident information, standardizes and preprocesses it, and then reads case law information from the database. It then uses this information to calculate the degree of fault and calculate the amount of compensation. It also recognizes the user's emotional state and adjusts the response accordingly.

[1138] Prompt Sentence Examples

[1139] When entering details of a traffic accident, specific prompts might include:

[1140] "Please enter details about a rear-end collision that occurred in Tokyo on October 1, 2023. For example, please enter the date and time of the accident, location, parties involved (victim, perpetrator), accident circumstances (rear-end collision, side collision, etc.), and specific details of the damage (vehicle damage, need for medical treatment, etc.)."

[1141] This system enables quick and accurate damage compensation determination and provides optimal responses that take the user's feelings into consideration, thereby reducing the burden on the user and providing a more comfortable user experience.

[1142] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1143] Step 1: User Input Phase

[1144] User: Enters detailed information about the traffic accident into the input terminal. Specifically, the user enters the date and time of the accident, the location, the parties involved (victim, perpetrator), the accident situation (rear-end collision, side collision, etc.), and the specific details of the damage. This is done using the input form displayed on the terminal.

[1145] Input: Accident details (date, time, location, people involved, situation, damage details)

[1146] Output: Initial data entered into the terminal

[1147] Specific actions: The user enters details into the smartphone interface.

[1148] Step 2: Data transmission phase

[1149] Terminal: Validates input and sends data to the server based on format. Format validation includes consistency of date and time formats and address notation.

[1150] Input: Initial data entered into the terminal

[1151] Output: Standardized data sent to the server

[1152] Specific operation: The smartphone sends the entered data to the server.

[1153] Step 3: Data reception and preprocessing phase

[1154] Server: Receives accident information sent by users, checks for errors in format and content, and then standardizes the received data to ensure consistency in format and content.

[1155] Input: Data sent to the server

[1156] Output: Standardized and preprocessed data

[1157] Specific operation: The server checks the format and content of the received data and standardizes it, for example, standardizing the date and time and address notation.

[1158] Step 4: Case law information loading phase

[1159] Server: Reads case information from the "Red Book" and "Blue Book" and structures it using natural language processing techniques, including converting it into a format that can be used by the AI ​​model.

[1160] Input: Standardized accident information

[1161] Output: Structured case law information

[1162] Specific operation: The server extracts text data from the database and analyzes and structures it using natural language processing.

[1163] Step 5: Calculation of fault

[1164] Server: Structured case law information and standardized accident information are input into the AI ​​model, and the fault ratio is calculated based on past case law data.

[1165] Input: Structured case law information, standardized accident information

[1166] Output: Calculated fault ratio

[1167] How it works: The server inputs data into the AI ​​model and calculates the fault ratio. For example, in the case of a rear-end collision, an 80:20 fault ratio is applied.

[1168] Step 6: Calculation of damages

[1169] Server: Calculates the amount of compensation based on the calculated percentage of fault and clearly indicates the breakdown (medical expenses, repair costs, compensation, etc.).

[1170] Input: Fault ratio, detailed damage information (medical expenses, repair costs, compensation, etc.)

[1171] Output: Calculated damages amount and breakdown

[1172] Specific operation: The server calculates the total damage amount and then applies the fault ratio to calculate the amount that the at-fault party must pay. For example, if the total damage amount is 1 million yen and the at-fault party is responsible for 80% of the damage, the amount of compensation will be 800,000 yen.

[1173] Step 7: Emotion Recognition Phase

[1174] Terminal: The emotion engine analyzes the user's reactions when inputting information, facial expressions, tone of voice, etc.

[1175] Input: User reaction data (facial expressions, tone of voice, etc.)

[1176] Output: Recognized emotional state of the user

[1177] Specific operation: The smartphone's camera and microphone capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[1178] Step 8: Presentation of results

[1179] Server: Organizes the calculated percentage of fault and compensation amount and sends them to the user in an easy-to-understand format.

[1180] Input: Calculated percentage of fault and amount of damages

[1181] Output: Organized result data

[1182] What it does: The server organizes the results and formats them appropriately for presentation to the user.

[1183] Terminal: Displays the results from the server to the user, providing any necessary information or links to additional resources (such as an FAQ page).

[1184] Input: Organized result data

[1185] Output: The results displayed to the user and links to related information

[1186] Specific operation: The smartphone displays the results and their breakdown, as well as a response based on the user's emotions (such as a leaflet or a link to an FAQ page).

[1187] The above are the specific processing steps and operations of this system. This process has the effect of improving the accuracy of input information, quickly calculating the percentage of fault, and taking user emotions into consideration.

[1188] (Application example 2)

[1189] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1190] When a car accident occurs, both the victim and the at-fault party want a quick and accurate judgment on compensation, but the process is complicated and often causes great stress for users. In addition, there is a lack of means to provide appropriate emotional support to the driver and passengers during the accident process, which can easily increase the psychological burden.

[1191] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a past case database using the standardized and preprocessed accident information, means for calculating a fault ratio based on the case information, means for calculating a compensation amount according to a standard, means for presenting the calculated fault ratio and compensation amount to the user, means for collecting accident data using an in-vehicle sensor, means for standardizing and analyzing the collected data in real time, and means for recognizing the user's emotions and adjusting the response. This enables a quick and accurate determination of compensation and reduces the psychological burden by providing support that takes the user's emotions into consideration.

[1192] The "means for receiving input accident information" refers to a device or system that receives information about accidents sent from a user or an autonomous vehicle.

[1193] "Means for standardizing and preprocessing received accident information" refers to devices or systems that organize received accident information into a unified format and process it into a form suitable for analysis.

[1194] "Means for reading and analyzing relevant case information from a database of past case law" refers to a device or system that searches stored past case law data, obtains case law information corresponding to the input accident information, and analyzes it.

[1195] The "means for calculating the degree of fault" is a device or system that calculates the degree of fault in an accident based on the acquired case law information.

[1196] A "means for calculating the amount of compensation according to a standard" is a device or system that calculates the amount of compensation to be paid by the victim and the perpetrator based on the calculated degree of fault.

[1197] The "means for presenting the calculated fault ratio and compensation amount to the user" refers to a device or system that presents the calculation results to the user visually or audibly in an easy-to-understand manner.

[1198] "Means for collecting accident data using in-vehicle sensors" refers to devices and systems that use various sensors installed in autonomous vehicles to collect situational data when an accident occurs.

[1199] "Means for standardizing and analyzing collected data in real time" refers to devices and systems that instantly standardize data obtained from sensors inside the vehicle and quickly perform analysis work.

[1200] The "means for recognizing the user's emotions and adjusting the response" refers to a device or system that recognizes the user's emotional state from their facial expressions and voice, and provides an appropriate response accordingly.

[1201] The system of the present invention is intended to be installed in an autonomous vehicle. When a traffic accident occurs, the system analyzes the details of the accident quickly and accurately, determines compensation for damages, and provides emotional support to the user.

[1202] System configuration

[1203] Hardware Configuration

[1204] 1. Sensor equipment: Vehicle sensors such as cameras, LiDAR, and GPS.

[1205] 2. User input terminals: displays and voice recognition systems inside autonomous vehicles.

[1206] 3. Server: A central processing unit that analyzes data.

[1207] 4. Database: External storage for storing past case data and damage compensation standards.

[1208] Software Configuration

[1209] 1. Data standardization and preprocessing module: Software that standardizes received accident data and prepares it in a form suitable for analysis.

[1210] 2. Natural Language Processing Module: NLP library for parsing and structuring text data.

[1211] 3. AI analysis module: A generative AI model that calculates the degree of fault and compensation amount based on accident data and case law data.

[1212] 4. Emotion recognition module: Software that analyzes the user's facial expressions and voice to recognize emotions.

[1213] Program processing explanation

[1214] When an accident occurs, the server processes data in the following manner.

[1215] Receiving and standardizing accident data

[1216] The server receives real-time accident data collected from autonomous vehicles through sensors. The data is then converted into a standardized format by the data standardization and preprocessing module, making it suitable for analysis. This standardization ensures consistency in date and time formats and address notation, improving analysis accuracy.

[1217] Analysis of case law information

[1218] The server inputs the standardized accident data into a natural language processing module, which then structures the data from past legal cases. The structured data is then analyzed by an AI analysis module, which calculates the percentage of fault and the amount of compensation. Specifically, the standard percentage of fault (80:20) in a rear-end collision, for example, is applied.

[1219] Emotion Recognition and Response

[1220] The user's emotional state is captured in real time using the in-car camera and microphone and analyzed by the emotion recognition module. The analyzed emotional data is sent to the server, and the system responds taking the user's psychological state into consideration. For example, if the user is feeling stressed or anxious, the system may respond by displaying a reassuring message on the display or playing relaxing music through the in-car speakers.

[1221] Presentation of results

[1222] The server then sends the analyzed fault percentage and compensation amount to the user's input terminal and presents it to the user in an easy-to-understand format, allowing the user to quickly and accurately determine the amount of compensation, which is useful for dealing with the situation after the accident.

[1223] Specific examples

[1224] For example, if a user has an accident and shows an anxious expression, the generative AI model will generate the following prompt sentence and present it to the user.

[1225] Example prompt sentence:

[1226] "An accident has occurred. Please remain calm. We will now analyze the situation and inform you of the details of compensation."

[1227] "I'll play some relaxing music so you don't have to worry."

[1228] In this way, the system is expected to improve the overall user experience by enabling faster and more accurate accident response in autonomous vehicles and providing support that takes into account the user's emotions.

[1229] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1230] Step 1:

[1231] The server receives accident data from the vehicle's sensor devices, including camera footage, LiDAR data, GPS location information, etc. It collects these input data in real time for further processing.

[1232] Step 2:

[1233] The server converts the received accident data into a unified format using the data standardization and preprocessing module. For example, it standardizes date and time formats and address notations, and corrects data inconsistencies. The standardized data is then formatted for analysis and moves on to the next step.

[1234] Step 3:

[1235] The server inputs the standardized accident data into a natural language processing module, reads relevant case information from a database of past cases, and structures it. Specifically, it analyzes the text data and extracts information on the accident situation and the degree of fault. This structured data is then passed to the AI ​​analysis module.

[1236] Step 4:

[1237] The server uses an AI analysis module to compare structured case data with accident data and calculate the fault ratio and compensation amount. For example, in the case of a rear-end collision, the fault ratio is calculated as 80:20. This calculation provides a basis for compensation amount.

[1238] Step 5:

[1239] The server sends the calculated percentage of fault and compensation amount to the user's input terminal for presentation to the user. The user can check this information through the in-car display or voice guidance system. The presented information includes a breakdown of the compensation amount and the legal precedents that serve as the basis.

[1240] Step 6:

[1241] The emotion recognition module uses the in-car camera and microphone to analyze the user's facial expressions and tone of voice to recognize their emotional state. For example, if the user has an anxious expression, the module analyzes the emotional data and sends it to the server.

[1242] Step 7:

[1243] The server adjusts the response and display method based on the user's emotional state. For example, if the server recognizes that the user is feeling stressed or anxious, it will display a reassuring message on the display and play relaxing music through the car speakers.

[1244] Step 8:

[1245] The user can check the final amount of compensation and the percentage of fault and decide on the next steps. Based on this information, it is expected that contacting insurance companies and legal procedures will proceed smoothly.

[1246] Through the above steps, the system of the present invention realizes quick and accurate accident analysis and damage compensation determination, and furthermore, it reduces the psychological burden by supporting the user's emotions.

[1247] 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.

[1248] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1249] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1250] [Fourth embodiment]

[1251] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1252] 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.

[1253] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1254] 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.

[1255] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1256] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1257] 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.

[1258] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1259] 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.

[1260] 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 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.

[1261] 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.

[1262] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1263] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1264] The system of the present invention is designed to quickly and accurately automate compensation for damages in automobile accidents, and includes a process for inputting details of a traffic accident and calculating the percentage of fault and the amount of compensation. Specific embodiments and the flow of program processing are described below.

[1265] System configuration

[1266] The system mainly consists of the following components:

[1267] 1. User input terminal: A device with an interface for entering details of a traffic accident. This can be a computer, smartphone, tablet, etc.

[1268] 2. Server: This is the core device that receives data sent by users and performs various processes, such as standardizing data, reading legal precedent information, and calculating the degree of fault and the amount of compensation.

[1269] 3. Database: A system that stores past case data and compensation standards.

[1270] System Operation

[1271] Below we explain the process from entering accident information to presenting the final amount of compensation.

[1272] User Input Phase

[1273] User: Enters detailed information about the traffic accident into the input terminal, such as the date and time of the accident, location, parties involved (victim, perpetrator), accident circumstances (rear-end collision, side collision, etc.), and specific details of the damage (vehicle damage, need for medical treatment, etc.).

[1274] Terminal: Checks the input and sends the data to the server based on the format.

[1275] Data reception and preprocessing phase

[1276] Server: Receives the accident information sent by the user.

[1277] Server: Standardizes incoming data and ensures consistency in format and content, for example, standardizing date and time formats and address notation.

[1278] Server: Data preprocessing includes imputing missing values ​​and detecting and correcting input errors.

[1279] Case law information loading phase

[1280] Server: Loads relevant information from case law databases, including damage standards such as the "Red Book" and "Blue Book."

[1281] Server: Uses natural language processing techniques to structure the text data and convert it into a format that can be used by the AI ​​model.

[1282] Fault calculation phase

[1283] Server: Inputs standardized and preprocessed accident information and structured case law information into the AI ​​model.

[1284] Server: The AI ​​model calculates the percentage of fault based on past legal precedent data. For example, by looking at past legal precedents for rear-end collisions, the model may determine that the assailant is 80% at fault and the victim is 20% at fault.

[1285] Damages calculation phase

[1286] Server: Calculates the amount of compensation based on the determined percentage of fault and the damage data entered. Damages such as medical expenses, repair costs, and compensation are calculated according to standards.

[1287] Server: For example, if medical expenses are 500,000 yen, repair costs are 300,000 yen, and compensation is 200,000 yen, the total damages are 1,000,000 yen. The amount of compensation that the perpetrator must pay is 80% of 1,000,000 yen, or 800,000 yen.

[1288] Results presentation phase

[1289] Server: Organizes the calculated percentage of fault and amount of damages and converts them into a format that can be notified to the user.

[1290] Terminal: Displays the results received from the server to the user. For example, the final payment amount of 800,000 yen and its breakdown (medical expenses 400,000 yen, repair expenses 240,000 yen, and compensation 160,000 yen) is displayed to the user.

[1291] Specific examples

[1292] For example, consider a rear-end collision that occurred in Shibuya Ward, Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator), resulting in damage to the rear of Person A's vehicle and injuries requiring medical treatment. User Person A enters the accident information, and the device sends that information to the server. The server processes the received data, loads appropriate case law information, and runs it through an AI model. Person B is determined to be 80% at fault, and the amount of compensation to Person A is calculated to be 800,000 yen. This result is presented to the user.

[1293] This system allows users to automatically calculate the amount of damages quickly and accurately, significantly reducing the effort required for settlement negotiations.

[1294] The processing flow will be explained below.

[1295] Step 1:

[1296] User: Enter detailed information about the traffic accident (e.g., date and time of the accident, location, people involved, accident situation, damage details, etc.).

[1297] Terminal: Converts the input information into the appropriate format and sends it to the server.

[1298] Step 2:

[1299] Server: Receives the accident information sent by the user.

[1300] Server: Checks whether the format and content of the received data are correct.

[1301] Step 3:

[1302] Server: Standardizes the received accident information. For example, standardize the date and time to the "YYYY-MM-DD HH:MM" format and standardize address notation.

[1303] Server: Preprocesses the data, completes missing data, and corrects input errors.

[1304] Step 4:

[1305] Server: Reads case information from the "Red Book" and "Blue Book."

[1306] Server: The read text data is structured using natural language processing technology and converted into a format that can be used by the AI ​​model.

[1307] Step 5:

[1308] Server: Structured case law information and standardized accident information are input into the AI ​​model.

[1309] Server: The AI ​​model refers to past case data and calculates the fault ratio based on the input accident information. For example, in the case of a rear-end collision, the fault ratio is usually 80:20.

[1310] Step 6:

[1311] Server: Calculate the amount of compensation based on the percentage of fault. For example, if the victim's medical expenses are 500,000 yen, repair costs are 300,000 yen, and compensation is 200,000 yen, the total damages will be 1 million yen.

[1312] Server: Calculate the amount of compensation that the at-fault party must pay based on their percentage of fault. In the example above, the amount that must be paid is 800,000 yen, which is 80% of the total damages of 1 million yen, and is the at-fault party's percentage of the total damages.

[1313] Step 7:

[1314] Server: Organizes the calculated percentage of fault and compensation amount and summarizes them in a format that is easy for users to understand.

[1315] Terminal: Displays the results received from the server to the user. For example, the final payment amount of 800,000 yen and its breakdown (medical expenses 400,000 yen, repair expenses 240,000 yen, and compensation 160,000 yen) is displayed.

[1316] Step 8:

[1317] User: Check the displayed results and, if necessary, correct the accident information again or proceed with settlement negotiations.

[1318] Example 1

[1319] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1320] When a traffic accident occurs, it is important to calculate compensation for damages quickly and accurately. However, conventional methods require manual input, calculations, and analysis of legal precedents, which is time-consuming and prone to errors. To solve these problems, a system is needed that automates the process from inputting accident information to calculating compensation amounts, thereby improving accuracy.

[1321] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1322] In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a database of past case law using the standardized and preprocessed accident information, means for calculating the degree of fault based on the case law information, means for calculating the amount of compensation in accordance with a standard, means for presenting the calculated degree of fault and amount of compensation to the user, means for checking the input accident information and transmitting the data in an appropriate format, and means for using natural language processing technology to structure text data and convert it into a format for input to an AI model. This automates the process from inputting accident information to calculating the amount of compensation, making it possible to perform the process quickly and accurately.

[1323] "Accident information" refers to detailed data including the date and time of the traffic accident, the location of the accident, information about those involved (such as the names and contact information of the victim and perpetrator), the circumstances of the accident (such as a rear-end collision or a side collision), and the damages (such as damage to the vehicle and the need for medical treatment).

[1324] The "receiving means" refers to a hardware or software element that has the function of accurately acquiring input from a user or data from an external system.

[1325] "Standardization and preprocessing means" refers to processes that standardize the format of received data, fill in inconsistencies and missing values, and prepare the data for analysis.

[1326] "Case law information" refers to data on the results of past trials and settlements regarding similar traffic accidents, as well as the percentage of fault and amount of compensation calculated at those times.

[1327] "Natural language processing technology" is a technology that allows computers to understand and analyze the language that humans use on a daily basis, and is a technology that structures text data and performs semantic analysis.

[1328] "Fault ratio" is a number that indicates the percentage of responsibility that each party should bear for damages caused in a traffic accident.

[1329] "Compensation" refers to the amount that the at-fault party must pay for the damages suffered by the victim in a traffic accident, including medical expenses, repair costs, and compensation.

[1330] The "presentation means" refers to hardware or software elements that have the function of displaying or notifying the calculated results to users and other related parties in an easy-to-understand manner.

[1331] An "AI model" is a collection of algorithms that use machine learning and deep learning to analyze data and automatically perform tasks such as prediction and classification.

[1332] "Structuring technology" is a technology that analyzes unstructured data and converts it into a format that can be used in databases and analysis systems.

[1333] The present invention provides a system for automatically calculating compensation for damages after a traffic accident, quickly and accurately. Specific embodiments of this system are described below.

[1334] System configuration

[1335] This system consists of a user input terminal, a server, and a database.

[1336] User Input Device: The device used to enter details of a traffic accident, such as a computer, smartphone, or tablet.

[1337] Server: This is the core device that analyzes the received data and calculates the percentage of fault and the amount of compensation. It also performs data standardization, preprocessing, reading case law information, and various calculations.

[1338] Database: A system that stores past case data and damages standards. The case information database includes damages standards such as the "Red Book" and the "Blue Book."

[1339] System Operation

[1340] The system processes traffic accident data using the following hardware and software:

[1341] 1. When a traffic accident occurs, the user uses a smartphone or computer to input accident information, including the date and time of the accident, the location of the accident, information about the people involved, the accident situation, and details of the damage.

[1342] 2. The terminal checks the entered accident information, converts it into the appropriate format, and sends it to the server. For example, it standardizes the date and time format and address notation, and checks for and corrects any inconsistencies or missing entries.

[1343] 3. The server standardizes and preprocesses the received accident information. Standardization includes standardizing date and time formats and address notation, and preprocessing includes filling in missing values ​​and detecting and correcting input errors.

[1344] 4. The server reads the relevant case information from the case information database, analyzes and structures the text data using natural language processing technology, and then segments and tags the text data before storing it in the database.

[1345] 5. The server uses the standardized and preprocessed accident information and structured case law information to calculate the percentage of fault using an AI model. It references past case law data and calculates based on the percentage of fault in similar accidents.

[1346] 6. The server calculates the amount of compensation based on the percentage of fault and the damage data entered by the user. The damages include medical expenses, repair costs, compensation, etc. Based on these, the total amount of damages is calculated and the amount of compensation according to the percentage of fault is determined.

[1347] 7. The server organizes the calculated percentage of fault and compensation amount and converts it into a format that can be notified to the user. The results include a breakdown of the compensation amount (medical expenses, repair costs, compensation, etc.).

[1348] 8. The terminal displays the results received from the server to the user. For example, it may present the results in the form of "Final payment amount: 800,000 yen (medical expenses 400,000 yen, repair expenses 240,000 yen, compensation 160,000 yen)."

[1349] Specific examples

[1350] For example, consider a case involving a rear-end collision that occurred somewhere in Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator). When Person A's vehicle rear end was damaged and he suffered injuries requiring medical treatment, User A inputs the accident information. The input information is sent to the server, which processes the information. It reads relevant case law information and uses an AI model to determine the fault ratio as 80:20, calculating the amount of damages as 800,000 yen. The results are then presented to the user.

[1351] Prompt Sentence Examples

[1352] "Information on victim A and assailant B has been entered for a rear-end collision that occurred in a certain location in Tokyo on October 1, 2023. The damage involved damage to the rear of the vehicle and the need for medical treatment. Please calculate the percentage of fault and the amount of compensation based on past legal precedents."

[1353] This system makes it possible to quickly and accurately calculate compensation for damages caused by traffic accidents, significantly reducing the burden on users.

[1354] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1355] System processing flow

[1356] Step 1: Enter accident information

[1357] The user inputs details of the traffic accident using a smartphone or computer, including the date and time of the accident, the location of the accident, information about the parties involved (such as the names and contact information of the victim and at-fault parties), the circumstances of the accident (such as rear-end collision or side-impact collision), and details of damage (such as vehicle damage and medical needs).The system records the accident circumstances based on the input data and proceeds to the next step.

[1358] Step 2: Send data

[1359] The terminal checks the information entered by the user and converts it into the appropriate format. Specifically, it standardizes the date and time format and address notation. The converted data is then sent to the server. By converting the entered data into a format and sending it to the server, data consistency is maintained.

[1360] Step 3: Receiving and Preprocessing Data

[1361] The server receives the accident information sent from the terminal. After receiving it, it standardizes and preprocesses the data. Specifically, it standardizes the date and time format and address notation, fills in missing values, and detects and corrects input errors. This prepares the data in an analyzable format and allows it to proceed to the next step.

[1362] Step 4: Loading case law information

[1363] The server reads relevant case information from a case information database. For example, it extracts cases related to compensation for damages from databases such as the "Red Book" and "Blue Book." It then uses natural language processing technology to analyze and structure the text data, converting it into a format that can be input into the AI ​​model. This converts the case data into a format that can be used for analysis.

[1364] Step 5: Calculating the percentage of fault

[1365] The server uses the standardized and preprocessed accident information and structured case law information to calculate the percentage of fault using an AI model. Specifically, it calculates the percentage of fault corresponding to the input accident information based on past case law data. For example, it refers to past cases related to rear-end collisions to determine the percentage of fault between the assailant and the victim. It then proceeds to the next step based on the calculated percentage of fault.

[1366] Step 6: Calculating damages

[1367] The server calculates the amount of compensation based on the determined fault ratio and the damage data entered by the user. Specifically, it calculates the total amount of damages using data such as medical expenses, repair costs, and compensation, and then calculates the amount of compensation based on the fault ratio. For example, if the total amount of damages is 1 million yen and the fault ratio is 80:20, the amount of compensation that the perpetrator must pay is 800,000 yen. The process proceeds to the next step based on the calculated amount of compensation.

[1368] Step 7: Organize and communicate results

[1369] The server organizes the calculated percentage of fault and compensation amount and converts them into a format that can be notified to the user. Specifically, it formats the results, including a breakdown of the compensation amount (e.g., medical expenses, repair costs, compensation, etc.). The formatted data is sent to proceed to the next step.

[1370] Step 8: Viewing the results

[1371] The terminal displays the results received from the server to the user. Specifically, it presents the results in the form of "Final payment amount: 800,000 yen (medical expenses 400,000 yen, repair expenses 240,000 yen, compensation 160,000 yen)." This allows the user to quickly and accurately check the amount of compensation.

[1372] (Application example 1)

[1373] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1374] Conventional methods for calculating compensation for traffic accidents require human intervention, which requires a significant amount of time and effort. Furthermore, there is a problem in that accident analysis and compensation calculation for autonomous vehicles cannot be performed in real time. This can lead to delays in settlement negotiations and in some cases to an inability to present accurate compensation amounts. Furthermore, the time required for rapid accident analysis and notification of results increases the mental and financial burden on both the victim and the at-fault party.

[1375] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1376] In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a past case database using the standardized and preprocessed accident information, means for calculating a fault ratio based on the case information, means for calculating a compensation amount according to a standard, means for presenting the calculated fault ratio and compensation amount to a user, means for recording sensor information from the autonomous vehicle and collecting accident data, and means for transmitting the accident data to the server via the vehicle's communication module. This makes it possible, when an autonomous vehicle is involved in a traffic accident, to quickly record the accident situation and quickly and accurately calculate a fault ratio and compensation amount.

[1377] "Accident information" is detailed data about traffic accidents, including the date and time of the accident, location, people involved, accident circumstances, and damage details.

[1378] The "receiving means" refers to a method and device for capturing the input accident information into the server.

[1379] "Standardization and preprocessing means" refers to methods and devices for standardizing received incident information into a consistent format and correcting missing or incorrect data.

[1380] The "Case Law Database" is a data store that compiles case law and compensation standards related to past traffic accidents.

[1381] The "means for reading and analyzing" refers to a method and apparatus for extracting and analyzing relevant case information from a case database based on the standardized and preprocessed accident information.

[1382] The "means for calculating the degree of fault" refers to a method and device for calculating the degree of fault of each party based on legal precedent information.

[1383] The "means for calculating the amount of compensation" refers to a method and device for calculating the amount of compensation for each party based on the determined degree of fault and damage data.

[1384] The "means for presenting to the user" refers to a method and device for displaying the calculated fault ratio and compensation amount to the user.

[1385] "Means for recording sensor information" refers to methods and apparatus for storing accident-related data obtained from sensors installed on an autonomous vehicle.

[1386] A "communication module" is a communication device used to transfer data from a vehicle to a server.

[1387] This invention is a system that quickly and accurately collects accident information when an autonomous vehicle is involved in a traffic accident, and automatically calculates the percentage of fault and the amount of compensation. Specific embodiments of this system are described below.

[1388] System Overview

[1389] This system consists of a user input terminal, a server, a database, various sensors in the autonomous vehicle, and a communication module. These components work together as follows:

[1390] 1. User Input Terminal

[1391] This is a device that allows users to input detailed information about traffic accidents. The device can be a smartphone, tablet, or vehicle display system.

[1392] 2. Server

[1393] The server is the main device responsible for receiving accident information, standardizing and preprocessing it, reading case law information, calculating the degree of fault and compensation amounts, and presenting the results. In particular, it uses natural language processing technologies (such as NLTK and spaCy) and AI models (such as TensorFlow and PyTorch) to standardize and preprocess the received accident information and extract relevant information from the case law database.

[1394] 3. Database

[1395] The database holds information on past legal precedents and damage compensation standards, from which the server extracts the necessary information and uses it for analytical processing.

[1396] 4. Sensors and communication modules for autonomous vehicles

[1397] Autonomous vehicles are equipped with front and rear cameras and various sensors that record information in real time when an accident occurs. This data is sent to a server via the vehicle's communication module, using 5G communication technology.

[1398] System operation flow

[1399] The operation of the system consists of the following major steps:

[1400] 1. Collecting accident information

[1401] Sensors in autonomous vehicles record accident information in real time, acquiring information such as the location and time of the accident, and information on those involved. This information is then sent to a server via the vehicle's communication module.

[1402] 2. Receiving and preprocessing accident information

[1403] The server standardizes the received accident information, filling in missing values ​​and checking for format consistency, for example, standardizing date and time formats and address notation.

[1404] 3. Extraction and structuring of case law information

[1405] The server reads the relevant case information from the case database and structures it using natural language processing technology, converting it into a format that can be used by the AI ​​model.

[1406] 4. Calculation of the percentage of fault and the amount of compensation

[1407] Standardized and preprocessed accident information and structured case law information are input into the AI ​​model to calculate the degree of fault and the amount of compensation.

[1408] 5. Presentation of results

[1409] The calculated percentage of fault and amount of compensation for damages are sent from the server to the user input terminal in a format that can be confirmed by the user, and are displayed.

[1410] Specific examples

[1411] For example, in the case of a rear-end collision that occurred in Tokyo on October 1, 2023, the victim was Person A and the assailant was Person B, and Person A's rear end was damaged, resulting in injuries requiring medical treatment. Accident information acquired by the autonomous vehicle is sent to a server in real time, where it is standardized and preprocessed. The server refers to a database of past legal precedents, extracts appropriate data, and inputs it into an AI model to calculate the percentage of fault between the victim and the assailant, and calculates the amount of compensation. The results are displayed on the autonomous vehicle's display system and on the user's smartphone.

[1412] Prompt Sentence Examples

[1413] "Please enter the details of a rear-end collision that occurred in Tokyo on October 1, 2023. The victim is Person A and the perpetrator is Person B. The rear of Person A's vehicle is damaged and requires medical treatment."

[1414] This system allows for quick and accurate calculation of compensation in traffic accidents, significantly reducing the effort required for settlement negotiations.

[1415] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1416] Step 1:

[1417] Sensors in autonomous vehicles record accident information in real time.

[1418] Input: Real-time data from sensors and cameras (location of the accident, speed, impact angle, etc.).

[1419] Output: Recorded accident data.

[1420] Specific operation: Various sensors and cameras installed in the autonomous vehicle detect the moment of an accident and store the data in internal memory. These sensors include acceleration sensors, gyro sensors, and video cameras.

[1421] Step 2:

[1422] The accident data is transmitted to a server via the vehicle's communication module.

[1423] Input: Accident data recorded in step 1.

[1424] Output: The incident data sent to the server.

[1425] How it works: Recorded accident data is sent to a server in real time via a 5G communication module installed in the vehicle, which requires high data transfer speeds and low latency.

[1426] Step 3:

[1427] The server normalizes and pre-processes the received accident data.

[1428] Input: Accident data sent to the server.

[1429] Output: Standardized and preprocessed accident data.

[1430] Specific operation: The server standardizes the date and time format and address notation of the received accident data, and performs missing data completion and correction of input errors to maintain consistency. This process is often performed using the Python library Pandas.

[1431] Step 4:

[1432] The server reads the relevant case information from the case database and structures it using natural language processing technology.

[1433] Input: Standardized and preprocessed accident data.

[1434] Output: Structured case law information.

[1435] Specific operation: The server searches and extracts relevant information from case law databases such as the "Red Book" and "Blue Book," and structures the text data using natural language processing technology (such as NLTK or spaCy).

[1436] Step 5:

[1437] The server inputs standardized accident data and structured case law information into an AI model to calculate the degree of fault.

[1438] Input: Standardized accident data and structured case law information.

[1439] Output: Calculated fault percentage.

[1440] Specific operation: Standardized accident data and structured case law information are input into an AI model (using TensorFlow and PyTorch), and the fault ratio of each party is calculated based on past case law data.

[1441] Step 6:

[1442] The server calculates the amount of compensation based on the determined fault percentage and damage data.

[1443] Input: Calculated fault percentage and damage data.

[1444] Output: Calculated damages.

[1445] Specific operation: The server calculates the total amount of damages based on the percentage of fault and the input damage data (medical expenses, repair costs, compensation, etc.), and then calculates the amount of compensation that the at-fault party must pay. Specifically, it multiplies and adds the amounts.

[1446] Step 7:

[1447] The server presents the calculated percentage of fault and the amount of compensation to the user.

[1448] Input: Calculated percentage of fault and damages.

[1449] Output: Percent fault and damages in a user-viewable format.

[1450] Specific operation: The server converts the calculation results into an easy-to-understand format and sends them to the user's input device (smartphone or vehicle display system), allowing both the victim and the perpetrator to check the results in real time.

[1451] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1452] The system of the present invention quickly and accurately determines compensation for damages in automobile accidents and further improves the user experience by recognizing the user's emotions. Specific embodiments of the present invention and the flow of program processing are described below.

[1453] System configuration

[1454] The system mainly consists of the following components:

[1455] 1. User input terminal: A device with an interface for entering details of a traffic accident. This can be a computer, smartphone, tablet, etc.

[1456] 2. Server: This is the core device that receives data sent by users and performs various processes, such as standardizing data, reading legal precedent information, and calculating the degree of fault and the amount of compensation.

[1457] 3. Database: A system that stores past case data and compensation standards.

[1458] 4. Emotion engine: A device or software that recognizes emotions based on user input and reactions and adjusts the system's response.

[1459] System Operation

[1460] Below we explain the process from inputting accident information to presenting the final compensation amount and optimizing it through emotion recognition.

[1461] User Input Phase

[1462] User: Enters detailed information about the traffic accident into the input terminal, such as the date and time of the accident, location, parties involved (victim, perpetrator), accident circumstances (rear-end collision, side collision, etc.), and specific details of the damage (vehicle damage, need for medical treatment, etc.).

[1463] Terminal: Checks the input and sends the data to the server based on the format.

[1464] Data reception and preprocessing phase

[1465] Server: Receives the accident information sent by the user.

[1466] Server: Checks whether the format and content of the received data are correct.

[1467] Server: Standardizes the received accident information and checks for consistency in format and content, for example, standardizing date and time formats and address notation.

[1468] Case law information loading phase

[1469] Server: Reads case information from the "Red Book" and "Blue Book."

[1470] Server: The read text data is structured using natural language processing technology and converted into a format that can be used by the AI ​​model.

[1471] Fault calculation phase

[1472] Server: Structured case law information and standardized accident information are input into the AI ​​model.

[1473] Server: The AI ​​model refers to past case data and calculates the fault ratio based on the input accident information. For example, in the case of a rear-end collision, the fault ratio is usually 80:20.

[1474] Damages calculation phase

[1475] Server: Calculate the amount of compensation based on the percentage of fault. For example, if the victim's medical expenses are 500,000 yen, repair costs are 300,000 yen, and compensation is 200,000 yen, the total damages will be 1 million yen.

[1476] Server: Calculate the amount of compensation that the at-fault party must pay based on their percentage of fault. In the example above, the amount that must be paid is 800,000 yen, which is 80% of the total damages of 1 million yen, and is the at-fault party's percentage of the total damages.

[1477] Emotion Recognition Phase

[1478] Terminal: The emotion engine analyzes the user's reactions when inputting information, facial expressions, tone of voice, etc.

[1479] Emotion engine: The emotion engine recognizes emotions based on the user's input information and sends the emotional state to the server.

[1480] Server: Adjusts the content, expression, and presentation of responses based on the user's recognized emotions. For example, if the system recognizes that the user is feeling stressed or anxious, it will provide a more polite and reassuring response.

[1481] Results presentation phase

[1482] Server: Organizes the calculated percentage of fault and compensation amount and summarizes them in a format that is easy for users to understand.

[1483] Terminal: Displays the results received from the server to the user. For example, it displays the final payment amount of 800,000 yen and its breakdown (400,000 yen for medical expenses, 240,000 yen for repairs, and 160,000 yen for compensation). In addition, it can display a leaflet or a link to an FAQ page that reflects the user's emotions.

[1484] Specific examples

[1485] For example, consider a rear-end collision that occurred in Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator), resulting in damage to the rear of Person A's vehicle and injuries requiring medical treatment. User A enters accident information, and the device sends that information to the server. The server processes the received data, loads appropriate case law information, and runs it through an AI model. Person B is determined to be 80% at fault, and the amount of compensation to Person A is calculated to be 800,000 yen. This result is presented to the user by the emotion engine, taking into account the user's emotions.

[1486] In this way, users can quickly and accurately calculate the amount of compensation automatically, and the system takes the user's feelings into consideration when presenting optimal information, significantly reducing the effort and stress involved in settlement negotiations.

[1487] The processing flow will be explained below.

[1488] Step 1:

[1489] User: Enter detailed information about the traffic accident, such as the date and time of the accident (e.g., 14:30, October 1, 2023), the location of the accident (an intersection in Tokyo), the parties involved (victim A, assailant B), the accident circumstances (rear-end collision while waiting at a traffic light), and the specific details of the damage (damage to the rear of the vehicle, injuries requiring medical treatment).

[1490] Step 2:

[1491] Terminal: Converts the input information into the appropriate format and sends it to the server, including all the details of the accident.

[1492] Step 3:

[1493] Server: Receives the accident information sent by the user. Immediately after receiving the information, the server checks whether there are any errors in the data format or content. For example, it checks whether the date and time format is standardized to "YYYY-MM-DD HH:MM" and whether the address is written correctly.

[1494] Step 4:

[1495] Server: Standardizes the received accident information. Standardization includes standardizing date and time formats, address notation, and name notation. This ensures data consistency.

[1496] Step 5:

[1497] Server: Reads case law information from the "Red Book" and "Blue Book" databases. Structures the text data using natural language processing technology to analyze past case law data and compensation standards.

[1498] Step 6:

[1499] Server: Structured case law information and standardized accident information are input into the AI ​​model. The AI ​​model references past case law data and calculates the fault ratio based on the input accident information. For example, it automatically determines the fault ratio for a rear-end collision as 80:20.

[1500] Step 7:

[1501] Server: Calculates the amount of compensation based on the percentage of fault. Specifically, the damages such as medical expenses (e.g., 500,000 yen), repair costs (e.g., 300,000 yen), and compensation (e.g., 200,000 yen) are evaluated according to a standard, and the total amount of damages (e.g., 1 million yen) is calculated.

[1502] Server: Calculate the amount of compensation that the at-fault party must pay based on the percentage of fault. For example, if the total damages are 1 million yen, the amount that the at-fault party must pay is 800,000 yen, which is 80% of the total damages.

[1503] Step 8:

[1504] Device: The emotion engine analyzes the user's reactions, facial expressions, and tone of voice in real time when inputting information.

[1505] Emotion Engine: The emotion engine recognizes the user's emotional data (e.g., text typing speed, changes in tone of voice, subtle changes in facial expressions) and determines their emotional state.

[1506] Step 9:

[1507] Server: The emotion engine takes into account the emotional data it recognizes and adjusts the content and expression of the response. For example, if it senses that the user is feeling stressed, it will provide a more polite and reassuring response. If the user is feeling anxious, it will provide concise and prompt information.

[1508] Step 10:

[1509] Server: Converts the organized results of fault ratio and compensation amount into data to be presented to the user along with the emotion recognition results.

[1510] Step 11:

[1511] Terminal: Receives the final result data from the server and displays it to the user. The information presented to the user includes the final payment amount (e.g., 800,000 yen) and its breakdown (400,000 yen for medical expenses, 240,000 yen for repairs, and 160,000 yen for compensation), as well as recommendations for settlement negotiations. The display content and format are also adjusted according to the user's emotions, as recognized by the emotion engine. For example, if a user is feeling stressed, it will also display relief measures and links to specialist consultations.

[1512] Specific examples

[1513] For example, in the case of a rear-end collision that occurred at an intersection in Tokyo on October 1, 2023, involving Person A (victim) and Person B (perpetrator), the rear of Person A's vehicle was damaged and he suffered injuries requiring medical treatment. User A enters the details of the accident, and the device sends the information to the server. The server standardizes and preprocesses the data, reads case law information, and calculates the degree of fault and the amount of compensation using an AI model. At this time, the emotion engine recognizes Person A's emotions in real time, and the server responds accordingly. The amount of compensation ultimately presented to Person A is 800,000 yen, and advice that takes emotions into consideration is also provided along with a detailed breakdown of the amount.

[1514] This system allows users to quickly and accurately calculate compensation amounts automatically, and emotion recognition enables user-friendly information presentation, significantly reducing the effort required for settlement negotiations and reducing stress for users.

[1515] Example 2

[1516] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1517] When a traffic accident occurs, it is necessary to quickly and accurately determine compensation for damages, but it often takes time to refer to past case law information. Furthermore, if there is an error in the information entered by the user, the processing may be further delayed. Furthermore, a system that ignores the user's emotional state may impair the user experience and increase stress. It is necessary to solve these issues, realize fast and accurate calculation of compensation amounts, and improve the user experience.

[1518] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1519] In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a database of past cases using the standardized and preprocessed accident information, means for calculating a fault ratio based on the case information, means for calculating a compensation amount according to a standard, means for recognizing the user's emotional state and adjusting the response content, and means for presenting the calculated fault ratio and compensation amount to the user. This enables a quick and accurate calculation of compensation amount, and also enables the user experience to be improved by providing a response that takes the user's emotional state into consideration.

[1520] (Definitions of important words)

[1521] "Accident information" is detailed data related to a traffic accident, including the date and time of the accident, the location, the people involved, the accident situation, and specific details of the damage.

[1522] "Standardization" is the process of standardizing the format and content of received data to ensure consistency.

[1523] "Preprocessing" is the process of checking data for errors or omissions and converting it into an appropriate format.

[1524] The "case law database" is a database that stores case law information related to past traffic accidents.

[1525] "Analysis" is the process of analyzing data and extracting and utilizing useful information from it.

[1526] "Fault ratio" is a number that indicates the percentage of fault of each party involved in a traffic accident.

[1527] "Amount of damages" refers to the amount to be paid for damages caused by a traffic accident.

[1528] "Natural language processing technology" is a technology that analyzes text data and converts it into a format that machines can understand.

[1529] "Emotional state" refers to the user's mental and emotional state.

[1530] "Adjusting the response content" refers to the process of changing the system's response and display content according to the user's emotional state.

[1531] The above are definitions of important terms contained in the claims.

[1532] MODE FOR CARRYING OUT THE INVENTION

[1533] This system quickly and accurately determines compensation for damages in traffic accidents, and provides an optimal user experience while recognizing the user's emotions. This system consists of the following main components:

[1534] User Input Terminal

[1535] Terminal: A device with an interface for inputting detailed information about a traffic accident. Specifically, it includes a computer, smartphone, tablet, etc. The user uses this terminal to input accident information.

[1536] Example: A user may use a smartphone to input the date and time of an accident, its location, the people involved, the circumstances of the accident, and specific details of the damage.

[1537] server

[1538] Server: A core device that receives data sent by users and performs various processes. The server performs multiple processes such as data standardization, reading past case law information, and calculating the degree of fault and compensation amount.

[1539] Example: A server receives user input and standardizes date and time formats or address notation to ensure uniform formatting.

[1540] Database

[1541] Database: A system that stores past case data and damage compensation standards, allowing the server to load and analyze case information.

[1542] Example: The server reads case law information on rear-end collisions from the "Red Book," then uses natural language processing technology to structure the data and convert it into a format that can be used by an AI model.

[1543] Emotion Engine

[1544] Emotion engine: A device or software that recognizes emotions based on user input and reactions and adjusts the system's response.

[1545] Example: When a user types something, the camera and microphone on the smartphone are used to analyze emotions from facial expressions and tone of voice.

[1546] Specific procedures for determining damages

[1547] The server receives the input accident information, standardizes and preprocesses it, and then reads case law information from the database. It then uses this information to calculate the degree of fault and calculate the amount of compensation. It also recognizes the user's emotional state and adjusts the response accordingly.

[1548] Prompt Sentence Examples

[1549] When entering details of a traffic accident, specific prompts might include:

[1550] "Please enter details about a rear-end collision that occurred in Tokyo on October 1, 2023. For example, please enter the date and time of the accident, location, parties involved (victim, perpetrator), accident circumstances (rear-end collision, side collision, etc.), and specific details of the damage (vehicle damage, need for medical treatment, etc.)."

[1551] This system enables quick and accurate damage compensation determination and provides optimal responses that take the user's feelings into consideration, thereby reducing the burden on the user and providing a more comfortable user experience.

[1552] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1553] Step 1: User Input Phase

[1554] User: Enters detailed information about the traffic accident into the input terminal. Specifically, the user enters the date and time of the accident, the location, the parties involved (victim, perpetrator), the accident situation (rear-end collision, side collision, etc.), and the specific details of the damage. This is done using the input form displayed on the terminal.

[1555] Input: Accident details (date, time, location, people involved, situation, damage details)

[1556] Output: Initial data entered into the terminal

[1557] Specific actions: The user enters details into the smartphone interface.

[1558] Step 2: Data transmission phase

[1559] Terminal: Validates input and sends data to the server based on format. Format validation includes consistency of date and time formats and address notation.

[1560] Input: Initial data entered into the terminal

[1561] Output: Standardized data sent to the server

[1562] Specific operation: The smartphone sends the entered data to the server.

[1563] Step 3: Data reception and preprocessing phase

[1564] Server: Receives accident information sent by users, checks for errors in format and content, and then standardizes the received data to ensure consistency in format and content.

[1565] Input: Data sent to the server

[1566] Output: Standardized and preprocessed data

[1567] Specific operation: The server checks the format and content of the received data and standardizes it, for example, standardizing the date and time and address notation.

[1568] Step 4: Case law information loading phase

[1569] Server: Reads case information from the "Red Book" and "Blue Book" and structures it using natural language processing techniques, including converting it into a format that can be used by the AI ​​model.

[1570] Input: Standardized accident information

[1571] Output: Structured case law information

[1572] Specific operation: The server extracts text data from the database and analyzes and structures it using natural language processing.

[1573] Step 5: Calculation of fault

[1574] Server: Structured case law information and standardized accident information are input into the AI ​​model, and the fault ratio is calculated based on past case law data.

[1575] Input: Structured case law information, standardized accident information

[1576] Output: Calculated fault ratio

[1577] How it works: The server inputs data into the AI ​​model and calculates the fault ratio. For example, in the case of a rear-end collision, an 80:20 fault ratio is applied.

[1578] Step 6: Calculation of damages

[1579] Server: Calculates the amount of compensation based on the calculated percentage of fault and clearly indicates the breakdown (medical expenses, repair costs, compensation, etc.).

[1580] Input: Fault ratio, detailed damage information (medical expenses, repair costs, compensation, etc.)

[1581] Output: Calculated damages amount and breakdown

[1582] Specific operation: The server calculates the total damage amount and then applies the fault ratio to calculate the amount that the at-fault party must pay. For example, if the total damage amount is 1 million yen and the at-fault party is responsible for 80% of the damage, the amount of compensation will be 800,000 yen.

[1583] Step 7: Emotion Recognition Phase

[1584] Terminal: The emotion engine analyzes the user's reactions when inputting information, facial expressions, tone of voice, etc.

[1585] Input: User reaction data (facial expressions, tone of voice, etc.)

[1586] Output: Recognized emotional state of the user

[1587] Specific operation: The smartphone's camera and microphone capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[1588] Step 8: Presentation of results

[1589] Server: Organizes the calculated percentage of fault and compensation amount and sends them to the user in an easy-to-understand format.

[1590] Input: Calculated percentage of fault and amount of damages

[1591] Output: Organized result data

[1592] What it does: The server organizes the results and formats them appropriately for presentation to the user.

[1593] Terminal: Displays the results from the server to the user, providing any necessary information or links to additional resources (such as an FAQ page).

[1594] Input: Organized result data

[1595] Output: The results displayed to the user and links to related information

[1596] Specific operation: The smartphone displays the results and their breakdown, as well as a response based on the user's emotions (such as a leaflet or a link to an FAQ page).

[1597] The above are the specific processing steps and operations of this system. This process has the effect of improving the accuracy of input information, quickly calculating the percentage of fault, and taking user emotions into consideration.

[1598] (Application example 2)

[1599] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1600] When a car accident occurs, both the victim and the at-fault party want a quick and accurate judgment on compensation, but the process is complicated and often causes great stress for users. In addition, there is a lack of means to provide appropriate emotional support to the driver and passengers during the accident process, which can easily increase the psychological burden.

[1601] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input accident information, means for standardizing and preprocessing the received accident information, means for reading and analyzing relevant case information from a past case database using the standardized and preprocessed accident information, means for calculating a fault ratio based on the case information, means for calculating a compensation amount according to a standard, means for presenting the calculated fault ratio and compensation amount to the user, means for collecting accident data using an in-vehicle sensor, means for standardizing and analyzing the collected data in real time, and means for recognizing the user's emotions and adjusting the response. This enables a quick and accurate determination of compensation and reduces the psychological burden by providing support that takes the user's emotions into consideration.

[1602] The "means for receiving input accident information" refers to a device or system that receives information about accidents sent from a user or an autonomous vehicle.

[1603] "Means for standardizing and preprocessing received accident information" refers to devices or systems that organize received accident information into a unified format and process it into a form suitable for analysis.

[1604] "Means for reading and analyzing relevant case information from a database of past case law" refers to a device or system that searches stored past case law data, obtains case law information corresponding to the input accident information, and analyzes it.

[1605] The "means for calculating the degree of fault" is a device or system that calculates the degree of fault in an accident based on the acquired case law information.

[1606] A "means for calculating the amount of compensation according to a standard" is a device or system that calculates the amount of compensation to be paid by the victim and the perpetrator based on the calculated degree of fault.

[1607] The "means for presenting the calculated fault ratio and compensation amount to the user" refers to a device or system that presents the calculation results to the user visually or audibly in an easy-to-understand manner.

[1608] "Means for collecting accident data using in-vehicle sensors" refers to devices and systems that use various sensors installed in autonomous vehicles to collect situational data when an accident occurs.

[1609] "Means for standardizing and analyzing collected data in real time" refers to devices and systems that instantly standardize data obtained from sensors inside the vehicle and quickly perform analysis work.

[1610] The "means for recognizing the user's emotions and adjusting the response" refers to a device or system that recognizes the user's emotional state from their facial expressions and voice, and provides an appropriate response accordingly.

[1611] The system of the present invention is intended to be installed in an autonomous vehicle. When a traffic accident occurs, the system analyzes the details of the accident quickly and accurately, determines compensation for damages, and provides emotional support to the user.

[1612] System configuration

[1613] Hardware Configuration

[1614] 1. Sensor equipment: Vehicle sensors such as cameras, LiDAR, and GPS.

[1615] 2. User input terminals: displays and voice recognition systems inside autonomous vehicles.

[1616] 3. Server: A central processing unit that analyzes data.

[1617] 4. Database: External storage for storing past case data and damage compensation standards.

[1618] Software Configuration

[1619] 1. Data standardization and preprocessing module: Software that standardizes received accident data and prepares it in a form suitable for analysis.

[1620] 2. Natural Language Processing Module: NLP library for parsing and structuring text data.

[1621] 3. AI analysis module: A generative AI model that calculates the degree of fault and compensation amount based on accident data and case law data.

[1622] 4. Emotion recognition module: Software that analyzes the user's facial expressions and voice to recognize emotions.

[1623] Program processing explanation

[1624] When an accident occurs, the server processes data in the following manner.

[1625] Receiving and standardizing accident data

[1626] The server receives real-time accident data collected from autonomous vehicles through sensors. The data is then converted into a standardized format by the data standardization and preprocessing module, making it suitable for analysis. This standardization ensures consistency in date and time formats and address notation, improving analysis accuracy.

[1627] Analysis of case law information

[1628] The server inputs the standardized accident data into a natural language processing module, which then structures the data from past legal cases. The structured data is then analyzed by an AI analysis module, which calculates the percentage of fault and the amount of compensation. Specifically, the standard percentage of fault (80:20) in a rear-end collision, for example, is applied.

[1629] Emotion Recognition and Response

[1630] The user's emotional state is captured in real time using the in-car camera and microphone and analyzed by the emotion recognition module. The analyzed emotional data is sent to the server, and the system responds taking the user's psychological state into consideration. For example, if the user is feeling stressed or anxious, the system may respond by displaying a reassuring message on the display or playing relaxing music through the in-car speakers.

[1631] Presentation of results

[1632] The server then sends the analyzed fault percentage and compensation amount to the user's input terminal and presents it to the user in an easy-to-understand format, allowing the user to quickly and accurately determine the amount of compensation, which is useful for dealing with the situation after the accident.

[1633] Specific examples

[1634] For example, if a user has an accident and shows an anxious expression, the generative AI model will generate the following prompt sentence and present it to the user.

[1635] Example prompt sentence:

[1636] "An accident has occurred. Please remain calm. We will now analyze the situation and inform you of the details of compensation."

[1637] "I'll play some relaxing music so you don't have to worry."

[1638] In this way, the system is expected to improve the overall user experience by enabling faster and more accurate accident response in autonomous vehicles and providing support that takes into account the user's emotions.

[1639] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1640] Step 1:

[1641] The server receives accident data from the vehicle's sensor devices, including camera footage, LiDAR data, GPS location information, etc. It collects these input data in real time for further processing.

[1642] Step 2:

[1643] The server converts the received accident data into a unified format using the data standardization and preprocessing module. For example, it standardizes date and time formats and address notations, and corrects data inconsistencies. The standardized data is then formatted for analysis and moves on to the next step.

[1644] Step 3:

[1645] The server inputs the standardized accident data into a natural language processing module, reads relevant case information from a database of past cases, and structures it. Specifically, it analyzes the text data and extracts information on the accident situation and the degree of fault. This structured data is then passed to the AI ​​analysis module.

[1646] Step 4:

[1647] The server uses an AI analysis module to compare structured case data with accident data and calculate the fault ratio and compensation amount. For example, in the case of a rear-end collision, the fault ratio is calculated as 80:20. This calculation provides a basis for compensation amount.

[1648] Step 5:

[1649] The server sends the calculated percentage of fault and compensation amount to the user's input terminal for presentation to the user. The user can check this information through the in-car display or voice guidance system. The presented information includes a breakdown of the compensation amount and the legal precedents that serve as the basis.

[1650] Step 6:

[1651] The emotion recognition module uses the in-car camera and microphone to analyze the user's facial expressions and tone of voice to recognize their emotional state. For example, if the user has an anxious expression, the module analyzes the emotional data and sends it to the server.

[1652] Step 7:

[1653] The server adjusts the response and display method based on the user's emotional state. For example, if the server recognizes that the user is feeling stressed or anxious, it will display a reassuring message on the display and play relaxing music through the car speakers.

[1654] Step 8:

[1655] The user can check the final amount of compensation and the percentage of fault and decide on the next steps. Based on this information, it is expected that contacting insurance companies and legal procedures will proceed smoothly.

[1656] Through the above steps, the system of the present invention realizes quick and accurate accident analysis and damage compensation determination, and furthermore, it reduces the psychological burden by supporting the user's emotions.

[1657] 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.

[1658] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1659] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1660] 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.

[1661] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1662] 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.

[1663] 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).

[1664] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1665] 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."

[1666] 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 o...

Claims

1. means for receiving input accident information; means for standardizing and pre-processing the received incident information; A means for reading and analyzing relevant case information from a database of past cases using the standardized and preprocessed accident information; means for calculating a fault ratio based on the case law information; a means for calculating damages according to a standard; A system including a means for presenting the calculated fault ratio and damages amount to the user.

2. The system according to claim 1, further comprising means for structuring case law data using natural language processing technology in calculating the degree of fault and the amount of compensation.

3. 2. The system according to claim 1, further comprising means for receiving input of accident information from a user using an input form.

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